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Coreference-aware representation learning for neural named entity recognition

A technology of named entity recognition and convolutional neural network, applied in the field of computer learning systems, which can solve problems such as inconsistent semantic type prediction

Pending Publication Date: 2021-02-02
BAIDU USA LLC
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  • Claims
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Problems solved by technology

Such restrictions may lead these models to produce globally inconsistent semantic type predictions

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  • Coreference-aware representation learning for neural named entity recognition
  • Coreference-aware representation learning for neural named entity recognition
  • Coreference-aware representation learning for neural named entity recognition

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Embodiment Construction

[0022] In the following description, for purposes of explanation, specific details are set forth in order to provide an understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these details. Furthermore, those skilled in the art will appreciate that the embodiments of the present disclosure described below can be implemented in various ways, such as a process, apparatus, system, device or method on a tangible computer readable medium.

[0023] Components or modules shown in the figures are illustrations of exemplary embodiments of the present disclosure and are intended to avoid obscuring the present disclosure. It should also be understood that throughout the discussion, components may be described as separate functional units, which may include subunits, but those skilled in the art will recognize that various components or portions thereof may be divided into separate components or...

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Abstract

Previous neural network models that perform named entity recognition (NER) typically treat the input sentences as a linear sequence of words but ignore rich structural information, such as the coreference relations among non-adjacent words, phrases, or entities. Presented herein are novel approaches to learn coreference-aware word representations for the NER task. In one or more embodiments, a CNN-BiLSTM-CRF neural architecture is modified to include a coreference layer component on top of the BiLSTM layer to incorporate coreferential relations. Also, in one or more embodiments, a coreferenceregularization is added during training to ensure that the coreferential entities share similar representations and consistent predictions within the same coreference cluster. A model embodiment achieved new state-of-the-art performance when tested.

Description

technical field [0001] The present disclosure generally relates to systems and methods of computer learning that can provide enhanced computer performance, features, and utility. More specifically, the present disclosure relates to implementations for learning coreference-aware word representations. Background technique [0002] Named entity recognition (NER) is one of the fundamental tasks in natural language processing (NLP), which has a huge impact on many downstream applications including relation extraction, knowledge base completion, and entity linking. Given an input text, NER aims to find named entities from the raw text and classify them into predefined semantic types such as person (PER), organization (ORG), location (LOC), etc. [0003] The traditional way of NER is to treat it as a sequence labeling task, where each word is assigned a label (for example, "B-PER" (the starting word of the PERSON semantic type), "I-PER" ( PERSON semantic type), "O" ("Other" word,...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/295G06N3/04G06K9/62
CPCG06N3/049G06N3/045G06F18/22G06F40/295G06N20/00G06N3/08G06N7/01G06N3/044G06F17/18G06N5/046G06N3/02
Inventor 费洪亮戴泽宇李平
Owner BAIDU USA LLC
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