Open domain conference information named entity identification method and system

Abstract

The invention discloses an open domain conference information named entity identification method and an open domain conference information named entity identification system. The identification methodspecifically comprises the steps of obtaining original text information of an open domain data conference; converting the original text information into a plurality of digital sequences, wherein eachdigital sequence is one sentence; mapping the digital sequence into a word vector through a word embedding layer to obtain a word vector; adopting a named entity recognition model for the word vectorto obtain an optimal label combination index of each label at each time; converting the optimal combined tag index into a tag name through a word list; synthesizing the label names corresponding to the characters into word labels; and obtaining a conference name named entity and a conference place named entity according to the word tags. According to the method, the first character, the middle character and the last character of the entity type are marked on the basis of the characters, the marked type of one word can be formed, and the influence of new word processing, different word segmentation tools and word segmentation errors on the recognition and extraction effect is avoided.

Description

technical field [0001] The invention relates to the field of conference information retrieval, in particular to a method and system for identifying named entities of open domain conference information. Background technique [0002] With the rapid development of science and technology, there are more and more platforms and methods for academic exchanges among scientific and technological workers. Academic conferences are a platform for scientific and technological workers to introduce and share their scientific research work and achievements, as well as learn about research content and research results in related fields, by conducting academic lectures and publishing academic papers. Through academic conferences, it is possible to track the research directions and research hotspots in related fields, understand the research difficulties and key technical methods in the current research, and obtain instructive conclusions. In addition, tracking relevant information of academi...

AI Technical Summary

Problems solved by technology

[0004] Early named entity recognition and extraction were mainly based on rule-based methods. However, due to the variability and complexity of different named entity types in different fields, rules based on linguistic knowledge are poorly transplantable and have limitations.

In recent years, with the rise of machine learning and deep learning, statistics-based methods only need to use labeled corpora for training, and the accuracy of statistics on features from corpus is low, and there is a lack of public labeled corpus in specific fields, resulting in Recognition of low accuracy in extracting open-domain text

Images