Tat-Seng Chua

Also published as: Tat Seng Chua, Tat-seng Chua


2021

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Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Zhenghao Liu | Xiaoyuan Yi | Maosong Sun | Liner Yang | Tat-Seng Chua
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious corrections or fail to detect lots of errors. The quality estimation model is necessary to ensure learners get accurate GEC results and avoid misleading from poorly corrected sentences. Well-trained GEC models can generate several high-quality hypotheses through decoding, such as beam search, which provide valuable GEC evidence and can be used to evaluate GEC quality. However, existing models neglect the possible GEC evidence from different hypotheses. This paper presents the Neural Verification Network (VERNet) for GEC quality estimation with multiple hypotheses. VERNet establishes interactions among hypotheses with a reasoning graph and conducts two kinds of attention mechanisms to propagate GEC evidence to verify the quality of generated hypotheses. Our experiments on four GEC datasets show that VERNet achieves state-of-the-art grammatical error detection performance, achieves the best quality estimation results, and significantly improves GEC performance by reranking hypotheses. All data and source codes are available at https://github.com/thunlp/VERNet.

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TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance
Fengbin Zhu | Wenqiang Lei | Youcheng Huang | Chao Wang | Shuo Zhang | Jiancheng Lv | Fuli Feng | Tat-Seng Chua
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in existing research. In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions. We further propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text. It adopts sequence tagging to extract relevant cells from the table along with relevant spans from the text to infer their semantics, and then applies symbolic reasoning over them with a set of aggregation operators to arrive at the final answer. TAGOP achieves 58.0% inF1, which is an 11.1% absolute increase over the previous best baseline model, according to our experiments on TAT-QA. But this result still lags far behind performance of expert human, i.e.90.8% in F1. It is demonstrated that our TAT-QA is very challenging and can serve as a benchmark for training and testing powerful QA models that address hybrid form data.

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How Knowledge Graph and Attention Help? A Qualitative Analysis into Bag-level Relation Extraction
Zikun Hu | Yixin Cao | Lifu Huang | Tat-Seng Chua
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Knowledge Graph (KG) and attention mechanism have been demonstrated effective in introducing and selecting useful information for weakly supervised methods. However, only qualitative analysis and ablation study are provided as evidence. In this paper, we contribute a dataset and propose a paradigm to quantitatively evaluate the effect of attention and KG on bag-level relation extraction (RE). We find that (1) higher attention accuracy may lead to worse performance as it may harm the model’s ability to extract entity mention features; (2) the performance of attention is largely influenced by various noise distribution patterns, which is closely related to real-world datasets; (3) KG-enhanced attention indeed improves RE performance, while not through enhanced attention but by incorporating entity prior; and (4) attention mechanism may exacerbate the issue of insufficient training data. Based on these findings, we show that a straightforward variant of RE model can achieve significant improvements (6% AUC on average) on two real-world datasets as compared with three state-of-the-art baselines. Our codes and datasets are available at https://github.com/zig-kwin-hu/how-KG-ATT-help.

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Empowering Language Understanding with Counterfactual Reasoning
Fuli Feng | Jizhi Zhang | Xiangnan He | Hanwang Zhang | Tat-Seng Chua
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen
Yixin Cao | Ruihao Shui | Liangming Pan | Min-Yen Kan | Zhiyuan Liu | Tat-Seng Chua
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at https://srhthu.github.io/expertise-style-transfer/.

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Semantic Graphs for Generating Deep Questions
Liangming Pan | Yuxi Xie | Yansong Feng | Tat-Seng Chua | Min-Yen Kan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information about the input passage. In order to capture the global structure of the document and facilitate reasoning, we propose a novel framework that first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN). Afterward, we fuse the document-level and graph-level representations to perform joint training of content selection and question decoding. On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance. The code is publicly available at https://github.com/WING-NUS/SG-Deep-Question-Generation.

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Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
Zhiyuan Liu | Yixin Cao | Liangming Pan | Juanzi Li | Zhiyuan Liu | Tat-Seng Chua
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performance by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective evaluation, we propose a hard experimental setting where we select equivalent entity pairs with very different names as the test set. Under both the regular and hard settings, our method achieves significant improvements (5.10% on average Hits@1 in DBP15k) over 12 baselines in cross-lingual and monolingual datasets. Ablation studies on different subgraphs and a case study about attribute types further demonstrate the effectiveness of our method. Source code and data can be found at https://github.com/thunlp/explore-and-evaluate.

