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EMNLP 2017 - Conference on Empirical methods in natural language processing, proceedings of the 2nd workshop on Structured Prediction

EMNLP 2017 - Conference on Empirical Methods in Natural Lang...

引用

2nd workshop on Structured Prediction for natural language processing, SPNLP 2017, held in conjunction with the Conference on Empirical methods in natural language processing, EMNLP 2017

ISBN: (纸本)9781945626937

The proceedings contain 8 papers. The topics discussed include: dependency parsing with dilated iterated graph CNNs;entity identification as multitasking;towards neural machine translation with latent tree attention;structured prediction via learning to search under bandit feedback;syntax aware LSTM model for semantic role labeling;spatial language understanding with multimodal graphs using declarative learning based programming;boosting information extraction systems with character-level neural networks and free noisy supervision;and piecewise latent variables for neural variational text processing.

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Heterogeneous graph Attention Networks for Semi-supervised Short Text Classification 9

Heterogeneous Graph Attention Networks for Semi-supervised S...

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Conference on Empirical methods in natural language processing / 9th International Joint Conference on natural language processing (EMNLP-IJCNLP)

作者： Hu, Linmei Yang, Tianchi Shi, Chuan Ji, Houye Li, Xiaoli Beijing Univ Posts & Telecommun Beijing Peoples R China Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781950737901

Short text classification has found rich and critical applications in news and tweet tagging to help users find relevant information. Due to lack of labeled training data in many practical use cases, there is a pressing need for studying semi-supervised short text classification. Most existing studies focus on long texts and achieve unsatisfactory performance on short texts due to the sparsity and limited labeled data. In this paper, we propose a novel heterogeneous graph neural network based method for semi-supervised short text classification, leveraging full advantage of few labeled data and large unlabeled data through information propagation along the graph. In particular, we first present a flexible HIN (heterogeneous information network) framework for modeling the short texts, which can integrate any type of additional information as well as capture their relations to address the semantic sparsity. Then, we propose Heterogeneous graph ATtention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. The attention mechanism can learn the importance of different neighboring nodes as well as the importance of different node (information) types to a current node. Extensive experimental results have demonstrated that our proposed model outperforms state-of-the-art methods across six benchmark datasets significantly.

关键词： Semantics

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Cause-effect relation learning

Cause-effect relation learning

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7th workshop on graph-based methods for natural language processing, Textgraphs 2012

作者： Kozareva, Zornitsa USC Information Sciences Institute 4676 Admiralty Way Marina del Rey CA United States

ISBN: (纸本)9781937284374

To be able to answer the question What causes tumors to shrink?, one would require a large cause-effect relation repository. Many efforts have been payed on is-a and part-of relation leaning, however few have focused on cause-effect learning. This paper describes an automated bootstrapping procedure which can learn and produce with minimal effort a cause-effect term repository. To filter out the erroneously extracted information, we incorporate graph-based methods. To evaluate the performance of the acquired cause-effect terms, we conduct three evaluations: (1) human-based, (2) comparison with existing knowledge bases and (3) application driven (SemEval-1 Task 4) in which the goal is to identify the relation between pairs of nominals. The results show that the extractions at rank 1500 are 89% accurate, they comprise 61% from the terms used in the SemEval-1 Task 4 dataset and can be used in the future to produce additional training examples for the same task. © 2012 The Association for Computational Linguistics.

关键词： graphic methods

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Evaluating text coherence based on semantic similarity graph 11

Evaluating text coherence based on semantic similarity graph

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11th workshop on graph-based methods for natural language processing, Textgraphs 2017, in conjunction with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Putra, Jan Wira Gotama Tokunaga, Takenobu School of Computing Tokyo Institute of Technology Tokyo Meguro Ôokayama 2-12-1 152-8550 Japan

ISBN: (纸本)9781945626609

Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of semantic vectors representing sentences. We provide three graph construction methods establishing an edge from a given vertex to a preceding adjacent vertex, to a single similar vertex, or to multiple similar vertices. We evaluated our methods in the document discrimination task and the insertion task by comparing our proposed methods to the supervised (Entity Grid) and unsupervised (Entity graph) baselines. In the document discrimination task, our method outperformed the unsupervised baseline but could not do the supervised baseline, while in the insertion task, our method outperformed both baselines. © 2017 Association for Computational Linguistics

关键词： Conveying

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Chinese named entity recognition with graph-based semi-supervised learning model 8

Chinese named entity recognition with graph-based semi-super...

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8th SIGHAN workshop on Chinese language processing, SIGHAN 2015

作者： Han, Aaron Li-Feng Zeng, Xiaodong Wong, Derek F. Chao, Lidia S. Institute for Logic Language and Computation University of Amsterdam Science Park 107 Amsterdam1098 Netherlands NLP2CT Laboratory Department of Computer and Information Science University of Macau Macau China

ISBN: (纸本)9781941643570

Named entity recognition (NER) plays an important role in the NLP literature. The traditional methods tend to employ large annotated corpus to achieve a high performance. Different with many semi-supervised learning models for NER task, in this paper, we employ the graph-based semi-supervised learning (GBSSL) method to utilize the freely available unlabeled data. The experiment shows that the unlabeled corpus can enhance the state-of-Theart conditional random field (CRF) learning model and has potential to improve the tagging accuracy even though the margin is a little weak and not satisfying in current experiments. © 2015 proceedings of the 8th SIGHAN workshop on Chinese language processing, SIGHAN 2015 - co-located with 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on natural language processing, ACL IJCNLP 2015. All rights reserved.

