VS@HLT-NAACLPub Date : 2015-06-01DOI: 10.3115/v1/W15-1507
J. Urbain, Glenn Bushee, George Kowalski
{"title":"Distributional Semantic Concept Models for Entity Relation Discovery","authors":"J. Urbain, Glenn Bushee, George Kowalski","doi":"10.3115/v1/W15-1507","DOIUrl":"https://doi.org/10.3115/v1/W15-1507","url":null,"abstract":"We present an ad hoc concept modeling approach using distributional semantic models to identify fine-grained entities and their relations in an online search setting. Concepts are generated from user-defined seed terms, distributional evidence, and a relational model over concept distributions. A dimensional indexing model is used for efficient aggregation of distributional, syntactic, and relational evidence. The proposed semi-supervised model allows concepts to be defined and related at varying levels of granularity and scope. Qualitative evaluations on medical records, intelligence documents, and open domain web data demonstrate the efficacy of our approach.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"74 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114252092","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}
{"title":"Combining Distributed Vector Representations for Words","authors":"Justin Garten, Kenji Sagae, Volkan Ustun, Morteza Dehghani","doi":"10.3115/v1/W15-1513","DOIUrl":"https://doi.org/10.3115/v1/W15-1513","url":null,"abstract":"Recent interest in distributed vector representations for words has resulted in an increased diversity of approaches, each with strengths and weaknesses. We demonstrate how diverse vector representations may be inexpensively composed into hybrid representations, effectively leveraging strengths of individual components, as evidenced by substantial improvements on a standard word analogy task. We further compare these results over different sizes of training sets and find these advantages are more pronounced when training data is limited. Finally, we explore the relative impacts of the differences in the learning methods themselves and the size of the contexts they access.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129153305","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}
VS@HLT-NAACLPub Date : 2015-06-01DOI: 10.3115/v1/W15-1504
Mikael Kågebäck, Fredrik D. Johansson, Richard Johansson, Devdatt P. Dubhashi
{"title":"Neural context embeddings for automatic discovery of word senses","authors":"Mikael Kågebäck, Fredrik D. Johansson, Richard Johansson, Devdatt P. Dubhashi","doi":"10.3115/v1/W15-1504","DOIUrl":"https://doi.org/10.3115/v1/W15-1504","url":null,"abstract":"Word sense induction (WSI) is the problem of \u0000automatically building an inventory of senses \u0000for a set of target words using only a text \u0000corpus. We introduce a new method for embedding word instances and their context, for use in WSI. The method, Instance-context embedding (ICE), leverages neural word embeddings, and the correlation statistics they capture, to compute high quality embeddings of word contexts. In WSI, these context embeddings are clustered to find the word senses present in the text. ICE is based on a novel method for combining word embeddings using continuous Skip-gram, based on both se- \u0000mantic and a temporal aspects of context \u0000words. ICE is evaluated both in a new system, and in an extension to a previous system \u0000for WSI. In both cases, we surpass previous \u0000state-of-the-art, on the WSI task of SemEval-2013, which highlights the generality of ICE. Our proposed system achieves a 33% relative improvement.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133317605","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}
{"title":"Distributed Word Representations Improve NER for e-Commerce","authors":"Mahesh Joshi, Ethan Hart, Mirko Vogel, Jean-David Ruvini","doi":"10.3115/v1/W15-1522","DOIUrl":"https://doi.org/10.3115/v1/W15-1522","url":null,"abstract":"This paper presents a case study of using distributed word representations, word2vec in particular, for improving performance of Named Entity Recognition for the eCommerce domain. We also demonstrate that distributed word representations trained on a smaller amount of in-domain data are more effective than word vectors trained on very large amount of out-of-domain data, and that their combination gives the best results.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122574762","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}
VS@HLT-NAACLPub Date : 2015-06-01DOI: 10.3115/v1/W15-1516
Abdulaziz Alghunaim, Mitra Mohtarami, D. S. Cyphers, James R. Glass
{"title":"A Vector Space Approach for Aspect Based Sentiment Analysis","authors":"Abdulaziz Alghunaim, Mitra Mohtarami, D. S. Cyphers, James R. Glass","doi":"10.3115/v1/W15-1516","DOIUrl":"https://doi.org/10.3115/v1/W15-1516","url":null,"abstract":"Vector representations for language has been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Aspect Based Sentiment Analysis. In particular, we target three sub-tasks namely aspect term extraction, aspect category detection, and aspect sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. We utilize vector representations to compute various vectorbased features and conduct extensive experiments to demonstrate their effectiveness. Using simple vector based features, we achieve F1 scores of 79.91% for aspect term extraction, 86.75% for category detection, and the accuracy 72.39% for aspect sentiment prediction.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134397039","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}
VS@HLT-NAACLPub Date : 2015-06-01DOI: 10.3115/v1/W15-1521
Thang Luong, Hieu Pham, Christopher D. Manning
{"title":"Bilingual Word Representations with Monolingual Quality in Mind","authors":"Thang Luong, Hieu Pham, Christopher D. Manning","doi":"10.3115/v1/W15-1521","DOIUrl":"https://doi.org/10.3115/v1/W15-1521","url":null,"abstract":"Recent work in learning bilingual representations tend to tailor towards achieving good performance on bilingual tasks, most often the crosslingual document classification (CLDC) evaluation, but to the detriment of preserving clustering structures of word representations monolingually. In this work, we propose a joint model to learn word representations from scratch that utilizes both the context coocurrence information through the monolingual component and the meaning equivalent signals from the bilingual constraint. Specifically, we extend the recently popular skipgram model to learn high quality bilingual representations efficiently. Our learned embeddings achieve a new state-of-the-art accuracy of 80.3 for the German to English CLDC task and a highly competitive performance of 90.7 for the other classification direction. At the same time, our models outperform best embeddings from past bilingual representation work by a large margin in the monolingual word similarity evaluation. 1","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130406270","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}