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Re-examining the Role of Schema Linking in Text-to-SQL
Wenqiang Lei | Weixin Wang | Zhixin Ma | Tian Gan | Wei Lu | Min-Yen Kan | Tat-Seng Chua
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In existing sophisticated text-to-SQL models, schema linking is often considered as a simple, minor component, belying its importance. By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking. We also build a simple BERT-based baseline, called Schema-Linking SQL (SLSQL) to perform a data-driven study. We find when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity. Many remaining errors are attributable to corpus noise. This suggests schema linking is the crux for the current text-to-SQL task. Our analytic studies provide insights on the characteristics of schema linking for future developments of text-to-SQL tasks.

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Learning Goal-oriented Dialogue Policy with opposite Agent Awareness
Zheng Zhang | Lizi Liao | Xiaoyan Zhu | Tat-Seng Chua | Zitao Liu | Yan Huang | Minlie Huang
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treats the opposite agent policy as part of the environment. While in real-world scenarios, the behavior of an opposite agent often exhibits certain patterns or underlies hidden policies, which can be inferred and utilized by the target agent to facilitate its own decision making. This strategy is common in human mental simulation by first imaging a specific action and the probable results before really acting it. We therefore propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agent’s policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy. We evaluate our model on both cooperative and competitive dialogue tasks, showing superior performance over state-of-the-art baselines.

2019

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Low-Resource Name Tagging Learned with Weakly Labeled Data
Yixin Cao | Zikun Hu | Tat-seng Chua | Zhiyuan Liu | Heng Ji
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotations unexplored that extensively exist on the web. In this paper, we propose a novel neural model for name tagging solely based on weakly labeled (WL) data, so that it can be applied in any low-resource settings. To take the best advantage of all WL sentences, we split them into high-quality and noisy portions for two modules, respectively: (1) a classification module focusing on the large portion of noisy data can efficiently and robustly pretrain the tag classifier by capturing textual context semantics; and (2) a costly sequence labeling module focusing on high-quality data utilizes Partial-CRFs with non-entity sampling to achieve global optimum. Two modules are combined via shared parameters. Extensive experiments involving five low-resource languages and fine-grained food domain demonstrate our superior performance (6% and 7.8% F1 gains on average) as well as efficiency.

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Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach
Wenqiang Lei | Weiwen Xu | Ai Ti Aw | Yuanxin Xiang | Tat Seng Chua
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs. We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task. We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.

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Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model
Chengjiang Li | Yixin Cao | Lei Hou | Jiaxin Shi | Juanzi Li | Tat-Seng Chua
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages, which may benefit many knowledge-driven applications. It is challenging due to the heterogeneity of KGs and limited seed alignments. In this paper, we propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG). It can make better use of seed alignments to propagate over the entire graphs with KG-based constraints. Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities. As for the cross-graph model, we extend Graph Attention Network (GAT) with projection constraint to robustly encode graphs, and two KGs share the same GAT to transfer structural knowledge as well as to ignore unimportant neighbors for alignment via attention mechanism. Results on publicly available datasets as well as further analysis demonstrate the effectiveness of KECG. Our codes can be found in https: //github.com/THU-KEG/KECG.

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Graph Neural Networks with Generated Parameters for Relation Extraction
Hao Zhu | Yankai Lin | Zhiyuan Liu | Jie Fu | Tat-Seng Chua | Maosong Sun
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we propose a novel graph neural network with generated parameters (GP-GNNs). The parameters in the propagation module, i.e. the transition matrices used in message passing procedure, are produced by a generator taking natural language sentences as inputs. We verify GP-GNNs in relation extraction from text, both on bag- and instance-settings. Experimental results on a human-annotated dataset and two distantly supervised datasets show that multi-hop reasoning mechanism yields significant improvements. We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.

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Multi-Channel Graph Neural Network for Entity Alignment
Yixin Cao | Zhiyuan Liu | Chengjiang Li | Zhiyuan Liu | Juanzi Li | Tat-Seng Chua
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make better use of seed alignments. Extensive experiments on five publicly available datasets demonstrate our superior performance (5% Hits@1 up on average). Source code and data used in the experiments can be accessed at https://github.com/thunlp/MuGNN .

