| Word Sense Disambiguation |
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| Word sense disambiguation (WSD) is the task of selecting the appropriate senses of a word in a given context. It is essence of communication in a natural language. It is motivated by its use in many crucial applications such as Information retrieval, Information extraction, Machine Translation, artof- Speech tagging, etc. Various issues like scalability, ambiguity, diversity (of languages) and evaluation pose challenges to WSD solutions. |
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| Sub-Lexical Modelling |
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| The finite state transducer (FST) approach [1] has been widely used recently as an effective and flexible framework for speech systems. In this framework, a speech recognizer is represented as the composition of a series of FSTs combining various knowledge sources across sub-lexical and high-level linguistic layers. |
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| A Simple Rule-Based Part of Speech Tagger |
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| Automatic part of speech tagging is an area of natural language processing where statistical techniques have been more successful than rule-based methods. In this paper, we present a simple rule-based part of speech tagger which automatically acquires its rules and tags with accuracy comparable to stochastic taggers. |
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| Semantic Analysis - Scope |
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| Topics Covered: Goals of Semantic Analyzer, The Varargs Bug, Why separate semantic analysis, What does semantic analysis do, Typical semantic errors, A sample semantic analyzer etc. |
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| Semantic Analysis |
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| Topics Covered: What Is Semantic Analysis, Types and Declarations, Type Checking, Case Study: ML Data Type, Implementation, Type Compatibility, Sub typing, Scope Checking, Object Oriented Issues |
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| The Use of a Structural N-gram Language Model |
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| This paper describes the use of a statistical structural N-gram model in the natural language generation component of a Spanish-English generation-heavy hybrid machine translation system. A structural N-gram model captures the relationship between words in a dependency representation without taking into account the overall structure at the phrase level. |
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| Natural Language Processing |
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| Natural Language Processing (NLP) is the computerized approach to analyzing text that is based on both a set of theories and a set of technologies. And, being a very active area of research and development, there is not a single agreed-upon definition that would satisfy everyone, but there are some aspects, which would be part of any knowledgeable person’s definition. |
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| Natural Language Generation |
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| Natural Language Generation is a subfield of Computational Linguistics and language-oriented Artificial Intelligence research devoted to studying and simulating the production of written or spoken discourse. The study of human language generation is a multidisciplinary enterprise, requiring expertise in areas of linguistics, psychology, engineering and computer science. |
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| lexical-semantics |
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| Topics Covered: Word meanings, Relations among lexemes and their senses, The internal structure of words, Applications to Linking Theory and Shallow Semantic Interpretation, Creativity and the lexicon, Selectional restriction-based word sense disambiguation. |
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| Interlingua |
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| Interlingua (ISO 639 language codes ia, ina) is an international auxiliary language (IAL), developed between 1937 and 1951 by the International Auxiliary Language Association (IALA). |
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| Context-Free Grammars For English |
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| Topics Covered: Context-free rules and trees, Sentence-level constructions, The noun phrase, The verb phrase and sub categorization, Spoken language syntax, Grammar equivalence and normal form, Finite-state and context-free grammars, Grammars and human processing |
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| Coherence in text |
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| The concept of text coherence was developed for linear text, i.e. text of sequentially organized content. The present article addresses to what extent this concept can be applied to hypertext. |
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