关键词： natural language processing systems

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Relation Specific Transformations for Open World Knowledge graph Completion 14

Relation Specific Transformations for Open World Knowledge G...

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14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Shah, Haseeb Villmow, Johannes Ulges, Adrian Department of Computer Science University of Alberta Canada DCSM Department RheinMain University of Applied Sciences Germany

ISBN: (纸本)9781952148422

We propose an open-world knowledge graph completion model that can be combined with common closed-world approaches (such as ComplEx) and enhance them to exploit text-based representations for entities unseen in training. Our model learns relation-specific transformation functions from text-based embedding space to graph-based embedding space, where the closed-world link prediction model can be applied. We demonstrate state-of-the-art results on common open-world benchmarks and show that our approach benefits from relation-specific transformation functions (RST), giving substantial improvements over a relation-agnostic approach. © COLING *** rights reserved.

关键词： Knowledge graph

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Incremental construction of millstream configurations using graph transformation 9

Incremental construction of millstream configurations using ...

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9th International workshop Finite State methods and natural language processing, FSMNLP 2011

作者： Bensch, Suna Drewes, Frank Jürgensen, Helmut van der Merwe, Brink Department of Computing Science Umeå University Sweden Department of Computer Science University of Western Ontario Canada Department of Computing Science Stellenbosch University South Africa

Millstream systems are a non-hierarchical model of natural language. We describe an incremental method for building Millstream configurations while reading a sentence. This method is based on a lexicon associating wor... 详细信息

关键词： Hierarchical systems

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Evaluating and optimizing the parameters of an unsupervised graph-based WSD algorithm 1

Evaluating and optimizing the parameters of an unsupervised ...

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1st workshop on graph-based Algorithms for natural language processing, Textgraphs 2006 at Human language Technologies

作者： Agirre, Eneko Martínez, David de Lacalle, Oier López Soroa, Aitor IXA NLP Group University of Basque Country Donostia Basque Contry Spain

Véronis (2004) has recently proposed an innovative unsupervised algorithm for word sense disambiguation based on small-world graphs called HyperLex. This paper explores two sides of the algorithm. first, we extend Véronis' work by optimizing the free parameters (on a set of words which is different to the target set). Second, given that the empirical comparison among unsupervised systems (and with respect to supervised systems) is seldom made, we used hand-tagged corpora to map the induced senses to a standard lexicon (WordNet) and a publicly available gold standard (Senseval 3 English Lexical Sample). Our results for nouns show that thanks to the optimization of parameters and the mapping method, HyperLex obtains results close to supervised systems using the same kind of bag-of-words features. Given the information loss inherent in any mapping step and the fact that the parameters were tuned for another set of words, these are very interesting results. © 2006 Association for Computational Linguistics

关键词： Mapping

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A document graph based query focused multi-document summarizer 2

A document graph based query focused multi-document summariz...

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2nd International workshop on Cross Lingual Information Access: Addressing the Information Need of Multilingual Societies, CLIA 2008 - held in conjunction with the 3rd International Joint Conference on natural language processing, IJCNLP 2008

作者： Paladhi, Sibabrata Bandyopadhyay, Sivaji Department of Computer Sc. and Engg. Jadavpur University India

This paper explores the research issue and methodology of a query focused multidocument summarizer. Considering its possible application area is Web, the computation is clearly divided into offline and online tasks. At initial preprocessing stage an offline document graph is constructed, where the nodes are basically paragraphs of the documents and edge scores are defined as the correlation measure between the nodes. At query time, given a set of keywords, each node is assigned a query dependent score, the initial graph is expanded and keyword search is performed over the graph to find a spanning tree identifying relevant nodes satisfying the keywords. Paragraph ordering of the output summary is taken care of so that the output looks coherent. Although all the examples, shown in this paper are based on English language, we show that our system is useful in generating query dependent summarization for non- English languages also. We also present the evaluation of the system. © 2008 CLIA 2008 - 2nd International workshop on Cross Lingual Information Access: Addressing the Information Need of Multilingual Societies - 3rd International Joint Conference on natural language processing, IJCNLP 2008 - proceedings of the workshop. All rights reserved.

关键词： graphic methods

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Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom graph 9

Enhancing Dialogue Symptom Diagnosis with Global Attention a...

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Conference on Empirical methods in natural language processing / 9th International Joint Conference on natural language processing (EMNLP-IJCNLP)

作者： Chen, Qin Lin, Xinzhu He, Xiahui Tou, Huaixiao Chen, Ting Wei, Zhongyu Fudan Univ Sch Data Sci Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China Ping An Technol Hangzhou Peoples R China

ISBN: (纸本)9781950737901

Symptom diagnosis is a challenging yet profound problem in natural language processing. Most previous research focus on investigating the standard electronic medical records for symptom diagnosis, while the dialogues between doctors and patients that contain more rich information are not well studied. In this paper, we first construct a dialogue symptom diagnosis dataset based on an online medical forum with a large amount of dialogues between patients and doctors. Then, we provide some benchmark models on this dataset to boost the research of dialogue symptom diagnosis. In order to further enhance the performance of symptom diagnosis over dialogues, we propose a global attention mechanism to capture more symptom related information, and build a symptom graph to model the associations between symptoms rather than treating each symptom independently. Experimental results show that both the global attention and symptom graph are effective to boost dialogue symptom diagnosis. In particular, our proposed model achieves the state-of-the-art performance on the constructed dataset.

关键词： Diagnosis

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