VS@HLT-NAACLPub Date : 2015-06-01DOI: 10.3115/v1/W15-1525
John M. Conroy, Sashka Davis
{"title":"Vector Space Models for Scientific Document Summarization","authors":"John M. Conroy, Sashka Davis","doi":"10.3115/v1/W15-1525","DOIUrl":"https://doi.org/10.3115/v1/W15-1525","url":null,"abstract":"In this paper we compare the performance of three approaches for estimating the latent weights of terms for scientific document summarization, given the document and a set of citing documents. The first approach is a termfrequency (TF) vector space method utilizing a nonnegative matrix factorization (NNMF) for dimensionality reduction. The other two are language modeling approaches for predicting the term distributions of human-generated summaries. The language model we build exploits the key sections of the document and a set of citing sentences derived from auxiliary documents that cite the document of interest. The parameters of the model may be set via a minimization of the Jensen-Shannon (JS) divergence. We use the OCCAMS algorithm (Optimal Combinatorial Covering Algorithm for Multi-document Summarization) to select a set of sentences that maximizes the term-coverage score while minimizing redundancy. The results are evaluated with standard ROUGE metrics, and the performance of the resulting methods achieve ROUGE scores exceeding those of the average human summarizer.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114455138","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}
VS@HLT-NAACLPub Date : 2015-06-01DOI: 10.3115/v1/W15-1514
Mohit Bansal
{"title":"Dependency Link Embeddings: Continuous Representations of Syntactic Substructures","authors":"Mohit Bansal","doi":"10.3115/v1/W15-1514","DOIUrl":"https://doi.org/10.3115/v1/W15-1514","url":null,"abstract":"We present a simple method to learn continuous representations of dependency substructures (links), with the motivation of directly working with higher-order, structured embeddings and their hidden relationships, and also to avoid the millions of sparse, template-based word-cluster features in dependency parsing. These link embeddings allow a significantly smaller and simpler set of unary features for dependency parsing, while maintaining improvements similar to state-of-the-art, n-ary word-cluster features, and also stacking over them. Moreover, these link vectors (made publicly available) are directly portable as offthe-shelf, dense, syntactic features in various NLP tasks. As one example, we incorporate them into constituent parse reranking, where their small feature set again matches the performance of standard non-local, manuallydefined features, and also stacks over them.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121857893","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}
VS@HLT-NAACLPub Date : 2015-06-01DOI: 10.3115/v1/W15-1524
Ayah Zirikly, Mona T. Diab
{"title":"Named Entity Recognition for Arabic Social Media","authors":"Ayah Zirikly, Mona T. Diab","doi":"10.3115/v1/W15-1524","DOIUrl":"https://doi.org/10.3115/v1/W15-1524","url":null,"abstract":"The majority of research on Arabic Named Entity Recognition (NER) addresses the the task for newswire genre, where the language used is Modern Standard Arabic (MSA), however, the need to study this task in social media is becoming more vital. Social media is characterized by the use of both MSA and Dialectal Arabic (DA), with often code switching between the two language varieties. Despite some common characteristics between MSA and DA, there are significant differences between which result in poor performance when MSA targeting systems are applied for NER in DA. Additionally, most NER systems rely primarily on gazetteers, which can be more challenging in a social media processing context due to an inherent low coverage. In this paper, we present a gazetteers-free NER system for Dialectal data that yields an F1 score of 72.68% which is an absolute improvement of 2 3% over a comparable state-ofthe-art gazetteer based DA-NER system.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125131906","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}
VS@HLT-NAACLPub Date : 2015-06-01DOI: 10.3115/v1/W15-1519
Sameer Singh, Tim Rocktäschel, S. Riedel
{"title":"Towards Combined Matrix and Tensor Factorization for Universal Schema Relation Extraction","authors":"Sameer Singh, Tim Rocktäschel, S. Riedel","doi":"10.3115/v1/W15-1519","DOIUrl":"https://doi.org/10.3115/v1/W15-1519","url":null,"abstract":"Matrix factorization of knowledge bases in universal schema has facilitated accurate distantlysupervised relation extraction. This factorization encodes dependencies between textual patterns and structured relations using lowdimensional vectors defined for each entity pair; although these factors are effective at combining evidence for an entity pair, they are inaccurate on rare pairs, or for relations that depend crucially on the entity types. On the other hand, tensor factorization is able to overcome these shortcomings when applied to link prediction by maintaining entity-wise factors. However these models have been unsuitable for universal schema. In this paper we first present an illustration on synthetic data that explains the unsuitability of tensor factorization to relation extraction with universal schemas. Since the benefits of tensor and matrix factorization are complementary, we then investigate two hybrid methods that combine the benefits of the two paradigms. We show that the combination can be fruitful: we handle ambiguously phrased relations, achieve gains in accuracy on real-world relations, and demonstrate that entity embeddings encode entity types.","PeriodicalId":299646,"journal":{"name":"VS@HLT-NAACL","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123999065","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}
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