Chinese NER Using Lattice LSTM

Published in

Annual Meeting of the Association for Computati...(2018)

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TL;DR

A lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon, is investigated, which outperforms both word-based and character-based L STM baselines.

Abstract

We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.

Authors

Jie Yang

5 papers

References54 items

YEDDA: A Lightweight Collaborative Text Span Annotation Tool

Empower Sequence Labeling with Task-Aware Neural Language Model

Lattice Long Short-Term Memory for Human Action Recognition

Neural Word Segmentation with Rich Pretraining

Semi-supervised Multitask Learning for Sequence Labeling

Chinese NER Using Lattice LSTM

Published in

Annual Meeting of the Association for Computati...(2018)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Jie Yang

5 papers

References54 items

YEDDA: A Lightweight Collaborative Text Span Annotation Tool

Empower Sequence Labeling with Task-Aware Neural Language Model

Lattice Long Short-Term Memory for Human Action Recognition

Neural Word Segmentation with Rich Pretraining

Semi-supervised Multitask Learning for Sequence Labeling

Cross-Sentence N-ary Relation Extraction with Graph LSTMs

Semi-supervised sequence tagging with bidirectional language models

Neural Lattice-to-Sequence Models for Uncertain Inputs

Adversarial Multi-Criteria Learning for Chinese Word Segmentation

A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media

F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media

Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation

Multi-prototype Chinese Character Embedding

Combining Discrete and Neural Features for Sequence Labeling

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

Neural Architectures for Named Entity Recognition

Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning

Named Entity Recognition with Bidirectional LSTM-CNNs

Joint Entity Recognition and Disambiguation

Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings

Long Short-Term Memory Neural Networks for Chinese Word Segmentation

Bidirectional LSTM-CRF Models for Sequence Tagging

Boosting Named Entity Recognition with Neural Character Embeddings

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Word Segmentation for Chinese Novels

Domain Adaptation for CRF-based Chinese Word Segmentation using Free Annotations

Comparison of the Impact of Word Segmentation on Name Tagging for Chinese and Japanese

Lexicon Infused Phrase Embeddings for Named Entity Resolution

Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.

Distributed Representations of Words and Phrases and their Compositionality

Discriminative Learning with Natural Annotations: Word Segmentation as a Case Study

Effective Bilingual Constraints for Semi-Supervised Learning of Named Entity Recognizers

Named Entity Recognition with Bilingual Constraints

Unsupervised Domain Adaptation for Joint Segmentation and POS-Tagging

Natural Language Processing (Almost) from Scratch

Chinese Named Entity Recognition with a Sequence Labeling Approach: Based on Characters, or Based on Words?

Design Challenges and Misconceptions in Named Entity Recognition

Chinese Named Entity Recognition with Conditional Probabilistic Models

The Third International Chinese Language Processing Bakeoff: Word Segmentation and Named Entity Recognition

Word Segmentation and Named Entity Recognition for SIGHAN Bakeoff3

Chinese Named Entity Recognition with Conditional Random Fields

Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach

The Penn Chinese TreeBank: Phrase structure annotation of a large corpus

Named Entity Recognition with Long Short-Term Memory

Chinese Word Segmentation as Character Tagging

Backpropagation Applied to Handwritten Zip Code Recognition

Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure

Dropout: a simple way to prevent neural networks from overfitting

Chinese Named Entity Recognition_via Joint Identification and Categorization

Chinese Named Entity Recognition and Word Segmentation Based on Character

Unsupervised Segmentation Helps Supervised Learning of Character Tagging for Word Segmentation and Named Entity Recognition

Characterbased LSTM-CRF with radical-level features for Chinese named entity recognition

Field of Study

Computer Science

Journal Information

Name

ArXiv

Volume

abs/1805.02023

Venue Information

Name

Annual Meeting of the Association for Computational Linguistics

Type

conference

URL

https://www.aclweb.org/anthology/venues/acl/

Alternate Names

Annu Meet Assoc Comput Linguistics
Meeting of the Association for Computational Linguistics
ACL
Meet Assoc Comput Linguistics