Case study-bp struggles to resolve sustainability disaster-Ethic paper Term

Case study-bp struggles to resolve sustainability disaster-Ethic - Term Paper Example This incidence has affected the company severely damaging its reputation, costing it heavy losses and declining its business and productivity. The Deepwater Horizon disaster has produced cleanup costs that may exceed $40 b. In addition, billions more ultimately will be spent as a consequence of criminal and civil penalties and lawsuits. It also has produced an inestimable loss of reputation and goodwill for BP, a huge company that is more than a century old. The company was also blamed for acting unethically and avoiding its corporate social responsibilities. The aim of this paper is to examine key questions concerned with corporate ethics. These questions include whether any corporations believe that they must give evidence of acting ethically, whether this insistence is just politically-correct window dressing in many instances, whether it represents genuine moral concern on the part of top managers at the companies which issue them, whether it is truly good business for a corporat ion to institute ethical practices or whether these practices simply purchase a tawdry respectability within the community for a company. The answers to these key questions will be discussed in this paper in the context of BP. The role and significance of ethical considerations in corporate management Contrary to the earlier business practices and beliefs the present day corporate management provides significant importance to ethics in business and recognizes the the role and significance of ethical considerations in corporate management. Traditionally, the ethical principles were used for defining business rules and regulations and a set of standards which corporations should follow but presently, there are considered as serious issues required for the success of the organization and the top level management consider them vital issues for the business as they have to be accountable to their stakeholders including their customers who are life and blood for them and the ethical issue s are part of their social responsibility (Cavanagh, Moberg and Velasquez, 1981). The ethical considerations are still a dilemma for many corporations and they find it difficult in deciding about ethically acceptable or unacceptable behavior or situation. In the absence of clear and concrete definition of business ethics the corporations need the services of ethical consultants to resolve ethical issues. In the context of the Deepwater Horizon oil spill, BP has been alleged for many serious ethical problems that have occurred after the incidence, but a close investigation of the issues suggests, given the facts as presented in this case, that the company has acted quite ethically and genuinely to all its stakeholders. The most critical ethical dilemma before the company was whether the company was fair and honest enough in handling the issues after the disaster. There is no limit to the expectations of the people and they always seek more than what they get. The company is spending substantially for the clean up projects for the damage caused by the Deepwater Hor

Columbia Gs Admission Essay Essay Example for Free

Columbia Gs Admission Essay Essay My name is Edgar Martinez. I was born in Cartagena de Indias (Colombia) 34 years ago. My childhood was pretty normal. My parents had enough to provide for my little brother and I. My father was a local councilman at the time my mom a homemaker. I always wanted to be a politician like my father I admired him as a matter of fact I still do. I graduated from High School in 1995 and soon after and got drafted to go the Colombian Army. I was sent to Santa Marta a small town located a 200 miles north from my hometown. There I encounter my first experience with the real word and left for good my childhood. In the middle of deployment my father a secretary of Energy for the national government was kidnaped . After that life was never going to be the same. We waited and waited and waited †¦.. and waited. And after a few weeks a platton of navy seals of some sort rescue my father and he was brought back to us alive and almost well. The trauma of being in captivity was devastating for him and the family . My parents desided to send me to the US to be in a safer place. Little did I know I would never return to live in the place I was born. I came to the US in 1997 directly from Cartagena to Salt Lake City.

Linguistic Automatic Generation Natural Language

Linguistic Automatic Generation Natural Language 1. Introduction 1.1. The Problem Statement This thesis deals with the problem of Automatic generation of a UML Model from Natural Language Software Requirement Specifications. This thesis describes the development of Auto Modeler an Automated Software Engineering tool that takes Natural Language Software System Requirement Specifications as Input, performs an automated OO analysis and tries to produce an UML Model (a partial one in its present state i.e. static Class diagrams only) as output. The basis for Auto Modeler is described in [2][3]. 1.2. Motivation We conducted a short survey of the Software Industry in Islamabad in order to determine what sorts of Automated Software Engineering Tools were required by the Software houses. The result of the Survey (see Appendix-I for the survey report) indicated that there is demand for such a tool as Auto Modeler. Since such tools i.e. [2][3] that have already been developed are either not available in the market or are very expensive, and thus out of the reach of most software houses. Therefore we decided to build our own tool that can be used by the software industry in order to enable them to be more productive and competitive. But at present Auto Modeler is not ready for commercial use. But it is hoped that future versions of Auto Modeler will be able to cater to the needs of the Software Houses. 1.3. Background 1.3.1. The need for Automated Software Engineering Tools: In this era of Information Technology great demands are placed on Software Systems and on all those that are involved in the SDLC. The developed software should not only be of high quality but it should also be developed in minimal amount of time. When it comes to Software quality, the software must be highly reliable and it should meet the customers needs and it should satisfy the customers expectations. Automated Software Engineering Tools can assist the Software Engineers and Software Developers in producing High Quality Software in minimal amount of time. 1.3.2. Requirements Engineering: Requirements engineering consists of the following tasks [6]:  · Requirements Elicitation  · Requirements Analysis  · Requirements Specification  · Requirements Validation / Verification  · Requirements Management Requirements engineering is recognized as a critical task, since many software failures originate from inconsistent, incomplete or simply incorrect System Requirements specifications. 1.3.3. Natural Language Requirement Specifications: Formal methods have been successfully used to express Requirements Specifications, but often the customer cannot understand them and therefore cannot validate them [4]. Natural Language is the only common medium understood by both the Customer and the Analyst [4]. So the System Requirements Specifications are often written in Natural Language. 