2018

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Temporally Grounding Natural Sentence in Video
Jingyuan Chen | Xinpeng Chen | Lin Ma | Zequn Jie | Tat-Seng Chua
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN) is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frame-by-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the state-of-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.

2016

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Generative Topic Embedding: a Continuous Representation of Documents
Shaohua Li | Tat-Seng Chua | Jun Zhu | Chunyan Miao
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Answering Opinion Questions on Products by Exploiting Hierarchical Organization of Consumer Reviews
Jianxing Yu | Zheng-Jun Zha | Tat-Seng Chua
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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SSHLDA: A Semi-Supervised Hierarchical Topic Model
Xian-Ling Mao | Zhao-Yan Ming | Tat-Seng Chua | Si Li | Hongfei Yan | Xiaoming Li
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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A Semi-Supervised Bayesian Network Model for Microblog Topic Classification
Yan Chen | Zhoujun Li | Liqiang Nie | Xia Hu | Xiangyu Wang | Tat-Seng Chua | Xiaoming Zhang
Proceedings of COLING 2012

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The Use of Dependency Relation Graph to Enhance the Term Weighting in Question Retrieval
Weinan Zhang | Zhaoyan Ming | Yu Zhang | Liqiang Nie | Ting Liu | Tat-Seng Chua
Proceedings of COLING 2012

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Community Answer Summarization for Multi-Sentence Question with Group L1 Regularization
Wen Chan | Xiangdong Zhou | Wei Wang | Tat-Seng Chua
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2011

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Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews
Jianxing Yu | Zheng-Jun Zha | Meng Wang | Kai Wang | Tat-Seng Chua
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Aspect Ranking: Identifying Important Product Aspects from Online Consumer Reviews
Jianxing Yu | Zheng-Jun Zha | Meng Wang | Tat-Seng Chua
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Exploiting Salient Patterns for Question Detection and Question Retrieval in Community-based Question Answering
Kai Wang | Tat-Seng Chua
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Automatic Generation of Semantic Fields for Annotating Web Images
Gang Wang | Tat Seng Chua | Chong-Wah Ngo | Yong Cheng Wang
Coling 2010: Posters

2009

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Summarizing Definition from Wikipedia
Shiren Ye | Tat-Seng Chua | Jie Lu
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Query Segmentation Based on Eigenspace Similarity
Chao Zhang | Nan Sun | Xia Hu | Tingzhu Huang | Tat-Seng Chua
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2008

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Modeling Context in Scenario Template Creation
Long Qiu | Min-Yen Kan | Tat-Seng Chua
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2007

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A Multi-resolution Framework for Information Extraction from Free Text
Mstislav Maslennikov | Tat-Seng Chua
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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ARE: Instance Splitting Strategies for Dependency Relation-Based Information Extraction
Mstislav Maslennikov | Hai-Kiat Goh | Tat-Seng Chua
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Proceedings of the Workshop on Task-Focused Summarization and Question Answering
Tat-Seng Chua | Jade Goldstein | Simone Teufel | Lucy Vanderwende
Proceedings of the Workshop on Task-Focused Summarization and Question Answering

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Paraphrase Recognition via Dissimilarity Significance Classification
Long Qiu | Min-Yen Kan | Tat-Seng Chua
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2004

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A Public Reference Implementation of the RAP Anaphora Resolution Algorithm
Long Qiu | Min-Yen Kan | Tat-Seng Chua
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Cascading Use of Soft and Hard Matching Pattern Rules for Weakly Supervised Information Extraction
Jing Xiao | Tat-Seng Chua | Hang Cui
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Web-based List Question Answering
Hui Yang | Tat-Seng Chua
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Extracting Key Semantic Terms from Chinese Speech Query for Web Searches
Gang Wang | Tat-Seng Chua | Yong-Cheng Wang
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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QUALIFIER: Question Answering by Lexical Fabric and External Resources
Hui Yang | Tat-Seng Chua
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Extracting Pronunciation-translated Names from Chinese Texts using Bootstrapping Approach
Jing Xiao | Jimin Liu | Tat-Seng Chua
COLING-02: The First SIGHAN Workshop on Chinese Language Processing

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An Agent-based Approach to Chinese Named Entity Recognition
Shiren Ye | Tat-Seng Chua | Jimin Liu
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Building Semantic Perceptron Net for Topic Spotting
Jimin Liu | Tat-Seng Chua
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics