Natural Language Processing Scope English Language Essay

Natural Language Processing Scope English Language EssayAbstractThe challenging sphere of graphic actors line impact has been a major concern in the field of ready reckoner science and artificial science since the late 40s. It encompasses the next strive forrard in artificial intelligence to make computers and human interface more flexible and human understandable. Various methods were adopted since its inscription like machine translation, saving recognition, e-teaching, auto tutor and so on Researchers saw it as a likely bridge between human spoken words and computers which utilize programming languages and binary codes. As mentioned earlier, it is still a challenging task of making a computer to understand human natural language as such. Hence, further enhancements and techniques will foster the demanding yet breeding and futuristic computational trends.Keywords human language technology Natural Language Processing, Semantic, Syntactic, lexical, Phonology, MT Machine T ranslationIntroductionThe computational scheme has evolved from basic right of instructions in the form of binary codes to mnemonic instruction codes to programming languages that have prevailed intensively during the later part of twentieth century. Along that evolution came the inspirational research on making the computer understand natural human language and interact with the humans in short applying natural language touch on to normal computer usage and beyond.Natural language bear upon can be defined as a theoretical approach enclosing analysis and manipulation of natural language texts usually spoken by humans. This is done at various levels of linguistic analysis in order to attain a human-like approach to processing of tasks and former(a) problems.It must be noted that natural language processing is not a single defined standard constitution but a collection of numerous language processing techniques and methods. Also, in view of facilitating the user and standing true to the name, texts must be of natural language usage and not a set of selected texts that could be used for processing. Because, the later approach would certainly forgo the real meaning of natural language processing.In any human language technology agreement, various levels of linguistic analysis of the text are performed. This is done because humans usually breakup linguistic texts into various levels and then process or understand the language. Human-like approach and processing in the NLP systems are considered as an integral part of AI. The applications of NLP are versatile and are currently being researched and implemented in field like military science, security systems, virtual reality simulation, medicine and regular computer science and artificial intelligence.The techniques and approaches that have been used or researched so furthermost form the basic platform of NLP. Some of them are based on classification of natural linguistic phonology, morphology, lexical variat ions, syntactic, semantic, pragmatic levels. Some of the notable hunts done in this field areMachine Translation Weaver and Booth (1946)Syntactic Structures Chomsky (1957)Case grammar FillmoreSemantic Networks QuillainConceptual Dependency SchankAugmented revolution Networks WoodsFunctional Grammar KayAlso that there have been famous prototypes developed to highlight the impact of particular techniques and principles. They areELIZA WeizenbaumSHRDLU WinogradPARRYLUNAR WoodsThe scene of the article revolves around the evolution of NLP and its implementation in security systems.MethodsStrata of natural language processingThe optimal descriptive way of putting forward the actions that are going on in natural language processing system is through the strata of natural language processing. During the early days of natural language processing, it was held that the different data of natural language processing followed a sequential pattern. But current Psycholinguistic resear ches have revealed that the system follows rather a coinciding pattern. This is because humans use all of the strata of language processing and they dont follow a sequential pattern. For this reason, in order to achieve high efficiency of NLP system more strata of language processing must be adopted.This stratum deals with the interpretation of speech sounds within and across words. Thereare three types of rules that are typically used1) Phonetic rules for sounds within words2) Phonemic rules for variations of pronunciation when words are spoken together3) Prosodic rules for fluctuation in stress and intonation across a sentence.MorphologyThis strata deal with the componential nature of words, which are composed of morphemes the smallest units of meaning. For example, the word postproduction can be morphologically analyzed into three separate morphemes the affix post, the root product and the suffix tion. Since the meaning of each morpheme remains the same across words, human s break down an unknown word into its constituent morphemes in order to understand its meaning. In the same way, an NLP system recognizes the meaning given by each morpheme in order to achieve and interpret meaning.LexicalBoth the humans and NLP systems at this stratum, interpret the meaning of individual words.Several types of processing contribute to word-level understanding the first of these being assignment of a single part-of-speech tag to each word. In this processing, words that can be given as more than one part-of-speech are assigned the most probable part-of speech tag based on the context in which they occur.Moreover at the lexical stratum, those words that have only one possible sense or meaning can be replaced by a semantic prototype of that meaning. The nature of the representation varies according to the semantic theory utilized in the NLP system. One can notice that, a single lexical unit is split into its more basic properties. If there is a set of semantic prim itives used across all words, these simplified lexical representations make it possible to unify meaning across words and to produce complex interpretations, more the same as humans do.SyntacticThe concept of analysing the sentence by looking into the grammatical composition of a sentence and its dependency is used here. This needs both grammar and a parser. The output achieved here is a representation of the sentence that gives the structural dependency relationships between the words. The efficiency of a parser depends on the different grammars used. Not all NLP applications require a full parse of sentences, therefore the remaining challenges in parsing of prepositional phrase attachment and conjunction scoping no longer stymie those applications for which phrasal and clausal dependencies are sufficient. Syntax conveys meaning in most languages because order and dependency