Named entity recognizing method based on LSTM

Abstract

The invention relates to a named entity recognizing method based on LSTM. The method includes the steps that training linguistic data of named entity recognition is labeled to form labeled linguisticdata; words and characters in the labeled linguistic data are converted into vector quantities; a named entity recognition model based on LSTM is set up by using word and character vector quantities,and parameters of the named entity recognition model are trained; named entity recognition prediction is conducted on the data to be predicted through the trained named entity recognition model. By adopting the vector quantities based on the words and characters, characteristics of the characters and the words can be obtained, and the problem of unregistered words can be avoided; in addition, compared with a traditional pure CRF model algorithm, the named entity recognizing method adopting long short-term memory neural network is capable of absorbing more character and word characteristics tofurther improve entity recognition precision.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to an LSTM-based named entity recognition method. Background technique [0002] Named Entity Recognition (NER for short) refers to identifying entities with specific meanings in text, mainly including names of people, places, institutions, and proper nouns. Practical scenarios for named entity recognition methods include: [0003] Scenario 1: Event detection. Place, time, and person are several basic components of time. When constructing an event summary, relevant persons, places, units, etc. can be highlighted. In the event search system, related people, time, and places can be used as index keywords. The relationship between several components of an event describes the event in more detail at the semantic level. [0004] Scenario 2: Information retrieval. Named entities can be used to enhance and improve the effect of the retrieval system. When the user enter...

AI Technical Summary

Problems solved by technology

[0009] Dictionary-based named entity recognition relies heavily on the dictionary database and cannot identify unregistered words

HMM (Hidden Markov) and CRF (Conditional Random Field) methods based on word frequency statistics can only associate the semantics of the next word with the previous word, and the recognition accuracy is not high enough, especially the recognition rate of unregistered words is low

The method based on the artificial neural network model has the problem of gradient disappearance during training, and in actual applications, the number of network layers is small, and the final named entity recognition results have no obvious advantages

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