1.3.4. Object Oriented Analysis: The System Analyst must manually process The Natural Language Requirements Specifications Document and perform an OO Analysis and produce the results in the form of an UML Model, which has become a Standard in the Software Industry. The manual process is laborious, time consuming and often prone to errors. Some specified requirements might be left out. If there are problems or errors in the original requirements specifications, they may not be discovered in the manual process. OOA applies the OO paradigm to models of proposed systems by defining classes, objects and the relationships between them. Classes are the most important building block of an OO system and from these we instantiate objects. Once an individual object is created it inherits the same operations, relationships, semantics, and attributes identified in the class. Attributes of classes, and hence objects, hold values of properties. Operations, also called methods, describe what can be done to an object/class.[1] A relationship between classes/objects can show various attributes such as aggregation, composition, generalization and dependency. Attributes and operations represent the semantics of the class, while relationships represent the semantics of the model [1]. The KRB seven-step method, introduced by Kapur, Ravindra and Brown, proposes how to find classes and objects manually [1]. Hence, Identify candidate classes (nouns in NL). Define classes (look for instantiations of classes). Establishing associations (capturing verbs to create association for each pair of classes in 1 and 2). Expanding many-to-many associations. Identify class attributes. Normalize attributes so that they are associated with the class of objects that they truly describe. Identify class operations. From this process we can see that one goal of OOA is to identify NL concepts that can be transformed into OO concepts; which can then be used to form system models in particular notations. Here we shall concentrate on UML [1]. 1.3.5. Natural Language Processing (NLP): If an automatic analysis of the NL Requirements Document is carried out then it is not only possible to quickly find errors in the Specifications but with the right methods we can quickly generate a UML model from the Requirements. Although, Natural language is inherently ambiguous, imprecise and incomplete; often a natural language document is redundant, and several classes of terminological problems (e.g., jargon or specialist terms) can arise to make communication difficult [2] and it has been proven that Natural Language processing with holistic objectives is a very complex task, it is possible to extract sufficient meaning from NL sentences to produce reliable models. Complexities of language range from simple synonyms and antonyms to such complex issues as idioms, anaphoric relations or metaphors. Efforts in this particular area have had some success in generating static object models using some complex NL requirement sentences. 1.3.5.1. Linguistic analysis: Linguistic analysis studies NL text from different linguistic levels, i.e. words, sentence and meaning.[1] (i) Word-tagging analyses how a word is used in a sentence. In particular, words can be changeable from one sentence to another depending on context (e.g. light can be used as noun, verb, adjective and adverb; and while can be used as preposition, conjunction, verb and noun). Tagging techniques are used to specify word-form for each single word in a sentence, and each word is tagged as a Part Of Speech (POS), e.g. a NN1 tag would denote a singular noun, while VBB would signify the base form of a verb.[1] (ii) Syntactic analysis applies phrase marker, or labeled bracketing, techniques to segment NL as phrases, clauses and sentences, so that the NL is delineated by syntactical/grammatical annotations. Hence we can shows how words are grouped and connected to each other in a sentence.[1] (iii) Semantic analysis is the study of the meaning. It uses discourse annotation techniques to analyze open-class or content words and closed-class words (i.e. prepositions, conjunctions, pronouns). The POS tags and syntactic elements mentioned previously can be linked in the NL text to create relationships. Applying these linguistic analysis techniques, NLP tools can carry out morphological processing, syntactic processing and semantic processing. The processing of NL text can be supported by Semantic Network (SN) and corpora that provide a knowledge base for text analysis. The difficulty of OOA is not just due to the ambiguity and complexity of NL itself, but also the gap in meaning between the NL concepts and OO concepts.[1] 1.3.6. From NLP to UML Model Creation. After NLP the sentences are simplified in order to make identification of UML model elements form NL elements easy. Simple Heurists are used to Identify UML Model elements from Natural Text: (see Chapter 7) * Nouns indicate a class * Verb indicates an operation * Possessive relationships and Verbs like to have, identify, denote indicate attributes * Determiners are used to identify the multiplicity of roles in associations. 1.5. Plan of the thesis In Chapter 2 we present a brief survey of previous work and work similar to our work. Chapters 3, 4, 5, 6 and 7 describe the theoretical basis for Auto Modeler. Chapter 8 Describes the Architecture of Auto Modeler. In Chapter 9 we describe Auto Modeler in action with a case study. In Chapter 10 we present conclusions. 2. Literature Survey The first relevant published technique attempting to produce a systematic procedure to produce design models from NL requirements was Abbot. Abbott (1983) proposes a linguistic based method for analyzing software requirements, expressed in English, to derive basic data types and operations. [1] This approach was further developed by Booch (1986). Booch describes an Object-Oriented Design method where nouns in the problem description suggest objects and classes of objects, and verbs suggest operations.[1] Saeki et al. (1987) describe a process of incrementally constructing software modules from object-oriented specifications obtained from informal natural language requirements. Their system analyses the informal requirements one sentence at a time. Nouns and verbs are automatically extracted from the informal requirements but the system cannot determine which words are relevant for the construction of the formal specification. Hence an important role is played by the human analyst who reviews and refines the system results manually after each sentence is processed.[1] Dunn and Orlowska (1990) describe a natural language interpreter for the construction of NIAM (Nijssens, or Natural-language, Information Analysis Method ) conceptual schemas. The construction of conceptual schemas involves allocating surface objects to entity types (semantic classes) and the identification of elementary fact types. The system accepts declarative sentences only and uses grammar rules and a dictionary for type allocation and the identification of elementary fact types.[1] Meziane (1994) implemented a system for the identification of VDM data types and simple operations from natural language software requirements. The system first generates an Entity-Relationship Model (ERM) from the input text and then generates VDM data types from the ERM.[1] Mich and Garigliano (1994) and Mich (1996) describe an NL-based prototype system, NL-OOPS, that is aimed at the generation of object-oriented analysis models from natural language specifications. This system demonstrated how a large scale NLP system called LOLITA can be used to support the OO analysis stage.[1] V. Ambriola and V. Gervasi.