Entity attribute value extraction method based on bidirectional long-short-term memory network

Abstract

The invention belongs to the technical field of network text data processing, and particularly relates to an entity attribute value extraction method based on a bidirectional long-short-term memory network, which comprises the following steps of: 1, preprocessing a document set; step 2, adopting category mapping to identify attribute values from statements containing entities; 3, performing deep syntactic analysis on the sentences of the entities and the attribute values, and extracting related sentence components as training corpora; and 4, performing vector conversion on the training corpusby adopting a word vector model, training BLSTM model parameters in combination with syntactic features, and classifying the entities and the attribute values into a given attribute name category. According to the method, a bidirectional long-short-term memory network is adopted, so that the relationship among entities, attribute names and attribute values can be accurately judged.

Description

technical field [0001] The invention belongs to the technical field of network text data processing, and in particular relates to a method for extracting entity attribute values ​​based on a two-way long-short-term memory network. Background technique [0002] With the vigorous development of various online media, unstructured text data on the Internet has shown explosive growth, such as news, Weibo, blogs, chat records, emails, etc. These data contain a lot of valuable information, such as entities. Entity is the most basic unit of information in text data. With the release of a large number of data, the ambiguity and diversity of entity names are becoming more and more common. Only identifying entity names cannot meet people's needs for deep semantic information in text. Therefore, in order to describe the essence of an entity, more and more researchers have begun to pay attention to the attribute information of the entity, such as the age, place of origin, and date of bi...

AI Technical Summary

Problems solved by technology

However, the massiveness, heterogeneity, openness, and non-standardization of text data on the Internet lead to various categories and complex composition of entity attribute values, which brings new research challenges to entity attribute value extraction technology.

[0003] The inventors have found that the existing solutions have at least the following defects: the relationship between entities, attribute names and attribute values ​​is difficult to distinguish

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