Semantic role recognition method based on phrase structure tree

Abstract

The invention relates to a semantic role recognition method based on a phrase structure tree. The method comprises the steps of sentence pruning, wherein when a system inputs one sentence, phrase analysis is performed on the sentence, the analyzed result is subjected to pruning through a parenthesis or a coordination structure, the complexity of the sentence is simplified, and the length of the sentence is shortened; clause extracting processing, wherein on the basis of the phrase structure tree, clauses in the pruned sentences are extracted, the extracted clauses and the remaining portion obtained after the clauses are extracted are subjected to semantic role analysis separately, the complete sentence semantic role is obtained, and the analysis result of the semantic role is reduced; boundary correction, wherein the reduced semantic role is combined with the phrase tree to perform predicate argument boundary correction on the sentences, and finally the sentence semantic role analysisresult is output. The sentence complexity is simplified, the sentence length is shortened, the complex and long sentence can be effectively processed, and the semantic role labeling condition is improved.

Description

technical field [0001] The invention relates to a natural language translation technology, in particular to a semantic role recognition method combined with a phrase structure tree. Background technique [0002] Shallow semantic analysis is one of the research hotspots in the field of natural language processing in recent years, and semantic role labeling is the main form used in shallow semantic analysis at present. The main task of semantic role labeling (Semantic Role Labeling, referred to as SRL) is to analyze the " Predicate-argument" structure, given a sentence, find out the corresponding semantic role components of the predicate in the sentence, including core semantic roles (such as agent, subject, etc.) and subsidiary semantic roles (such as place, time, method, reason, etc. ). The semantic roles annotated by SRL provide strong support for answering 5W questions (who, what, when, where, why). For example, "He bought a bunch of roses yesterday at the Florist", perf...

AI Technical Summary

Problems solved by technology

The above method combines the phrase structure syntax tree to prune the sentence to realize the simplification of the sentence, but it does not summarize and classify the type of the sentence, and the degree of simplification of the sentence is not sufficient

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