[4] have developed CIRCE an environment for the analysis of natural language requirements. It is based on the concept of successive transformations that are applied to the requirements, in order to obtain concrete (i.e., rendered) views of models extracted from the requirements. CIRCE uses, CICO a domain-based, fuzzy matching, parser which parses the requirements document and converts it into an abstract parse tree. This parse tree is encoded as tuples and stored in a shared repository by CICO. A group of related tuples constitutes a T-Model. CIRCE uses internal tools to refine the encoded tuples called extensional knowledge and the knowledge about the basic behavior of software systems called intentional knowledge derived from modelers to further enrich the Tuple space. When a specific concrete view on the requirements is desired, a projector is called to build an abstract view of the data from the tuple space. A translator then converts the abstract view to a concrete view. In [5] V. Ambriola and V. Gervasi describe their experience of automatic synthesis of UML diagrams from Natural Language Requirement Specifications using their CIRCE environment. Delisle et al., in their project DIPETT-HAIKU, capture candidate objects, linguistically differentiating between Subjects (S) and Objects (O), and processes, Verbs (V), using the syntactic S-V-O sentence structure. This work also suggests that candidate attributes can be found in the noun modifier in compound nouns, e.g. reserved is the value of an attribute of â€Å"reserved book†.[1] Harmain and Gaizauskas developed a NLP based CASE tool, CM-Builder [2][3], which, automatically constructs an initial class model from NL text. It captures candidate classes, rather than candidate objects. Bà ¶rstler constructs an object model automatically based on pre-specified key words in a use case description. The verbs in the key words are transformed to behaviors and nouns are transformed to objects.[1] Overmyer and Rambow developed NLP system to construct UML class diagrams from NL descriptions. Both these efforts require user interaction to identify OO concepts.[1] The prototype tool developed by Perez-Gonzalez and Kalita supports automatic OO modeling from NL problem descriptions into UML notations, and produces both static and dynamic views. The underlying methodology includes theta roles and semi-natural language.[1] 3. Software Requirements Engineering Software requirements engineering is the science and discipline concerned with establishing and documenting software requirements [6]. It consists of: * Software requirements elicitation:- The process through which the customers (buyers and/or users) and the developer (contractor) of a software system discover, review, articulate, and understand the users needs and the constraints on the software and the development activity. * Software requirements analysis:- The process of analyzing the customers and users needs to arrive at a definition of software requirements. * Software requirements specification:- The development of a document that clearly and precisely records each of the requirements of the software system. * Software requirements verification:- The process of ensuring that the software requirements specification is in compliance with the system requirements, conforms to document standards of the requirements phase, and is an adequate basis for the architectural (preliminary) design phase. * Software requirements management:- The planning and controlling of the requirements elicitation, specification, analysis, and verification activities. In turn, system requirements engineering is the science and discipline concerned with analyzing and documenting system requirements. It involves transforming an operational need into a system description, system performance parameters, and a system configuration This is accomplished through the use of an iterative process of analysis, design, trade-off studies, and prototyping. Software requirements engineering has a similar definition as the science and discipline concerned with analyzing and documenting software requirements. It involves partitioning system requirements into major subsystems and tasks, then allocating those subsystems or tasks to software. It also transforms allocated system requirements into a description of software requirements and performance parameters through the use of an iterative process of analysis, design, trade-off studies, and prototyping. A system can be considered a collection of hardware, software, data, people, facilities, and procedures organized to accomplish some common objectives. In software engineering, a system is a set of software programs that provide the cohesiveness and control of data that enables the system to solve the problem.[6] The major difference between system requirements engineering and software requirements engineering is that the origin of system requirements lies in user needs while the origin of software requirements lies in the system requirements and/or specifications. Therefore, the system requirements engineer works with users and customers, eliciting their needs, schedules, and available resources, and must produce documents understandable by them as well as by management, software requirements engineers, and other system requirements engineers. The software requirements engineer works with the system requirements documents and engineers, translating system documentation into software requirements which must be understandable by management and software designers as well as by software and system requirements engineers. Accurate and timely communication must be ensured all along this chain if the software designers are to begin with a valid set of requirements. [6] 4. Automated Software Engineering Tools Software engineering is concerned with the analysis, design, implementation, testing, and maintenance of large software systems. Automated software engineering focuses on how to automate or partially automate these tasks to achieve significant improvements in quality and productivity. Automated software engineering applies computation to software engineering activities. The goal is to partially or fully automate these activities, thereby significantly increasing both quality and productivity. This includes the study of techniques for constructing, understanding, adapting and modeling both software artifacts and processes. Automatic and collaborative systems are both important areas of automated software engineering, as are computational models of human software engineering activities. Knowledge representations and artificial intelligence techniques applicable in this field are of particular interest, as are formal techniques that support or provide theoretical foundations.[7] Automated software engineering approaches have been applied in many areas of software engineering. These include requirements definition, specification, architecture, design and synthesis, implementation, modeling, testing and quality assurance, verification and validation, maintenance and evolution, configuration management, deployment, reengineering, reuse and visualization. Automated software engineering techniques have also been used in a wide range of domains and application areas including industrial software, embedded and real-time systems, aerospace, automotive and medical systems, Web-based systems and computer games.[7] Research into Automated Software Engineering includes the following areas: * Automated reasoning techniques * Component-based systems * Computer-supported cooperative work * Configuration management * Domain modeling and meta-modeling * Human-computer interaction * Knowledge acquisition and management * Maintenance and evolution * Model-based software development * Modeling language semantics * Ontologies and methodologies * Open systems development * Product line architectures * Program understanding * Program synthesis * Program transformation * Re-engineering * Requirements engineering * Specification languages * Software architecture and design * Software visualization * Testing, verification, and validation * Tutoring, help, and documentation systems 5. Natural Language Processing Natural language processing (NLP) is a subfield of artificial intelligence and linguistics. It studies the problems of automated generation and understanding of natural human languages. Natural language generation systems convert information from computer databases into normal-sounding human language, and natural language understanding systems convert samples of human language into more formal representations that are easier for computer programs to manipulate. 