Jie Yang


2019

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Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content
Sepideh Mesbah | Jie Yang | Robert-Jan Sips | Manuel Valle Torre | Christoph Lofi | Alessandro Bozzon | Geert-Jan Houben
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection. Existing dictionary-based, semi-supervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies. In this paper, we introduce a data augmentation approach that leverages variational autoencoders to learn high-quality data distributions from a large unlabeled dataset, and subsequently, to automatically generate a large labeled training set from a small set of labeled samples. This allows for efficient social-media ADR detection with low training and re-training costs to adapt to the changes and emergence of informal medical laymen terms. An extensive evaluation performed on Twitter and Reddit data shows that our approach matches the performance of fully-supervised approaches while requiring only 25% of training data.

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Subword Encoding in Lattice LSTM for Chinese Word Segmentation
Jie Yang | Yue Zhang | Shuailong Liang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We investigate subword information for Chinese word segmentation, by integrating sub word embeddings trained using byte-pair encoding into a Lattice LSTM (LaLSTM) network over a character sequence. Experiments on standard benchmark show that subword information brings significant gains over strong character-based segmentation models. To our knowledge, this is the first research on the effectiveness of subwords on neural word segmentation.

2018

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Chinese NER Using Lattice LSTM
Yue Zhang | Jie Yang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.

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YEDDA: A Lightweight Collaborative Text Span Annotation Tool
Jie Yang | Yue Zhang | Linwei Li | Xingxuan Li
Proceedings of ACL 2018, System Demonstrations

In this paper, we introduce Yedda, a lightweight but efficient and comprehensive open-source tool for text span annotation. Yedda provides a systematic solution for text span annotation, ranging from collaborative user annotation to administrator evaluation and analysis. It overcomes the low efficiency of traditional text annotation tools by annotating entities through both command line and shortcut keys, which are configurable with custom labels. Yedda also gives intelligent recommendations by learning the up-to-date annotated text. An administrator client is developed to evaluate annotation quality of multiple annotators and generate detailed comparison report for each annotator pair. Experiments show that the proposed system can reduce the annotation time by half compared with existing annotation tools. And the annotation time can be further compressed by 16.47% through intelligent recommendation.

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NCRF++: An Open-source Neural Sequence Labeling Toolkit
Jie Yang | Yue Zhang
Proceedings of ACL 2018, System Demonstrations

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch http://pytorch.org/, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods.

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Design Challenges and Misconceptions in Neural Sequence Labeling
Jie Yang | Shuailong Liang | Yue Zhang
Proceedings of the 27th International Conference on Computational Linguistics

We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.

2017

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Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring
Fei Dong | Yue Zhang | Jie Yang
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts of the essay can contribute differently for scoring, which is not captured by existing models. We address these issues by building a hierarchical sentence-document model to represent essays, using the attention mechanism to automatically decide the relative weights of words and sentences. Results show that our model outperforms the previous state-of-the-art methods, demonstrating the effectiveness of the attention mechanism.

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Neural Reranking for Named Entity Recognition
Jie Yang | Yue Zhang | Fei Dong
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

We propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as Barack Obama, into their entity types, such as PER. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, “PER was born in LOC” can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.

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Neural Word Segmentation with Rich Pretraining
Jie Yang | Yue Zhang | Fei Dong
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmentation research has exploited richer sources of external information, such as punctuation, automatic segmentation and POS. We investigate the effectiveness of a range of external training sources for neural word segmentation by building a modular segmentation model, pretraining the most important submodule using rich external sources. Results show that such pretraining significantly improves the model, leading to accuracies competitive to the best methods on six benchmarks.

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Universal Dependencies Parsing for Colloquial Singaporean English
Hongmin Wang | Yue Zhang | GuangYong Leonard Chan | Jie Yang | Hai Leong Chieu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.

2016

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Unsupervised Multi-Author Document Decomposition Based on Hidden Markov Model
Khaled Aldebei | Xiangjian He | Wenjing Jia | Jie Yang
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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LibN3L:A Lightweight Package for Neural NLP
Meishan Zhang | Jie Yang | Zhiyang Teng | Yue Zhang
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a light-weight machine learning tool for NLP research. The package supports operations on both discrete and dense vectors, facilitating implementation of linear models as well as neural models. It provides several basic layers which mainly aims for single-layer linear and non-linear transformations. By using these layers, we can conveniently implement linear models and simple neural models. Besides, this package also integrates several complex layers by composing those basic layers, such as RNN, Attention Pooling, LSTM and gated RNN. Those complex layers can be used to implement deep neural models directly.

2015

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Unsupervised Decomposition of a Multi-Author Document Based on Naive-Bayesian Model
Khaled Aldebei | Xiangjian He | Jie Yang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2006

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Competitive Evaluation of Commercially Available Speech Recognizers in Multiple Languages
Susanne Burger | Zachary A. Sloane | Jie Yang
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

2001

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Towards Automatic Sign Translation
Jie Yang | Jiang Gao | Ying Zhang | Alex Waibel
Proceedings of the First International Conference on Human Language Technology Research