By Alexander Franz
This is a thrilling 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 entire box: the spirit of empiricism.
This publication introduces a brand new method of the real NLP factor of computerized ambiguity answer, in response to statistical types of textual content. This method is in comparison with prior paintings and proved to yield larger accuracy for usual language research. an efficient implementation method can be defined, that is without delay beneficial for traditional language research. The publication is noteworthy for demonstrating a brand new empirical method of NLP; it's crucial studying for researchers in common language processing or computational linguistics.
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Additional resources for Automatic Ambiguity Resolution in Natural Language Processing: An Empirical Approach
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. Gibson concludes that while both studies were successful in pointing out necessary componentsof ambiguity resolution, these principles in isolation do not constitute an empirically adequate theory. The models based on the AI approach of commonsense semantics suffer from different sorts of problems.
2 Ambiguity Resolution as a Classification Problem As described in Chapter 2, an ambiguous natural language expression has more than one possible interpretation. For example, a word might have more than one possible Part-of-Speech (POS), or a Prepositional Phrase might have more than possible attachment site. This section shows how ambiguity resolution can be viewed as a classification problem. 1 Making Decisions under Uncertainty Decision theory is concerned with choosing the "best" action from a set of alternatives.
This results in a set of a t t a c h m e n t "bigrams". Then, Maximum Likelihood estimates were used to derive the following probabilities: Pw,rb_attach (preposition verb,noun) Pnoun_attach (preposition verb,noun) The probat)ilities for specific nouns and verbs were smoothed with each preposition's observed frequency of noun a t t a c h m e n t and verb a t t a c h m e n t in general, I(N,p) and l(v,p) The a t t a c h m e n t decision was then based on the log ~-7~F" likelihood ratio between the probabilities for verb and noun attachment.
Automatic Ambiguity Resolution in Natural Language Processing: An Empirical Approach by Alexander Franz