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Aggregating and Predicting Sequence Labels from Crowd Annotations. 从人群注释中聚合和预测序列标签。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2017-01-01 DOI: 10.18653/v1/P17-1028
An T Nguyen, Byron C Wallace, Junyi Jessy Li, Ani Nenkova, Matthew Lease
{"title":"Aggregating and Predicting Sequence Labels from Crowd Annotations.","authors":"An T Nguyen,&nbsp;Byron C Wallace,&nbsp;Junyi Jessy Li,&nbsp;Ani Nenkova,&nbsp;Matthew Lease","doi":"10.18653/v1/P17-1028","DOIUrl":"https://doi.org/10.18653/v1/P17-1028","url":null,"abstract":"<p><p>Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory. We evaluate a suite of methods across two different applications and text genres: Named-Entity Recognition in news articles and Information Extraction from biomedical abstracts. Results show improvement over strong baselines. Our source code and data are available online.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2017 ","pages":"299-309"},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.18653/v1/P17-1028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35568131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 75
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media 理解公共社交媒体中关于工作和工作相关幸福感的话语
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2016-08-01 DOI: 10.18653/v1/P16-1099
Tong Liu, Christopher Homan, Cecilia Ovesdotter Alm, Megan C. Lytle-Flint, Ann Marie White, Henry A. Kautz
{"title":"Understanding Discourse on Work and Job-Related Well-Being in Public Social Media","authors":"Tong Liu, Christopher Homan, Cecilia Ovesdotter Alm, Megan C. Lytle-Flint, Ann Marie White, Henry A. Kautz","doi":"10.18653/v1/P16-1099","DOIUrl":"https://doi.org/10.18653/v1/P16-1099","url":null,"abstract":"We construct a humans-in-the-loop supervised learning framework that integrates crowdsourcing feedback and local knowledge to detect job-related tweets from individual and business accounts. Using data-driven ethnography, we examine discourse about work by fusing language-based analysis with temporal, geospational, and labor statistics information.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"251 1","pages":"1044-1053"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75760239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
Nonparametric Spherical Topic Modeling with Word Embeddings. 基于词嵌入的非参数球形主题建模。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2016-08-01 DOI: 10.18653/v1/P16-2087
Kayhan Batmanghelich, Ardavan Saeedi, Karthik Narasimhan, Sam Gershman
{"title":"Nonparametric Spherical Topic Modeling with Word Embeddings.","authors":"Kayhan Batmanghelich,&nbsp;Ardavan Saeedi,&nbsp;Karthik Narasimhan,&nbsp;Sam Gershman","doi":"10.18653/v1/P16-2087","DOIUrl":"https://doi.org/10.18653/v1/P16-2087","url":null,"abstract":"<p><p>Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere. Such a representation is well-suited for directional data. We use a Hierarchical Dirichlet Process for our base topic model and propose an efficient inference algorithm based on Stochastic Variational Inference. This model enables us to naturally exploit the semantic structures of word embeddings while flexibly discovering the number of topics. Experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence on two different text corpora while offering efficient inference.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2016 ","pages":"537-542"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327958/pdf/nihms-999400.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36858644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 78
Neural Tree Indexers for Text Understanding 文本理解的神经树索引器
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2016-07-15 DOI: 10.18653/V1/E17-1002
Tsendsuren Munkhdalai, Hong Yu
{"title":"Neural Tree Indexers for Text Understanding","authors":"Tsendsuren Munkhdalai, Hong Yu","doi":"10.18653/V1/E17-1002","DOIUrl":"https://doi.org/10.18653/V1/E17-1002","url":null,"abstract":"Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic treebased recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and node function. We implemented and evaluated a binary tree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification, outperforming state-of-the-art recurrent and recursive neural networks.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"1 1","pages":"11-21"},"PeriodicalIF":0.0,"publicationDate":"2016-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78981638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 103
Neural Semantic Encoders 神经语义编码器
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2016-07-14 DOI: 10.18653/V1/E17-1038
Tsendsuren Munkhdalai, Hong Yu
{"title":"Neural Semantic Encoders","authors":"Tsendsuren Munkhdalai, Hong Yu","doi":"10.18653/V1/E17-1038","DOIUrl":"https://doi.org/10.18653/V1/E17-1038","url":null,"abstract":"We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access 1 multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"91 1","pages":"397-407"},"PeriodicalIF":0.0,"publicationDate":"2016-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73677189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 133
Exploring Autism Spectrum Disorders Using HLT. 使用 HLT 探索自闭症谱系障碍。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2016-06-01 DOI: 10.18653/v1/w16-0308
Julia Parish-Morris, Mark Liberman, Neville Ryant, Christopher Cieri, Leila Bateman, Emily Ferguson, Robert T Schultz
{"title":"Exploring Autism Spectrum Disorders Using HLT.","authors":"Julia Parish-Morris, Mark Liberman, Neville Ryant, Christopher Cieri, Leila Bateman, Emily Ferguson, Robert T Schultz","doi":"10.18653/v1/w16-0308","DOIUrl":"10.18653/v1/w16-0308","url":null,"abstract":"<p><p>The phenotypic complexity of Autism Spectrum Disorder motivates the application of modern computational methods to large collections of observational data, both for improved clinical diagnosis and for better scientific understanding. We have begun to create a corpus of annotated language samples relevant to this research, and we plan to join with other researchers in pooling and publishing such resources on a large scale. The goal of this paper is to present some initial explorations to illustrate the opportunities that such datasets will afford.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2016 ","pages":"74-84"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7558465/pdf/nihms-985179.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38604333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Measuring idiosyncratic interests in children with autism. 测量自闭症儿童的特殊兴趣。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2015-07-01 DOI: 10.3115/v1/p15-2035
