Jiawei Han


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Facet-Aware Evaluation for Extractive Summarization
Yuning Mao | Liyuan Liu | Qi Zhu | Xiang Ren | Jiawei Han
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Commonly adopted metrics for extractive summarization focus on lexical overlap at the token level. In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries. Specifically, we treat each sentence in the reference summary as a facet, identify the sentences in the document that express the semantics of each facet as support sentences of the facet, and automatically evaluate extractive summarization methods by comparing the indices of extracted sentences and support sentences of all the facets in the reference summary. To facilitate this new evaluation setup, we construct an extractive version of the CNN/Daily Mail dataset and perform a thorough quantitative investigation, through which we demonstrate that facet-aware evaluation manifests better correlation with human judgment than ROUGE, enables fine-grained evaluation as well as comparative analysis, and reveals valuable insights of state-of-the-art summarization methods. Data can be found at https://github.com/morningmoni/FAR.

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Empower Entity Set Expansion via Language Model Probing
Yunyi Zhang | Jiaming Shen | Jingbo Shang | Jiawei Han
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Entity set expansion, aiming at expanding a small seed entity set with new entities belonging to the same semantic class, is a critical task that benefits many downstream NLP and IR applications, such as question answering, query understanding, and taxonomy construction. Existing set expansion methods bootstrap the seed entity set by adaptively selecting context features and extracting new entities. A key challenge for entity set expansion is to avoid selecting ambiguous context features which will shift the class semantics and lead to accumulative errors in later iterations. In this study, we propose a novel iterative set expansion framework that leverages automatically generated class names to address the semantic drift issue. In each iteration, we select one positive and several negative class names by probing a pre-trained language model, and further score each candidate entity based on selected class names. Experiments on two datasets show that our framework generates high-quality class names and outperforms previous state-of-the-art methods significantly.

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EVIDENCEMINER: Textual Evidence Discovery for Life Sciences
Xuan Wang | Yingjun Guan | Weili Liu | Aabhas Chauhan | Enyi Jiang | Qi Li | David Liem | Dibakar Sigdel | John Caufield | Peipei Ping | Jiawei Han
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements. Some of these statements may serve as textual evidence that is key to tasks such as hypothesis generation and new finding validation. We present EVIDENCEMINER, a web-based system that lets users query a natural language statement and automatically retrieves textual evidence from a background corpora for life sciences. EVIDENCEMINER is constructed in a completely automated way without any human effort for training data annotation. It is supported by novel data-driven methods for distantly supervised named entity recognition and open information extraction. The entities and patterns are pre-computed and indexed offline to support fast online evidence retrieval. The annotation results are also highlighted in the original document for better visualization. EVIDENCEMINER also includes analytic functionalities such as the most frequent entity and relation summarization. EVIDENCEMINER can help scientists uncover important research issues, leading to more effective research and more in-depth quantitative analysis. The system of EVIDENCEMINER is available at https://evidenceminer.firebaseapp.com/.

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Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding
Jiaming Shen | Heng Ji | Jiawei Han
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Linguistic steganography studies how to hide secret messages in natural language cover texts. Traditional methods aim to transform a secret message into an innocent text via lexical substitution or syntactical modification. Recently, advances in neural language models (LMs) enable us to directly generate cover text conditioned on the secret message. In this study, we present a new linguistic steganography method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model. We formally analyze the statistical imperceptibility of this method and empirically show it outperforms the previous state-of-the-art methods on four datasets by 15.3% and 38.9% in terms of bits/word and KL metrics, respectively. Finally, human evaluations show that 51% of generated cover texts can indeed fool eavesdroppers.

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Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning
Yuning Mao | Yanru Qu | Yiqing Xie | Xiang Ren | Jiawei Han
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

While neural sequence learning methods have made significant progress in single-document summarization (SDS), they produce unsatisfactory results on multi-document summarization (MDS). We observe two major challenges when adapting SDS advances to MDS: (1) MDS involves larger search space and yet more limited training data, setting obstacles for neural methods to learn adequate representations; (2) MDS needs to resolve higher information redundancy among the source documents, which SDS methods are less effective to handle. To close the gap, we present RL-MMR, Maximal Margin Relevance-guided Reinforcement Learning for MDS, which unifies advanced neural SDS methods and statistical measures used in classical MDS. RL-MMR casts MMR guidance on fewer promising candidates, which restrains the search space and thus leads to better representation learning. Additionally, the explicit redundancy measure in MMR helps the neural representation of the summary to better capture redundancy. Extensive experiments demonstrate that RL-MMR achieves state-of-the-art performance on benchmark MDS datasets. In particular, we show the benefits of incorporating MMR into end-to-end learning when adapting SDS to MDS in terms of both learning effectiveness and efficiency.

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Understanding the Difficulty of Training Transformers
Liyuan Liu | Xiaodong Liu | Jianfeng Gao | Weizhu Chen | Jiawei Han
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Transformers have proved effective in many NLP tasks. However, their training requires non-trivial efforts regarding carefully designing cutting-edge optimizers and learning rate schedulers (e.g., conventional SGD fails to train Transformers effectively). Our objective here is to understand __what complicates Transformer training__ from both empirical and theoretical perspectives. Our analysis reveals that unbalanced gradients are not the root cause of the instability of training. Instead, we identify an amplification effect that influences training substantially—for each layer in a multi-layer Transformer model, heavy dependency on its residual branch makes training unstable, since it amplifies small parameter perturbations (e.g., parameter updates) and results in significant disturbances in the model output. Yet we observe that a light dependency limits the model potential and leads to inferior trained models. Inspired by our analysis, we propose Admin (Adaptive model initialization) to stabilize the early stage’s training and unleash its full potential in the late stage. Extensive experiments show that Admin is more stable, converges faster, and leads to better performance

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Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding
Jiaxin Huang | Yu Meng | Fang Guo | Heng Ji | Jiawei Han
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner. It has in general two sub-tasks: (i) extracting aspects from each review, and (ii) classifying aspect-based reviews by sentiment polarity. In this paper, we propose a weakly-supervised approach for aspect-based sentiment analysis, which uses only a few keywords describing each aspect/sentiment without using any labeled examples. Existing methods are either designed only for one of the sub-tasks, or are based on topic models that may contain overlapping concepts. We propose to first learn <sentiment, aspect> joint topic embeddings in the word embedding space by imposing regularizations to encourage topic distinctiveness, and then use neural models to generalize the word-level discriminative information by pre-training the classifiers with embedding-based predictions and self-training them on unlabeled data. Our comprehensive performance analysis shows that our method generates quality joint topics and outperforms the baselines significantly (7.4% and 5.1% F1-score gain on average for aspect and sentiment classification respectively) on benchmark datasets.

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SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery
Jiaming Shen | Wenda Qiu | Jingbo Shang | Michelle Vanni | Xiang Ren | Jiawei Han
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Entity set expansion and synonym discovery are two critical NLP tasks. Previous studies accomplish them separately, without exploring their interdependencies. In this work, we hypothesize that these two tasks are tightly coupled because two synonymous entities tend to have a similar likelihood of belonging to various semantic classes. This motivates us to design SynSetExpan, a novel framework that enables two tasks to mutually enhance each other. SynSetExpan uses a synonym discovery model to include popular entities’ infrequent synonyms into the set, which boosts the set expansion recall. Meanwhile, the set expansion model, being able to determine whether an entity belongs to a semantic class, can generate pseudo training data to fine-tune the synonym discovery model towards better accuracy. To facilitate the research on studying the interplays of these two tasks, we create the first large-scale Synonym-Enhanced Set Expansion (SE2) dataset via crowdsourcing. Extensive experiments on the SE2 dataset and previous benchmarks demonstrate the effectiveness of SynSetExpan for both entity set expansion and synonym discovery tasks.

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Text Classification Using Label Names Only: A Language Model Self-Training Approach
Yu Meng | Yunyi Zhang | Jiaxin Huang | Chenyan Xiong | Heng Ji | Chao Zhang | Jiawei Han
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Current text classification methods typically require a good number of human-labeled documents as training data, which can be costly and difficult to obtain in real applications. Humans can perform classification without seeing any labeled examples but only based on a small set of words describing the categories to be classified. In this paper, we explore the potential of only using the label name of each class to train classification models on unlabeled data, without using any labeled documents. We use pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification. Our method (1) associates semantically related words with the label names, (2) finds category-indicative words and trains the model to predict their implied categories, and (3) generalizes the model via self-training. We show that our model achieves around 90% accuracy on four benchmark datasets including topic and sentiment classification without using any labeled documents but learning from unlabeled data supervised by at most 3 words (1 in most cases) per class as the label name.


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Hierarchical Text Classification with Reinforced Label Assignment
Yuning Mao | Jingjing Tian | Jiawei Han | Xiang Ren
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 existing hierarchical text classification (HTC) methods attempt to capture label hierarchies for model training, they either make local decisions regarding each label or completely ignore the hierarchy information during inference. To solve the mismatch between training and inference as well as modeling label dependencies in a more principled way, we formulate HTC as a Markov decision process and propose to learn a Label Assignment Policy via deep reinforcement learning to determine where to place an object and when to stop the assignment process. The proposed method, HiLAP, explores the hierarchy during both training and inference time in a consistent manner and makes inter-dependent decisions. As a general framework, HiLAP can incorporate different neural encoders as base models for end-to-end training. Experiments on five public datasets and four base models show that HiLAP yields an average improvement of 33.4% in Macro-F1 over flat classifiers and outperforms state-of-the-art HTC methods by a large margin. Data and code can be found at https://github.com/morningmoni/HiLAP.

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CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
Zihan Wang | Jingbo Shang | Liyuan Liu | Lihao Lu | Jiacheng Liu | Jiawei Han
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Everyone makes mistakes. So do human annotators when curating labels for named entity recognition (NER). Such label mistakes might hurt model training and interfere model comparison. In this study, we dive deep into one of the widely-adopted NER benchmark datasets, CoNLL03 NER. We are able to identify label mistakes in about 5.38% test sentences, which is a significant ratio considering that the state-of-the-art test F1 score is already around 93%. Therefore, we manually correct these label mistakes and form a cleaner test set. Our re-evaluation of popular models on this corrected test set leads to more accurate assessments, compared to those on the original test set. More importantly, we propose a simple yet effective framework, CrossWeigh, to handle label mistakes during NER model training. Specifically, it partitions the training data into several folds and train independent NER models to identify potential mistakes in each fold. Then it adjusts the weights of training data accordingly to train the final NER model. Extensive experiments demonstrate significant improvements of plugging various NER models into our proposed framework on three datasets. All implementations and corrected test set are available at our Github repo https://github.com/ZihanWangKi/CrossWeigh.

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Reliability-aware Dynamic Feature Composition for Name Tagging
Ying Lin | Liyuan Liu | Heng Ji | Dong Yu | Jiawei Han
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Word embeddings are widely used on a variety of tasks and can substantially improve the performance. However, their quality is not consistent throughout the vocabulary due to the long-tail distribution of word frequency. Without sufficient contexts, rare word embeddings are usually less reliable than those of common words. However, current models typically trust all word embeddings equally regardless of their reliability and thus may introduce noise and hurt the performance. Since names often contain rare and uncommon words, this problem is particularly critical for name tagging. In this paper, we propose a novel reliability-aware name tagging model to tackle this issue. We design a set of word frequency-based reliability signals to indicate the quality of each word embedding. Guided by the reliability signals, the model is able to dynamically select and compose features such as word embedding and character-level representation using gating mechanisms. For example, if an input word is rare, the model relies less on its word embedding and assigns higher weights to its character and contextual features. Experiments on OntoNotes 5.0 show that our model outperforms the baseline model by 2.7% absolute gain in F-score. In cross-genre experiments on five genres in OntoNotes, our model improves the performance for most genre pairs and obtains up to 5% absolute F-score gain.

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Arabic Named Entity Recognition: What Works and What’s Next
Liyuan Liu | Jingbo Shang | Jiawei Han
Proceedings of the Fourth Arabic Natural Language Processing Workshop

This paper presents the winning solution to the Arabic Named Entity Recognition challenge run by Topcoder.com. The proposed model integrates various tailored techniques together, including representation learning, feature engineering, sequence labeling, and ensemble learning. The final model achieves a test F_1 score of 75.82% on the AQMAR dataset and outperforms baselines by a large margin. Detailed analyses are conducted to reveal both its strengths and limitations. Specifically, we observe that (1) representation learning modules can significantly boost the performance but requires a proper pre-processing and (2) the resulting embedding can be further enhanced with feature engineering due to the limited size of the training data. All implementations and pre-trained models are made public.

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Constrained Sequence-to-sequence Semitic Root Extraction for Enriching Word Embeddings
Ahmed El-Kishky | Xingyu Fu | Aseel Addawood | Nahil Sobh | Clare Voss | Jiawei Han
Proceedings of the Fourth Arabic Natural Language Processing Workshop

In this paper, we tackle the problem of “root extraction” from words in the Semitic language family. A challenge in applying natural language processing techniques to these languages is the data sparsity problem that arises from their rich internal morphology, where the substructure is inherently non-concatenative and morphemes are interdigitated in word formation. While previous automated methods have relied on human-curated rules or multiclass classification, they have not fully leveraged the various combinations of regular, sequential concatenative morphology within the words and the internal interleaving within templatic stems of roots and patterns. To address this, we propose a constrained sequence-to-sequence root extraction method. Experimental results show our constrained model outperforms a variety of methods at root extraction. Furthermore, by enriching word embeddings with resulting decompositions, we show improved results on word analogy, word similarity, and language modeling tasks.