5.1. Language Processing Language processing can be divided into two tasks:[11] * Processing written text, using lexical, syntactic, and semantic knowledge of the language as well as any required real world information.[11] * Processing spoken language, using all the information needed above, plus additional knowledge about phonology as well as enough additional information to handle the further ambiguities that arise in speech.[11] 5.2. Uses for NLP: 5.2.1. User interfaces. Better than obscure command languages. It would be nice if you could just tell the computer what you want it to do. Of course we are talking about a textual interface not speech.[10] 5.2.2. Knowledge-Acquisition. Programs that could read books and manuals or the newspaper. So you dont have to explicitly encode all of the knowledge they need to solve problems or do whatever they do.[10] 5.2.3. Information Retrieval. Find articles about a given topic. Program has to be able somehow to determine whether the articles match a given query.[10] 5.2.4. Translation. It sure would be nice if machines could automatically translate from one language to another. This was one of the first tasks they tried applying computers to. It is very hard.[10] 5.3. Linguistic levels of Analysis Language obeys regularities and exhibits useful properties at a number of somewhat separable levels.[10] Think of language as transfer of information. It is much more than that. But that is a good place to start. Suppose that the speaker has some meaning that they wish to convey to some hearer.[10] Speech (or gesture) imposes a linearity on the signal. All you can play with is the properties of a sequence of tokens. Actually, why tokens? Well for one thing that makes it possible to learn.[10] So the other thing to play with is the order the tokens can occur. So somehow, a meaning gets encoded as a sequence of tokens, each of which has some set of distinguishable properties, and is then interpreted by figuring out what meaning corresponds to those tokens in that order.[10] Another way to think about it is that the properties of the tokens and their sequence somehow elicits an understanding of the meaning. Language is a set of resources to enable us to share meanings, but isnt best thought of as a means for *encoding* meanings. This is a sort of philosophical issue perhaps, but if this point of view is true, it makes much of the AI approach to NLP somewhat suspect, as it is really based on the encoded meanings view of language.[10] The lowest level is the actual properties of the signal stream: phonology speech sounds and how we make them morphology the structure of words syntax how the sequences are structured semantics meanings of the strings There are important interfaces among all of these levels. For example sometimes the meaning of sentences can determine how individual words are pronounced.[10] This many levels is obviously needed. But language turns out to be more clever than this. For example, language can be more efficient by not having to say the same thing twice, so we have pronouns and other ways of making use of what has already been said: A bear went into the woods. It found a tree. Also, since language is most often used among people who are in the same situation, it can make use of features of the situation: this/that you/me/they here/there now/then The mechanisms whereby features of the context, whether it is the context created by a sequence of sentences, or the actual context where the speaking happens is called pragmatics.[10] Another issue has to do with the fact that the simple model of language as information transfer is clealy not right. For one thing, we know there are at least the following three types of sentences: statements imperatives questions And each of them can be used to do a different kind of thing. The first *might* be called information transfer. But what about imperatives? What about questions? To some degree the analysis of such sentences can involve the ideas of a basic notion of meaning Speech acts.[10] There are other, higher-levels of structuring that language exhibits. For example there is conversational structure, where people know when they get to talk in a conversation, and what constitutes a valid contribution. There is narrative structure whereby stories are put together in ways that make sense and are interesting. There is expository structure which involves the way that informative texts (like encyclopedias) are arranged so as to usefully convey information. These issues blend off from linguistics into literature and library science, among other things.[10] Of course with hypertext and multi-media and virtual reality, these higher levels of structure are being explored in new ways.[10] 5.4. Steps in Natural Language Understanding The steps in the process of natural language understanding are:[11] 5.4.1. Morphological analysis Individual words are analyzed into their components, and non-word tokens (such as punctuation) are separated from the words. For example, in the phrase Bills house the proper noun Bill is separated from the possessive suffix s.[11] 5.4.2. Syntactic analysis. Linear sequences of words are transformed into structures that show how the words relate to one another. This parsing step converts the flat list of words of the sentence into a structure that defines the units represented by that list. Constraints imposed include word order (manager the key is an illegal constituent in the sentence I gave the manager the key); number agreement; case agreement.[11] 5.4.3. Semantic analysis. The structures created by the syntactic analyzer are assigned meanings. In most universes, the sentence Colorless green ideas sleep furiously [Chomsky, 1957] would be rejected as semantically anomalous. This step must map individual words into appropriate objects in the knowledge base, and must create the correct structures to correspond to the way the meanings of the individual words combine with each other. [11] 5.4.4. Discourse integration. The meaning of an individual sentence may depend on the sentences that precede it and may influence the sentences yet to come. The entities involved in the sentence must either have been introduced explicitly or they must be related to entities that were. The overall discourse must be coherent. [11] 5.4.5. Pragmatic analysis. The structure representing what was said is reinterpreted to determine what was actually meant. [11] 5.5. Syntactic Processing Syntactic parsing determines the structure of the sentence being analyzed. Syntactic analysis involves parsing the sentence to extract whatever information the word order contains. Syntactic parsing is computationally less expensive than semantic processing.