One possible solution, which has not been extensively explored previously, is to augment productions in medical sublanguage grammars with probabilities to resolve the ambiguity. However, given the complexity of the medical domain, parsers using such grammars inevitably encounter ambiguous sentences, which could be interpreted by different groups of production rules and consequently result in two or more parse trees. Semantic-based sublanguage grammars have been shown to be an efficient method for medical language processing. Xu, Hua AbdelRahman, Samir Lu, Yanxin Denny, Joshua C. InĪpplying Semantic-based Probabilistic Context-Free Grammar to Medical Language Processing – A Preliminary Study on Parsing Medication Sentences It has inspired a large body of research. Note that none.a particularly challenging task, because of the inherent ambiguity of natural languages on both sides. In this domain, queries typically show a deeply nested structure, which makes the semantic parsing task rather challenging, e.g.: What states border.only 80% of the GEOQUERY queries are semantically tractable, which shows that GEOQUERY is indeed a more challenging domain than ATIS. Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques The technical discussion is organized around examples taken from the prototype LISP system which implements parts of the theory. The word expert theory is advanced as a better cognitive model of human language expertise than the traditional rule-based approach. The parser is structured around a coroutine control environment in which the generator-like word experts ask questions and exchange information in coming to collective agreement on sentence meaning. In the word expert parser, knowledge about language is distributed across a population of procedural experts, each representing a word of the language, and each an expert at diagnosing that word's intended usage in context. Toward a theory of distributed word expert natural language parsingĪn approach to natural language meaning-based parsing in which the unit of linguistic knowledge is the word rather than the rewrite rule is described.
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