A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging

Chen, Xinchi; Qiu, Xipeng; Huang, Xuanjing

doi:10.24963/ijcai.2017/553

Cited by 24 publications

(19 citation statements)

References 19 publications

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“…The third module in our model is a convolutional neural networks (CNN) to extract local contextual information. Motivated by the multiple kernels CNN used for sequential labeling (Chen et al, 2016), we also apply such CNN with different window sizes to this task.…”

Section: Cnn-lstm Model With Crf or Softmax Inferencementioning

confidence: 99%

Neural Metaphor Detecting with CNN-LSTM Model

Wu¹,

Wu²,

Chen³

et al. 2018

Proceedings of the Workshop on Figurative Language Processing

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Metaphors are figurative languages widely used in daily life and literatures. It's an important task to detect the metaphors evoked by texts. Thus, the metaphor shared task is aimed to extract metaphors from plain texts at word level. We propose to use a CNN-LSTM model for this task. Our model combines CNN and LSTM layers to utilize both local and long-range contextual information for identifying metaphorical information. In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task. We also incorporated some additional features such as part of speech (POS) tags and word cluster to improve the performance of model. Our best model achieved 65.06% F-score in the all POS testing subtask and 67.15% in the verbs testing subtask.

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Section: Cnn-lstm Model With Crf or Softmax Inferencementioning

confidence: 99%

Neural Metaphor Detecting with CNN-LSTM Model

Wu¹,

Wu²,

Chen³

et al. 2018

Proceedings of the Workshop on Figurative Language Processing

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“…• F 1 seg : F1 measure of Chinese word segmentation. This is the standard metric used in Chinese word segmentation task (Qiu et al, 2013;Chen et al, 2017). • F 1 udep : F1 measure of unlabeled dependency parsing.…”

Section: Methodsmentioning

confidence: 99%

“…• LSTM+CRF with {B, M, E, S} tags. The only difference between this scenario and the previous one is whether using conditional random field (CRF) after the MLP (Lafferty et al, 2001;Chen et al, 2017). • LSTM+MLP with {app, seg} tags.…”

Section: Chinese Word Segmentationmentioning

confidence: 99%

A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing

Yan

Qiu

Huang

2020

Transactions of the Association for Computational Linguistics

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Chinese word segmentation and dependency parsing are two fundamental tasks for Chinese natural language processing. The dependency parsing is defined on wordlevel. Therefore word segmentation is the precondition of dependency parsing, which makes dependency parsing suffer from error propagation and unable to directly make use of the character-level pretrained language model (such as BERT). In this paper, we propose a graph-based model to integrate Chinese word segmentation and dependency parsing. Different from previous transition-based joint models, our proposed model is more concise, which results in fewer efforts of feature engineering. Our graph-based joint model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. Besides, when BERT is combined, our model can substantially reduce the performance gap of dependency parsing between joint models and goldsegmented word-based models. Our code is publicly available at https://github. com/fastnlp/JointCwsParser.

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“…Given an input sequence x = (w 1 , ..., w n ) where w i is the i-th eojeol, Korean morphological analysis aims to produce an output sequence y = ( m 1 , t 1 , ..., m k , t k ) where m j is the j-th morpheme and t j is its POS tag. Korean morphological analysis is different from existing NLP tasks such as English POS tagging (Toutanova et al, 2003;Manning, 2011), and joint word segmentation and POS tagging for Chinese (Zhang and Clark, 2008;Shao et al, 2017;Chen et al, 2017). In English POS tagging, a word and its tag have a one-to-one mapping so that the length of an input sequence is equal to that of an output sequence.…”

Section: Morpheme Processing and Pos Taggingmentioning

confidence: 99%

Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model

Song¹,

Park²

2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Korean morphological analysis has been considered as a sequence of morpheme processing and POS tagging. Thus, a pipeline model of the tasks has been adopted widely by previous studies. However, the model has a problem that it cannot utilize interactions among the tasks. This paper formulates Korean morphological analysis as a combination of the tasks and presents a tied sequence-tosequence multi-task model for training the two tasks simultaneously without any explicit regularization. The experiments prove the proposed model achieves the state-of-the-art performance.

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A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging

Cited by 24 publications

References 19 publications

Neural Metaphor Detecting with CNN-LSTM Model

Neural Metaphor Detecting with CNN-LSTM Model

A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing

Korean Morphological Analysis with Tied Sequence-to-Sequence Multi-Task Model

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