[10] A grammar is a declarative representation that defines the syntactic facts of a language. The most common way to represent grammars is as a set of production rules, and the simplest structure for them to build is a parse tree which records the rules and how they are matched. [10] Sometimes backtracking is required (e.g., The horse raced past the barn fell), and sometimes multiple interpretations may exist for the beginning of a sentence (e.g., Have the students who missed the exam ). [10] Example: Syntactic processing interprets the difference between John hit Mary and Mary hit John. 5.6. Semantic Analysis After (or sometimes in conjunction with) syntactic processing, we must still produce a representation of the meaning of a sentence, based upon the meanings of the words in it. The following steps are usually taken to do this: [10] 5.6.1. Lexical processing. Look up the individual words in a dictionary. It may not be possible to choose a single correct meaning, since there may be more than one. The process of determining the correct meaning of individual words is called word sense disambiguation or lexical disambiguation. For example, Ill meet you at the diamond can be understood since at requires either a time or a location. This usually leads to preference semantics when it is not clear which definition we should prefer. [10] 5.6.2. Sentence-level processing. There are several approaches to sentence-level processing. These include semantic grammars, case grammars, and conceptual dependencies. [10] Example: Semantic processing determines the differences between such sentences as The ink is in the pen and The ink is in the pen. 5.6.3. Discourse and Pragmatic Processing. To understand most sentences, it is necessary to know the discourse and pragmatic context in which it was uttered. In general, for a program to participate intelligently in a dialog, it must be able to represent its own beliefs about the world, as well as the beliefs of others (and their beliefs about its beliefs, and so on).[10] The context of goals and plans can be used to aid understanding. Plan recognition has served as the basis for many understanding programs PAM is an early example. [10] 5.7. Issues in Syntax For various reasons, a lot of attention in computational linguistics has been paid to syntax. Partly this has to do with the fact that real linguistics have spent a lot of work on it. Partly because it needs to be done before just about anything else can be done. I wont talk much about morphology. We will assume that words can be associated with a set of features or properties. For example the word dog is a noun, it is singular, its meaning involves a kind of animal. The word dogs is related, obviously, but has the property of being plural. The word eat is a verb, it is in what we might call the base form, it denotes a particular kind of action. The word ate is related, it is in the past tense form. You can imagine Im sure that the techniques of knowledge representation that we have looked at can be applied to the problem of representing facts about the properties and relations among words. [11] The key observation in the theory of syntax is that the words in a sentence can be more or less naturally grouped into what are called phrases, and those phrases can often be treated as a unit. So in a sentence The dog chased the bear, the sequence the dog forms a natural unit. The sequence chased the bear is a natural unit, as is the bear.[11] Why do I say that the dog is a natural unit? Well one thing is that I can replace it by another sequence that has the same referent, or a related referent. For example I could replace it by: [11] Snoopy (a name) It (a pronoun) My brothers favorite pet (a more complex description) What about chased the bear? Again, I could replace it by died (a single word) was hit by a truck (a more complex event) This basic structure, in English, is sometimes called the subject-predicate structure. The subject is a nominal, something that can refer to an object or thing, the predicate is a verb phrase, which describes an action or event. Of course, as in the example, the verb phrase can also contain other constituents, for example another nominal. [11] These phrases also have structure. For example a noun phrase (a kind of nominal) can have a determiner, zero or more adjectives, and a noun, maybe followed by another phrase, like: the big dog that ate my homework Verb phrases can have complicated verb groups like will not be eaten Syntactic theories try to predict and explain what patterns are used in a language. Sometimes this involves figuring out what patterns just dont work. For example the following sentences have something wrong with them: [11] * the dogs runs home * he died the book * she saw himself in the mirror * they told it to she Figuring out exactly what is wrong with such sentences allows linguists to create theories that help understand the way that sentences

Developments in the West End Musical Scene

Developments in the West End Musical Scene DISCUSS THE RECENT DEVELOPMENTS IN THE WEST END MUSICAL SCENE; THIS SHOULD INCLUDE AN ANALYSIS OF THE MEGA-MUSICAL MANIA, THE TREND TO CREATE NEW MUSICALS BASED ON EXISTING SONGS (SONG MIGRATION) AND STAGE TRANSFERS OF SUCCESSFUL FILMS. From Sophocles through William Shakespeare to Eminem, writers have sought to use the rhythms of language to accentuate the story they are seeking to share. The pre-Caxton[1] society relied on an oral tradition to deliver stories of fact and fiction. Cultures spanning the entire globe and all ages of civilization have instinctively adopted musical storytelling; it is prominent in various forms even now – be it around a camp fire, at a tribal ceremony, an inner city playground or on a West End stage. In todays world, language and music are at our fingertips. They are both instantaneous. And they can be married in a second. Technically, music is intricate. Most writers will say the same about language. But in an inspired moment they can conjoin and express something wonderful both sonically and linguistically. The act of constructing such a moment can be the end result of many less fruitful moments – but there is always the chance that it could just happen instantaneously. Our logistical minds tell us that it just is not possible; that we would never be able to express ourselves beautifully and eloquently in musical form. And yet the compulsion to try and do so has arrested most people, even if only for a quickly aborted solitary moment. So perhaps here lies the fascination with musicals. They show life as we know it happily residing in an alternate reality – where music and language are easy bed partners and everything goes to extremes. Or does it? The West End is one of Londons most popular tourist attractions. It has built its reputation, in tandem with New Yorks Broadway, as the commercial mecca of musical theatre. Las Vegas has the showgirls but Broadway and the West End share the showtunes. Indeed, while their identities are undeniably distinct, the relationship between them is close; same sex twins rather than identical ones. Each has their own nuances of behaviour – the younger twin Broadway hunts that bit more keenly for the next off-beat musical whirlwind; the older West End plays percentages but plays them with palpable success. The term West End was originally coined as a geographical short cut – a way of describing a part of London synonymous with theatre. Since its inception into London vernacular the phrase West End has mutated to describe something meta-geographical. While once upon a time it merely represented an actual place, now it also describes the gateway to an invented world of glitz, glamour and show. The West End may still be the home of theatre, but the kind of theatre that it houses has become very easily classifiable. The listings do not lie. And neither do they try to. The West End is a haven for small ideas done big; big names, big shows, big spectacles, big budgets, big risks. The social and artistic significance of theatre as an art form has not suffered in the time since the West End theatres were constructed. But the immediacy of rival entertainments, chiefly television and film, has undoubtedly provided so comfortable an alternative for the borderline theatregoing public