contribute to meaning. For example the two sentences I smoked a queer. and The cigarette smoked me. differ only in terms of syntax, but convey contrasting meanings.SemanticThis is the strata at which most people think meaning is determined, however, as we cansee in the above defining of the stratum, it is all the levels that contribute to meaning.Semantic processing determines the possible meanings of a sentence by focusing on theinteractions among word-level meanings in the sentence. This level of processing caninclude the semantic disambiguation of words with multiple senses in an analogous wayto how syntactic disambiguation of words that can function as multiple parts-of-speech isaccomplished at the syntactic level. Semantic disambiguation permits one and only onesense of polysemous words to be selected and included in the semantic representation ofthe sentence. For example, amongst other meanings, file as a noun can mean either afolder for storing papers, or a tool to limit ones fingernails, or a line of individuals in aqueue. If information from the rest of the sentence were requi red for the disambiguation,the semantic, not the lexical level, would do the disambiguation. A wide scarper ofmethods can be implemented to accomplish the disambiguation, some which requireinformation as to the frequency with which each sense occurs in a particular school principal ofinterest, or in general usage, some which require consideration of the local context, andothers which utilize pragmatic knowledge of the domain of the roll.DiscourseWhile syntax and semantics work with sentence-length units, the discourse level of NLPworks with units of text longer than a sentence. That is, it does not interpret multisentencetexts as just concatenated sentences, each of which can be construe singly.Rather, discourse focuses on the properties of the text as a whole that convey meaning bymaking connections between component sentences. Several types of discourse processingcan occur at this level, two of the most common being anaphora occlusion anddiscourse/text structure recognition. Anaphora resolution is the transposition of words suchas pronouns, which are semantically vacant, with the appropriate entity to which theyrefer (30). Discourse/text structure recognition determines the functions of sentences inthe text, which, in turn, adds to the meaningful representation of the text. For example,newspaper articles can be deconstructed into discourse components such as Lead, MainStory, Previous Events, Evaluation, Attributed Quotes, and Expectation.PragmaticThis level is concerned with the purposeful use of language in situations and utilizescontext over and above the contents of the text for understanding The goal is to explainhow extra meaning is read into texts without actually being encoded in them. Thisrequires much world knowledge, including the understanding of intentions, plans, andgoals. Some NLP applications may utilize knowledge bases and inferencing modules. Forexample, the avocation two sentences require resolution of the anaphoric term they, butthi s resolution requires pragmatic or world knowledge.Natural Language processing in textual information retrievalAs the reader has probably already deduced, the complexity associated with natural language is especially key when retrieving textual information Baeza-Yates, 1999 to satisfy a users information needs. This is why in Textual Information Retrieval, NLP techniques are often used Allan, 2000 both for facilitating descriptions of document content and for presenting the users query, all with the aim of comparing both descriptions and presenting the user the documents that scoop out satisfy their information needs.In other words, a textual information retrieval system carries out the following tasks in response to a users queryIndexing the collection of documents in this phase, NLP techniques are applied to generate an index containing document descriptions. Normally each document is described through a set of terms that, in theory, best represents its content.When a user form ulates a query, the system analyses it, and if necessary, transforms it with the hope of representing the users information needs in the same way as the document content is represented.The system compares the description of each document with that of the query, and presents the user with those documents whose descriptions are closest to the query description.The results are usually listed in order of relevancy, that is, by the level of similarity between the document and query descriptions.CUsershpDesktopUntitled.bmpThe architecture of an information retrieval systemAs of now there are no NLP techniques that allow us to leave off a documents or querys meaning without any mistakes. In fact, the scientific community is divided on the procedure to follow in reaching this goal. In the following section we will explain the functions and peculiarities of the two key approaches to natural language processing a statistical approach and a linguistic focus. Both proposals differ considerably , even though in practice natural language processing systems use a mixed approach, combining techniques from both focuses.CONCLUSIONDespite the recyclable universal aspect of programming languages, these languages are still understood only by very few people, unlike the natural languages which are understood by all. The ability to turn natural into programming languages will eventually decrease the gap between very few and all, and open the benefits of computer programming to a larger number of users. In this paper, we showed how current state of-the-art techniques in natural language processing can allow us to devise a system for natural language programming that addresses both the descriptive and procedural programming paradigms. The output of the system consists of automatically generated program skeletons, which were shown to help non-expert programmers in their task of describing algorithms in a programmatic way.As it turns out, advances in natural language processing helped the task of natural language programming. But we believe that natural language processing could as well benefit from natural language programming. The process of deriving computer programs starting with a natural language text implies a plethora of sophisticated language processing tools such as syntactic parsers, clause detectors, argument structure identifiers, semantic analyzers, methods for co reference resolution, and so forth which can be efficaciously put at work and evaluated within the framework of natural language programming.We thus see natural language programming as a potential difference large scale end-user (or rather, end computer) application of text processing tools, which puts forward challenges for the natural language processing community and could eventually trigger advances in this field.