Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation
Recurrent neural network(RNN) has been broadly applied to natural language processing(NLP) problems. This kind of neural network is designed for modeling sequential data and has been testified to be quite efficient in sequential tagging tasks. In this paper, we propose to use bi-directional RNN with long short-term memory(LSTM) units for Chinese word segmentation, which is a crucial preprocess task for modeling Chinese sentences and articles. Classical methods focus on designing and combining hand-craft features from context, whereas bi-directional LSTM network(BLSTM) does not need any prior knowledge or pre-designing, and it is expert in keeping the contextual information in both directions. Experiment result shows that our approach gets state-of-the-art performance in word segmentation on both traditional Chinese datasets and simplified Chinese datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
ArticlesChinese Word SegmentationSegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Bidirectional LSTM-CRF Attention-based Model for Chinese Word Segmentation
Chinese word segmentation (CWS) is the basic of Chinese natural language processing (NLP). The quality of word segmentation will directly affect the rest of NLP tasks. Recently, with the artificial intelligence tide risi…
Chinese Word SegmentationSegmentationClassification of 12-Lead ECG Signals with Bi-directional LSTM Network
We propose a recurrent neural network classifier to detect pathologies in 12-lead ECG signals and train and validate the classifier with the Chinese physiological signal challenge dataset (http://www.icbeb.org/Challenge.…
ECG ClassificationGeneral ClassificationLong Short-Term Memory for Japanese Word Segmentation
This study presents a Long Short-Term Memory (LSTM) neural network approach to Japanese word segmentation (JWS). Previous studies on Chinese word segmentation (CWS) succeeded in using recurrent neural networks such as LS…
Chinese Word SegmentationJapanese Word SegmentationSegmentationChinese NER Using Lattice LSTM
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 explicitl…
Chinese Named Entity RecognitionNERSentenceDetection of Chinese Word Usage Errors for Non-Native Chinese Learners with Bidirectional LSTM
Selecting appropriate words to compose a sentence is one common problem faced by non-native Chinese learners. In this paper, we propose (bidirectional) LSTM sequence labeling models and explore various features to detect…
Grammatical Error DetectionPOSPositionSentence