that its popularity has. Ultimately, the publics relationship with theatre has somewhat inverted itself; once the entertainment of the people, theatre has become high-brow, elitist, exclusive even. Or so we are led to believe. Every year the people entrusted with running the countrys theatres are ensconced in attempts to make theatre more accessible. Nicholas Hytner at the National Theatre has incorporated a sponsorship deal with Travelex with the express purpose of enabling its shows to be available to people for as little as  £10 a ticket. Theatrical output is continuing to diversify in new directions. The National Theatre still produces the time-honoured classics that will appease their traditional supporters. But they also invited outside companies including Theatre de Complicite, Improbable, Shunt and Kneehigh to co-develop their new work. Arts Council funding dictates a certain amount of programming for in-house producing theatres throughout the country. It is impossible to equate the artistic worth of a proposed project while it exists solely as an outline on a piece of paper. But it is easier to quantify the greater social import of the same project. Therefore the involvement in various local outreach initiatives including young peoples theatre and new writing programmes serves duplicate purposes. But in doing so it runs the risk of wrestling a certain amount of control from the artistic directors, or at least diluting the intent of their work. But the West End is not really concerned with any of this. The theatres are privately owned and have little social obligation. West End theatre is a notoriously unpredictable money market. Make a big success of yourself and you can eventually buy it up – which is exactly what Andrew Lloyd Webber and Cameron Mackintosh have ended up doing. Lloyd Webbers Really Useful Group are the proprietors of twelve of the capitals larger theatres. By January 2006 Delfont Mackintosh will control another seven, and will have begun constructing the Sondheim Theatre – the first theatre to be built on Shaftesbury Avenue since 1931. The long-term plan of Delfont Mackintosh is to refurbish and modernise theatreland. But one cannot help but think that their extreme makeover will be restricted to the facilities and layout – and that the entertainment will remain as traditional as ever. The musical-as-we-know-it grew out of the 19th Century tradition of music hall, which itself was the bastard son of drink and rowdiness. After removing the alcohol from drunken singalongs, and relocating from the pub to theatres, the 1860s saw the popularity of the newly-arrived music hall go from good idea to massively popular entertainment. The humbling beginnings of the musical cannot help but reveal the nucleus of the idea; it was born of accident – of people seeking to have pure, unadulterated entertainment. In that respect, it has no one form; no one philosophy; indeed no real sense of philosophy; no real sense of purpose other than fun, fun, fun! As the musical was developing it was the bastion of popular music of the time. Through Gilbert and Sullivan, Irving Berlin, Bertolt Brecht and Kurt Weill and Cole Porter, the men and women behind musical were the most revered song writers at work in the Western world. Ten years into the post-war era there was a marked shift. The musical standards that made dry, wry and witty observations about upper middle class were about to be trumped by rock and roll. And John Osbournes 1956 kitchen-sink-drama Look Back In Anger was going to have repercussions outside the world of the well-made play[2]. The birth of transmittable media was only going to swell the amount of music being produced. In the early days of the wireless radio, families gathered to listen to the songs of Ivor Novello or Noel Coward. By the mid-1960s many families had television sets in their front rooms; radio broadcasts were a competitive business; and air transport links had made the world traversable for all those who could afford it. Music was a commodity that could be sent from one side of the world to the other. And in the slipstream of the music were the musicians themselves. Through television and radio, songwriters and musicians had an identity. They became icons – the most celebrated people on the planet. And their music was nowhere near the West End stage. For the first time since their inception musicals were not using the popular music of the time. Rocknroll was being held in musical purgatory by traditionalists unhappy at its low-brow ideals. While cinema was running as fast and far as it could with the concept of the film musical, the stage was seeking to deliver variations on earlier themes. Elvis Presley made numerous musical films – as did The Beatles. In the 1960s the West End was awash with Broadway imports – the influence of Leonard Bernstein, Rodgers and Hammerstein, Lerner and Loewe and other transatlantic success stories was diluting the integrity of the West End as the older brother of the musical. But the psychedelic overtones of that time were to create musical anomalies; while some composers flirted with the conce pt of rock, others werent afraid to dive headlong into its bottomless pit. After its anti-Vietnam stance and inclusion of group nudity caused outrage on Broadway, Hair opened in the West End in 1968. From being the chosen playground of mild-mannered conservatives, the musical was being politicised – and modernised. Within five years, the Age of Aquarius[3] had been further capitalized upon by Godspell, Oh Calcutta and even Jesus Christ Superstar – which proved to be the foundation on which the new dawn of the musical would be built. Todays twin Godfathers of musical composition for Broadway and the West End carry the bright torch of yesteryear; Stephen Sondheim represents his forefathers fascination with the off-beat, with Andrew Lloyd Webber never straying from the musically conservative beat. There are various factors that dictate the recent successes and failures in West End theatre. But the starting point for every West End production is money – a fact beautifully demonstrated by the plot of one of the West Ends most popular current productions The Producers. Essentially, the capitalist dawn that swallowed up free love has made currency the new leading man in musical theatre. Producers need big ideas and big songs to legislate for big budgets. So instead of trying to predict what people may like and creating a musical story around it, the West End decided to reduce the risk and simply take the music that people already like and create a story around that. In some ways the origin of song migration is old revue style shows – popular hits belted out with no real desire to create an accompanying piece of drama or comedy. Coupled with the screen to stage success of musical films like The Lion King, a producer was now able to weigh up potential West End shows safe in the knowledge that a stable of worldwide smash hits could enable a musical to run for years, even with a bad review. Suddenly the sheer bankability of Lloyd Webber was looking like an outlandish risk alongside the music of Abba[4], Queen[5] or even (the critically lauded but never supergroup status) of Madness[6]. Negotiations are in process for the trend to continue, with Bob Marley, The Beatles and Elton John just some of the musical legends in line to have their songs shoe-horned into some money-spinning stage extravaganza that makes almost no sense at all. Not that the public really care. They want to go and sing-a-long like the pub dwellers of the 1840s that unknowi ngly helped begin the process of musical theatre. And who shall we choose to lead the sing-song? Well, preferably someone famous off the telly, of course. The West End is