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Biomedical Event Extraction based on Knowledge-driven Tree-LSTM
Diya Li | Lifu Huang | Heng Ji | Jiawei Han
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)

Event extraction for the biomedical domain is more challenging than that in the general news domain since it requires broader acquisition of domain-specific knowledge and deeper understanding of complex contexts. To better encode contextual information and external background knowledge, we propose a novel knowledge base (KB)-driven tree-structured long short-term memory networks (Tree-LSTM) framework, incorporating two new types of features: (1) dependency structures to capture wide contexts; (2) entity properties (types and category descriptions) from external ontologies via entity linking. We evaluate our approach on the BioNLP shared task with Genia dataset and achieve a new state-of-the-art result. In addition, both quantitative and qualitative studies demonstrate the advancement of the Tree-LSTM and the external knowledge representation for biomedical event extraction.


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Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling
Liyuan Liu | Xiang Ren | Jingbo Shang | Xiaotao Gu | Jian Peng | Jiawei Han
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Many efforts have been made to facilitate natural language processing tasks with pre-trained language models (LMs), and brought significant improvements to various applications. To fully leverage the nearly unlimited corpora and capture linguistic information of multifarious levels, large-size LMs are required; but for a specific task, only parts of these information are useful. Such large-sized LMs, even in the inference stage, may cause heavy computation workloads, making them too time-consuming for large-scale applications. Here we propose to compress bulky LMs while preserving useful information with regard to a specific task. As different layers of the model keep different information, we develop a layer selection method for model pruning using sparsity-inducing regularization. By introducing the dense connectivity, we can detach any layer without affecting others, and stretch shallow and wide LMs to be deep and narrow. In model training, LMs are learned with layer-wise dropouts for better robustness. Experiments on two benchmark datasets demonstrate the effectiveness of our method.

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Learning Named Entity Tagger using Domain-Specific Dictionary
Jingbo Shang | Liyuan Liu | Xiaotao Gu | Xiang Ren | Teng Ren | Jiawei Han
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing human annotations with distant supervision (in conjunction with external dictionaries), but the generated noisy labels pose significant challenges on learning effective neural models. Here we propose two neural models to suit noisy distant supervision from the dictionary. First, under the traditional sequence labeling framework, we propose a revised fuzzy CRF layer to handle tokens with multiple possible labels. After identifying the nature of noisy labels in distant supervision, we go beyond the traditional framework and propose a novel, more effective neural model AutoNER with a new Tie or Break scheme. In addition, we discuss how to refine distant supervision for better NER performance. Extensive experiments on three benchmark datasets demonstrate that AutoNER achieves the best performance when only using dictionaries with no additional human effort, and delivers competitive results with state-of-the-art supervised benchmarks.

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End-to-End Reinforcement Learning for Automatic Taxonomy Induction
Yuning Mao | Xiang Ren | Jiaming Shen | Xiaotao Gu | Jiawei Han
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e.,, detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine which term to select and where to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6% on ancestor F1.

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Entropy-Based Subword Mining with an Application to Word Embeddings
Ahmed El-Kishky | Frank Xu | Aston Zhang | Stephen Macke | Jiawei Han
Proceedings of the Second Workshop on Subword/Character LEvel Models

Recent literature has shown a wide variety of benefits to mapping traditional one-hot representations of words and phrases to lower-dimensional real-valued vectors known as word embeddings. Traditionally, most word embedding algorithms treat each word as the finest meaningful semantic granularity and perform embedding by learning distinct embedding vectors for each word. Contrary to this line of thought, technical domains such as scientific and medical literature compose words from subword structures such as prefixes, suffixes, and root-words as well as compound words. Treating individual words as the finest-granularity unit discards meaningful shared semantic structure between words sharing substructures. This not only leads to poor embeddings for text corpora that have long-tail distributions, but also heuristic methods for handling out-of-vocabulary words. In this paper we propose SubwordMine, an entropy-based subword mining algorithm that is fast, unsupervised, and fully data-driven. We show that this allows for great cross-domain performance in identifying semantically meaningful subwords. We then investigate utilizing the mined subwords within the FastText embedding model and compare performance of the learned representations in a downstream language modeling task.


