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In linguistics, grammatical relations (also called grammatical functions, grammatical roles, or syntactic functions) are functional relationships between constituents in a clause. The standard examples of grammatical functions from traditional grammar are subject, direct object, and indirect object. In recent times, the syntactic functions (more generally referred to as grammatical relations), typified by the traditional categories of subject and object, have assumed an important role in linguistic theorizing, within a variety of approaches ranging from generative grammar to functional and cognitive theories. Many modern theories of grammar are likely to acknowledge numerous further types of grammatical relations (e.g. complement, specifier, predicative, etc.). The role of grammatical relations in theories of grammar is greatest in dependency grammars, which tend to posit dozens of distinct grammatical relations. Every head-dependent dependency bears a grammatical function.
Implicit sentiment analysis method based on syntax enhancement and context awareness
PendingCN122263853AImprove emotional expressionimprove accuracySemantic analysisBiological modelsCosine similarityGrammatical relation
The present application relates to the technical field of natural language processing, in particular to an implicit sentiment analysis method based on syntax enhancement and context awareness, which solves the technical problems that the existing analysis method ignores the difference of sentiment contribution of different syntax relations and easily introduces irrelevant semantic noise, etc., which encodes the word vector of the target sentence and its context sentence to obtain the text semantic feature of the target sentence and the text context feature of the target sentence; a syntax dependency relation weight matrix is constructed to fuse the text semantic feature and the syntax feature of the target sentence to obtain the syntax semantic joint feature; the cosine similarity of the target sentence and its context sentence is calculated and the context features are weighted and aggregated to obtain the aggregated context features; the local to global sentiment reasoning is realized by using the self-attention mechanism, the gated fusion and the hierarchical feature fusion framework based on bidirectional LSTM; the sentence-level global feature is input into the full connection layer to predict the sentiment polarity probability distribution of the target sentence, and finally the sentiment label category of the target sentence is obtained.
Owner:TAIYUAN UNIVERSITY OF TECHNOLOGY