a remarkably lucrative place. For his unscheduled stint in the opening cast run of The Producers at the end of 2004, Nathan Lane was being paid  £42,000 a week for the lead role as Max Bialystock. It is a clear indication of the simple transaction between moneymen and talent; the star name guarantees the box office receipts. The West End has been flooded with stars – some of whom have no musical pedigree – because celebrity is deemed to have finally overridden talent. The good, bad and ugly (in no particular order) of recent years include David Hasselhoff[7], Martine McCutcheon[8] and Denise Van Outen[9]. And if you dont want to spend money on star names, then youd better be sure to have some seriously impressive stage gimmicks; Miss Saigon famously had a helicopter, Chitty Chitty Bang Bang got in to hot water when the eponymous flying car failed to take off in previews, and Phantom of the Opera has a plunging chandelier moment that will wake up anyone snoozing in the stalls. So with standard tickets averaging out at around  £40, the theatregoer demands a truly amazing experience. But amazing and original are poles apart – and thats why when the formula is right, all you need to do is repeat it. There are exceptions. The Bombitty of Errors was a rap interpretation of Shakespeares Comedy of Errors, and was a small but perfectly-formed global success. Stomp became a phenomenon through gradual word of mouth and because it is a different kind of spectacle. Jerry Springer: The Opera began life as an idea at a scratch night at the Battersea Arts Centre and grabbed the attention of every newspaper and fundamentalist Christian in the Western World. But such shows grow from humble beginnings and are swept away on public curiosity. As in any art form, there are people willing to take risks because they believe their work has a market. Bombay Dreams and The Far Pavilions identify a recently developed appreciation of Asian music and culture. The off-Broadway hit Batboy continues in the tradition of earlier pacesetters The Rocky Horror Picture Show and Hedwig and the Angry Inch for kitsch rock operas. But some of these are accidental intruders in the world of the West End. They werent sure if they were really invited but came anyway. One group that certainly were invited are blockbuster films; whether they have songs in them or not. Seemingly the films dont even have to have been that successful. The Witches of Eastwick had a successful run in the West End. But more than likely, the film will have a readymade audience. The Full Monty was relocated to middle America from Sheffield to make it a Broadway success. Billy Elliott is well into previews, but the advance word is that it will be a significant hit. Or better still, just take a film with songs already in them – you dont stand to make as much money, but the guarantee of an audience is that much stronger. Mary Poppins has been well-received by most, and Chitty Chitty Bang Bang is in its third year. There are currently 36 theatres in the West End of Londons theatreland[10]. As of Monday 2 May 2005, 27 are currently housing a production. 17 of those are musicals. This ratio is fairly consistent – and shows no signs of relenting. Essentially a hit West End musical needs a hook; star name, hit songs, hit movie, famous composer, popular revival. Something that can be reduced to a two-word phrase. If you havent got any of those, then heaven help you. Because the West End public certainly wont. BIBLIOGRAPHY In-yer-face Theatre: British Drama TodayAleks Sierz Faber Faber2001 Scene Unseen: Londons West End TheatresBarson, Kendall, Longman, SmithEnglish Heritage Publications 2003 Brewers Theatre: Phrase Fable DictionaryCassell Market House Books1994 MusicalsKurt Ganzl Carlton Books2004 Time Out (London) Issue No. 1810 April 27-May 4 2005 Musicals 101.com 1 Footnotes [1] William Caxton, inventor of the printing press (1474), which enabled literature to be mass-produced and readily available to the public. [2] A term coined by Terrence Rattigan for the four-act structure of a play that had been the norm for successful playwrights in the first half of the Twentieth Century. [3] A term synonymous with late 60s flower power and liberal ideologies – derived from a song from the musical Hair. [4] Mamma Mia. [5] We Will Rock You. [6] Our House. [7] Chicago, Adelphi Theatre, 2004 [8] My Fair Lady, Theatre Royal Drury Lane, 2001 [9] Chicago, Adelphi, 2001, Tell Me On A Sunday, Gielgud, 2003 [10] They are the Adelphi, Albery, Aldwych, Apollo, Arts, Cambridge, Comedy, Criterion, Dominion, Theatre Royal Drury Lane, Duchess, Duke of Yorks, Fortune, Garrick, Gielgud, Haymarket, Her Majestys, London Palladium, Lyceum, Lyric, New Ambassadors, New London, Palace, Phoenix, Piccadilly, Playhouse, Prince Edward, Prince of Wales, Queens, Savoy, Shaftesbury, St Martins, Strand, Trafalgar Studios, Vaudeville, Wyndhams. Other central theatres not classified as West End include the Donmar Warehouse, National Theatre, Old Vic, Victoria Palace, Apollo Victoria, Lilian Baylis Saddlers Wells, Peacock, Almeida and the Royal Court.

Who is the monster - Frankenstein or his creation? Essay -- English Li

Who is the monster - Frankenstein or his creation? "Frankenstein" written by Mary Shelly, was first published in 1818. The book has sold millions of copies and has become deeply rooted in today's society by films and other versions of the books. The main bulk of the story is about Victor Frankenstein's horrendous adventure. However there are sub-plots throughout the story, Robert Walton's journey to the North Pole, and the time the monster spends with the DeLacy family. The definition of a monster is, "something of unusual size or appearance, a huge terrifying creature or an evil person." [Chambers School Dictionary] All of these definitions can be related to both Frankenstein and the creation. To the creation, a huge terrifying creature and of unusual size and appearance, and to Frankenstein an evil person, powered by his hatred of the monster. Mary Shelly's life was very similar to the story of "Frankenstein". Her child, also called William, died at a young age. Her mother died at childbirth and possibly gave the sense of abandonment felt by the creation, "My person was hideous and my stature gigantic. What did this mean? Who was I? What was I? Whence did I come? What was my destination? These questions continually recurred, but I was unable to solve them." This showed that the monster was confused about himself, yet had nobody to ask and to confide in. Much like Mary Shelly. When Mary was writing Frankenstein she was reading a book called Emile by John Jacques Rousseau. In this text, Rousseau argued that men are by nature harmless, (i.e. children are naturally innocent) but are made cruel by society, thought the things they have done to them. Rousseau's quote was, "Men become monsters because... ...e a second monster, the bride for the creation. However half way through Frankenstein destroyed the corpse and thus angered the monster further, when Frankenstein could have had the monster out of his life for all eternity. This is also a good example of Frankenstein's selfishness as it shows that he was only thinking about himself as he believes that the creation well use this second monster to reek havoc upon the world, "Three years before, I was engaged in the same manner and created a fiend whose unparalleled barbarity [Once again we see that he is being biased towards the monster as he never tried to reason with the monster and any time.] had desolated my heart and filled it forever with the bitterest remorse. I was now to form another being of whose dispositions I was alike ignorant; she might become ten thousand times more malignant that her mate".

