Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: The Fourth International Chinese Language Processing Bakeoff: Chinese Word Segmentation, Named Entity Recognition and Chinese POS Tagging
The Third International Chinese Language Processing Bakeoff: Word Segmentation And Named Entity Recognition
Regularized Structured Perceptron: A Case Study on Chinese Word Segmentation, POS Tagging and Parsing
Long Short-Term Memory Neural Networks for Chinese Word Segmentation
Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation
Bi-directional LSTM Recurrent Neural Network for Chinese Word Segmentation
A single-model approach for Arabic segmentation, POS tagging, and named entity recognition
Empirical Study of Unsupervised Chinese Word Segmentation Methods for SMT on Large-scale Corpora
Context-Based Chinese Word Segmentation using SVM Machine-Learning Algorithm without Dictionary Support
Character-Based LSTM-CRF with Radical-Level Features for Chinese Named Entity Recognition
A Hybrid Approach to Vietnamese Word Segmentation Using Part of Speech Tags
Classical Chinese Sentence Segmentation
A Simple and Effective Neural Model for Joint Word Segmentation and POS Tagging
The CIPS-SIGHAN CLP2010 Chinese Word Segmentation Backoff
Subword Encoding in Lattice LSTM for Chinese Word Segmentation
chinese word segmentation by classification of characters .
Word-Context Character Embeddings for Chinese Word Segmentation
Long Short-Term Memory for Japanese Word Segmentation
Max-Margin Tensor Neural Network for Chinese Word Segmentation
Joint word segmentation, POS-tagging and syntactic chunking
Chinese Segmentation And New Word Detection Using Conditional Random Fields
a formal component - based softwareengineering approach for developingtrustworty systems .
Food Allergy: Review, Classification and Diagnosis
Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition
Superpixel Segmentation Using Gaussian Mixture Model
news event extraction using 5 w 1 h approach & its analysis .