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Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences
Xiang Ren | Jiaming Shen | Meng Qu | Xuan Wang | Zeqiu Wu | Qi Zhu | Meng Jiang | Fangbo Tao | Saurabh Sinha | David Liem | Peipei Ping | Richard Weinshilboum | Jiawei Han
Proceedings of ACL 2017, System Demonstrations

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Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
Liyuan Liu | Xiang Ren | Qi Zhu | Shi Zhi | Huan Gui | Heng Ji | Jiawei Han
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.

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Identifying Semantically Deviating Outlier Documents
Honglei Zhuang | Chi Wang | Fangbo Tao | Lance Kaplan | Jiawei Han
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

A document outlier is a document that substantially deviates in semantics from the majority ones in a corpus. Automatic identification of document outliers can be valuable in many applications, such as screening health records for medical mistakes. In this paper, we study the problem of mining semantically deviating document outliers in a given corpus. We develop a generative model to identify frequent and characteristic semantic regions in the word embedding space to represent the given corpus, and a robust outlierness measure which is resistant to noisy content in documents. Experiments conducted on two real-world textual data sets show that our method can achieve an up to 135% improvement over baselines in terms of recall at top-1% of the outlier ranking.


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AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding
Xiang Ren | Wenqi He | Meng Qu | Lifu Huang | Heng Ji | Jiawei Han
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Cross-media Event Extraction and Recommendation
Di Lu | Clare Voss | Fangbo Tao | Xiang Ren | Rachel Guan | Rostyslav Korolov | Tongtao Zhang | Dongang Wang | Hongzhi Li | Taylor Cassidy | Heng Ji | Shih-fu Chang | Jiawei Han | William Wallace | James Hendler | Mei Si | Lance Kaplan
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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FastHybrid: A Hybrid Model for Efficient Answer Selection
Lidan Wang | Ming Tan | Jiawei Han
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Answer selection is a core component in any question-answering systems. It aims to select correct answer sentences for a given question from a pool of candidate sentences. In recent years, many deep learning methods have been proposed and shown excellent results for this task. However, these methods typically require extensive parameter (and hyper-parameter) tuning, which give rise to efficiency issues for large-scale datasets, and potentially make them less portable across new datasets and domains (as re-tuning is usually required). In this paper, we propose an extremely efficient hybrid model (FastHybrid) that tackles the problem from both an accuracy and scalability point of view. FastHybrid is a light-weight model that requires little tuning and adaptation across different domains. It combines a fast deep model (which will be introduced in the method section) with an initial information retrieval model to effectively and efficiently handle answer selection. We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets. Experimental results show that although the hybrid uses no training data, its accuracy is often on-par with supervised deep learning techniques, while significantly reducing training and tuning costs across different domains.

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Liberal Event Extraction and Event Schema Induction
Lifu Huang | Taylor Cassidy | Xiaocheng Feng | Heng Ji | Clare R. Voss | Jiawei Han | Avirup Sil
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Context-aware Entity Morph Decoding
Boliang Zhang | Hongzhao Huang | Xiaoman Pan | Sujian Li | Chin-Yew Lin | Heng Ji | Kevin Knight | Zhen Wen | Yizhou Sun | Jiawei Han | Bulent Yener
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Successful Data Mining Methods for NLP
Jiawei Han | Heng Ji | Yizhou Sun
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts


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Be Appropriate and Funny: Automatic Entity Morph Encoding
Boliang Zhang | Hongzhao Huang | Xiaoman Pan | Heng Ji | Kevin Knight | Zhen Wen | Yizhou Sun | Jiawei Han | Bulent Yener
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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The Wisdom of Minority: Unsupervised Slot Filling Validation based on Multi-dimensional Truth-Finding
Dian Yu | Hongzhao Huang | Taylor Cassidy | Heng Ji | Chi Wang | Shi Zhi | Jiawei Han | Clare Voss | Malik Magdon-Ismail
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers


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Resolving Entity Morphs in Censored Data
Hongzhao Huang | Zhen Wen | Dian Yu | Heng Ji | Yizhou Sun | Jiawei Han | He Li
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Tweet Ranking Based on Heterogeneous Networks
Hongzhao Huang | Arkaitz Zubiaga | Heng Ji | Hongbo Deng | Dong Wang | Hieu Le | Tarek Abdelzaher | Jiawei Han | Alice Leung | John Hancock | Clare Voss
Proceedings of COLING 2012


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Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions
Kavita Ganesan | ChengXiang Zhai | Jiawei Han
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)