Comparing the Reaction of Franklin D. Roosevelt and Herbert Hoover to t

Comparing the Reaction of Franklin D. Roosevelt and Herbert Hoover to the Great Depression The year was 1929. America goes through the biggest national crisis since the American Civil War. They called it the Great Depression. The Stock Market was going down, unemployment was going up, and money was becoming scarce. The United States had to look up to the one person who could lead the country out of this national catastrophe, The President. At this time the man who had that title was none other than Herbert Hoover. Hoover, A republican, hoped that this was all a nightmare, he hoped that the Depression was a small fluke that would fix itself after a short period of time. After seeing that the Depression was getting worse had to use federal relief efforts. At the end of his term a democrat, Franklin Roosevelt, took his place and tried to fulfill his campaign promises by getting the country out of the Depression. At first Hoover opposed any relief efforts, but as the Depression worsened, he started a few farm assistance programs. Hoover hoped that theses farm programs would help the farmers’ situation with the low crop prices. Unfortunately farmers had to come dependent on this government handout. Hoover also started federal work projects such as the Grand Coulee Dam and the Hoover Dam. These projects provided many jobs for people and provided affordable hydroelectric power for people but the Great Depression was a much bigger problem than a few extra job openings could fix. Hoping that raising tariffs could help American business Hoover created the Hawley-Smoot Tarrif. This actually worsened economy and caused lower export rates. One of Hoover’s big mistakes was that he wouldn’t go off the gold standard. Hoov... ...y dismiss the program and start a different program in it place. Hoover on the other hand wanted to wait and think. He wanted to make sure that the programs that he provided money for wouldn’t be a waste and would definitely work. Hoover didn’t want to spend anymore money than he had to. Hoover really didn’t want to raise the national debt no matter what. Roosevelt did whatever it took no matter what the cost. Money was no object to Hoover, as long as he thought that program or agency could have a chance of getting them through the Depression he took a gamble on it and raised the national debt. Hoover made sure that there was enough hard money to back up the paper money in America while Roosevelt played it risky and printed extra money. Hoover and Roosevelt’s policies were extremely dissimilar and they each viewed relief from the Depression very differently.

To Kill a Mockingbird by Harper Lee Essay example -- English Literatur

To Kill a Mockingbird Test Question 1 As readers, we saw Scout mature and grow as our narorater and as a person. She learned many things, but also lost many things. As she grew up and changed, she began to see how things really were, and gained the knowledge of the pure hate that one man can show another. Scout lost her innocence when she found this out. She began to see how cruel the world can be to someone who is a little different or strange. She saw this in the prejudice that was shown to Tom Robinson, Walter Cunningham, Miss Maudie and even herself. She gained the wisdom of the world outside her back door and began to see how society works. Although this may not have been a good thing, she could now see how unimportant it was. Who cared if one was not up high on the social ladder? Atticus taught Jem and Scout to be polite, caring kids. He instilled in them a great sense of love for their neighbor and told them things that would help them get on in life. Scout was very lucky to have someone to guide her along the way. Although she was faced with â€Å"the real world†, she had lots of people who would willingly explain to her and guide her. Scout really matured during the course of this book. She went from a six year old child with no knowledge of the real world to an about ten year old who had a lot of life’s most important lessons shown to her at a very young age. She had to learn, very quickly, that life would not always be easy and fun. She learned of the horrible ways men can treat other men and of the ugliness of station, poverty and hate. To say that Scout would of never found out about these is incorrect. She would of found out about them within due course, but to find out at nine or ten must have been a culture shock for her! Scout was a very brave little girl to accept, reflect, and reject this ugly knowledge that she gained. If she had just been able to keep the innocence of childhood that she lost, she would have been much better off. We cannot let children face the ugliness that Scout learned of. It was too much, to fast, even for someone so smart. We must show love to everyone so that our children will show love. To Kill a Mockingbird Test Question 2 The author, Harper Lee, picked an interesting person to narrate the story. This had some advantages and disadvantages as the story progressed. This writing te... ...o the story. Without him, the story might have been about Atticus prosecuting Tom instead of defending him. To Kill a Mockingbird Test Question 8 Harper Lee once called this book ‘a simple love story’. Critics today call it an icon of American literature. Why can we not call it both? The book is a love story between the children and Boo Radley. But critics say it was so mush more than that! It truly was. Harper Lee used so many literary tecniques, characterizations, relationship developments and so many other things even the greatest literary master probably couldn’t tell you what this book means. But why can’t a book be both? A Wrinkle in Time is a great adventure book but it’s also a great Newberry award winner. So can Harper Lee’s simple love story between three children and a man not wanting to be pulled into society’s drowning currents be one of the greatest books ever written? A book that has so many hidden and double meanings be one of the most well respected and loved books in all of America? Can a book that touches on so many issues that are still alive today and that peaks to every one of us in a special way be on of the most sold and read books in the world? Yes.