Jian Xu


2022

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CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang | Mosha Chen | Zhen Bi | Xiaozhuan Liang | Lei Li | Xin Shang | Kangping Yin | Chuanqi Tan | Jian Xu | Fei Huang | Luo Si | Yuan Ni | Guotong Xie | Zhifang Sui | Baobao Chang | Hui Zong | Zheng Yuan | Linfeng Li | Jun Yan | Hongying Zan | Kunli Zhang | Buzhou Tang | Qingcai Chen
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.

2017

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Semantic Frame Labeling with Target-based Neural Model
Yukun Feng | Dong Yu | Jian Xu | Chunhua Liu
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

This paper explores the automatic learning of distributed representations of the target’s context for semantic frame labeling with target-based neural model. We constrain the whole sentence as the model’s input without feature extraction from the sentence. This is different from many previous works in which local feature extraction of the targets is widely used. This constraint makes the task harder, especially with long sentences, but also makes our model easily applicable to a range of resources and other similar tasks. We evaluate our model on several resources and get the state-of-the-art result on subtask 2 of SemEval 2015 task 15. Finally, we extend the task to word-sense disambiguation task and we also achieve a strong result in comparison to state-of-the-art work.

2016

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Recognizing Reference Spans and Classifying their Discourse Facets
Kun Lu | Jin Mao | Gang Li | Jian Xu
Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL)

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Improve Chinese Word Embeddings by Exploiting Internal Structure
Jian Xu | Jiawei Liu | Liangang Zhang | Zhengyu Li | Huanhuan Chen
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Two-View Label Propagation to Semi-supervised Reader Emotion Classification
Shoushan Li | Jian Xu | Dong Zhang | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled data is limited. In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm. Specifically, our approach depends on two word-document bipartite graphs to model the relationship among the samples in the two views respectively. Besides, the two bipartite graphs are integrated by linking each source text sample with its corresponding response text sample via a length-sensitive transition probability. In this way, our two-view label propagation approach to semi-supervised reader emotion classification largely alleviates the reliance on the strong sufficiency and independence assumptions of the two views, as required in co-training. Empirical evaluation demonstrates the effectiveness of our two-view label propagation approach to semi-supervised reader emotion classification.

2014

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Personal Attributes Extraction in Chinese Text Bakeoff in CLP 2014: Overview
Ruifeng Xu | Shuai Wang | Feng Shi | Jian Xu
Proceedings of the Third CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Cross-lingual Opinion Analysis via Negative Transfer Detection
Lin Gui | Ruifeng Xu | Qin Lu | Jun Xu | Jian Xu | Bin Liu | Xiaolong Wang
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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PolyUCOMP-CORE_TYPED: Computing Semantic Textual Similarity using Overlapped Senses
Jian Xu | Qin Lu
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2012

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Explore Chinese Encyclopedic Knowledge to Disambiguate Person Names
Jie Liu | Ruifeng Xu | Qin Lu | Jian Xu
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

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PolyUCOMP: Combining Semantic Vectors with Skip bigrams for Semantic Textual Similarity
Jian Xu | Qin Lu | Zhengzhong Liu
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)