[Purpose/significance] Keywords, as a kind of vocabulary or term that can reveal the subject and core content of a text, can identify the functions and provide the underlying index support for fast and accurate acquisition of knowledge and documents.[Method/process] Aiming at the existing studies that are mostly limited to the semantic representation of symbols at the text level in vocabulary context modeling, this paper proposes a lexical function recognition model based on multi-feature fusion. On the basis of capturing the context-dependent features of keywords using the BERT model, the position information of keywords in the keyword list and the full text and prior knowledge of vocabulary functions are fused, and then the attention mechanism and feed-forward neural network are used for the identification of key words by problem-solving method.[Result/conclusion] The experimental results show that both the location information and priori knowledge of the keywords can improve their word function recognition effect, and the prior knowledge has a greater contribution to the recognition effect.
Zhang Guobiao
,
Li Pengcheng
,
Lu Wei
,
Cheng Qikai
. Research on Keyword Semantic Function Recognition Based on Multi-feature Fusion[J]. Library and Information Service, 2021
, 65(9)
: 89
-96
.
DOI: 10.13266/j.issn.0252-3116.2021.09.010
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