Masoud Rouhizadeh, Emily Prud'hommeaux, Jan van Santen, Richard Sproat
{"title":"Measuring idiosyncratic interests in children with autism.","authors":"Masoud Rouhizadeh,&nbsp;Emily Prud'hommeaux,&nbsp;Jan van Santen,&nbsp;Richard Sproat","doi":"10.3115/v1/p15-2035","DOIUrl":"https://doi.org/10.3115/v1/p15-2035","url":null,"abstract":"A defining symptom of autism spectrum disorder (ASD) is the presence of restricted and repetitive activities and interests, which can surface in language as a perseverative focus on idiosyncratic topics. In this paper, we use semantic similarity measures to identify such idiosyncratic topics in narratives produced by children with and without ASD. We find that neurotypical children tend to use the same words and semantic concepts when retelling the same narrative, while children with ASD, even when producing accurate retellings, use different words and concepts relative not only to neurotypical children but also to other children with ASD. Our results indicate that children with ASD not only stray from the target topic but do so in idiosyncratic ways according to their own restricted interests.","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2015 ","pages":"212-217"},"PeriodicalIF":0.0,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715463/pdf/nihms792406.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35626981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
An Extension of BLANC to System Mentions. BLANC对系统提及的扩展。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2014-06-01 DOI: 10.3115/v1/P14-2005
Xiaoqiang Luo, Sameer Pradhan, Marta Recasens, Eduard Hovy
{"title":"An Extension of BLANC to System Mentions.","authors":"Xiaoqiang Luo,&nbsp;Sameer Pradhan,&nbsp;Marta Recasens,&nbsp;Eduard Hovy","doi":"10.3115/v1/P14-2005","DOIUrl":"https://doi.org/10.3115/v1/P14-2005","url":null,"abstract":"<p><p>BLANC is a link-based coreference evaluation metric for measuring the quality of coreference systems on gold mentions. This paper extends the original BLANC (\"BLANC-gold\" henceforth) to system mentions, removing the gold mention assumption. The proposed BLANC falls back seamlessly to the original one if system mentions are identical to gold mentions, and it is shown to strongly correlate with existing metrics on the 2011 and 2012 CoNLL data.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2014 ","pages":"24-29"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3115/v1/P14-2005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35225786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 36
Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation. 预测提及的评分参考分区:参考实现。
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2014-06-01 DOI: 10.3115/v1/P14-2006
Sameer Pradhan, Xiaoqiang Luo, Marta Recasens, Eduard Hovy, Vincent Ng, Michael Strube
{"title":"Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation.","authors":"Sameer Pradhan,&nbsp;Xiaoqiang Luo,&nbsp;Marta Recasens,&nbsp;Eduard Hovy,&nbsp;Vincent Ng,&nbsp;Michael Strube","doi":"10.3115/v1/P14-2006","DOIUrl":"https://doi.org/10.3115/v1/P14-2006","url":null,"abstract":"<p><p>The definitions of two coreference scoring metrics- B<sup>3</sup> and CEAF-are underspecified with respect to <i>predicted</i>, as opposed to <i>key</i> (or <i>gold</i>) mentions. Several variations have been proposed that manipulate either, or both, the key and predicted mentions in order to get a one-to-one mapping. On the other hand, the metric BLANC was, until recently, limited to scoring partitions of key mentions. In this paper, we (i) argue that mention manipulation for scoring predicted mentions is unnecessary, and potentially harmful as it could produce unintuitive results; (ii) illustrate the application of all these measures to scoring predicted mentions; (iii) make available an open-source, thoroughly-tested reference implementation of the main coreference evaluation measures; and (iv) rescore the results of the CoNLL-2011/2012 shared task systems with this implementation. This will help the community accurately measure and compare new end-to-end coreference resolution algorithms.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2014 ","pages":"30-35"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3115/v1/P14-2006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35225788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 185
Interpretable Semantic Vectors from a Joint Model of Brain- and Text-Based Meaning. 大脑和文本意义联合模型中的可解释语义向量
Proceedings of the conference. Association for Computational Linguistics. Meeting Pub Date : 2014-06-01 DOI: 10.3115/v1/p14-1046
Alona Fyshe, Partha P Talukdar, Brian Murphy, Tom M Mitchell
{"title":"Interpretable Semantic Vectors from a Joint Model of Brain- and Text-Based Meaning.","authors":"Alona Fyshe, Partha P Talukdar, Brian Murphy, Tom M Mitchell","doi":"10.3115/v1/p14-1046","DOIUrl":"10.3115/v1/p14-1046","url":null,"abstract":"<p><p>Vector space models (VSMs) represent word meanings as points in a high dimensional space. VSMs are typically created using a large text corpora, and so represent word semantics as observed in text. We present a new algorithm (JNNSE) that can incorporate a measure of semantics not previously used to create VSMs: brain activation data recorded while people read words. The resulting model takes advantage of the complementary strengths and weaknesses of corpus and brain activation data to give a more complete representation of semantics. Evaluations show that the model 1) matches a behavioral measure of semantics more closely, 2) can be used to predict corpus data for unseen words and 3) has predictive power that generalizes across brain imaging technologies and across subjects. We believe that the model is thus a more faithful representation of mental vocabularies.</p>","PeriodicalId":74541,"journal":{"name":"Proceedings of the conference. Association for Computational Linguistics. Meeting","volume":"2014 ","pages":"489-499"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4497373/pdf/nihms589902.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34282421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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