By Alexander Franz
This is an exhilarating time for man made Intelligence, and for typical Language Processing particularly. over the past 5 years or so, a newly revived spirit has received prominence that grants to revitalize the complete box: the spirit of empiricism.
This publication introduces a brand new method of the real NLP factor of computerized ambiguity answer, in keeping with statistical types of textual content. This technique is in comparison with prior paintings and proved to yield greater accuracy for typical language research. a good implementation approach is usually defined, that is without delay invaluable for typical language research. The ebook is noteworthy for demonstrating a brand new empirical method of NLP; it really is crucial studying for researchers in typical language processing or computational linguistics.
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Extra resources for Automatic Ambiguity Resolution in Natural Language Processing: An Empirical Approach
Semantic or pragmatic information is also required. 2 Semantic and Pragmatic Approaches Semantic and pragmatic principles account for additional factors that are ignored by purely syntactic approaches. However, these approaches also suffer from a variety of shortcomings. 32 2. Previous Work on Syntactic Ambiguity Resolution As demonstrated by [Gibson, 1991], the specific principle of lexicai preference proposed by Ford, Bresnan, and Kaplan, as well as the specific pragmatic principles of Crain and Steedman, make wrong predictions in many cases.
Some of these principles contained prescriptions concerning the resohltion of structural ambiguity: - R i g h t A s s o c i a t i o n . TerminM symbols (lexical categories) aim to attach to the lowest right non-terminal node in the parse tree. - T w o S e n t e n c e M e m o r y L i m i t . " - E a r l y C l o s u r e . " - B a c k t r a c k i n g is E x p e n s i v e . When a phrase has been closed, it is "costly in terms of perceptual complexity" to backtrack and reorganize the constituents of that phrase.
Dependence between variables is introduced into the model by adding interaction terms. Interaction terms represent the conlbined effect of the levels of more than variable. For example, the term ux2(ij) represents the effect, of the ith level of the first variable and the j t h level of the second variable. 44 3. Loglinear Models for Ambiguity Resolution The notion of interaction term is central to loglinear models. Given a problem with a set of variables, a loglinear model is specified by the interaction terms that are included.
Automatic Ambiguity Resolution in Natural Language Processing: An Empirical Approach by Alexander Franz