Paper Group NANR 128
Syndromic Surveillance through Measuring Lexical Shift in Emergency Department Chief Complaint Texts. Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters. UPPC - Urdu Paraphrase Plagiarism Corpus. A Study of Valence & Argument Integration in Chinese Verb-Resultative Complement. Proceedings of the Workshop on Language Te …
Syndromic Surveillance through Measuring Lexical Shift in Emergency Department Chief Complaint Texts
| Title | Syndromic Surveillance through Measuring Lexical Shift in Emergency Department Chief Complaint Texts |
| Authors | Hafsah Aamer, Bahadorreza Ofoghi, Karin Verspoor |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/U16-1005/ |
| https://www.aclweb.org/anthology/U16-1005 | |
| PWC | https://paperswithcode.com/paper/syndromic-surveillance-through-measuring |
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Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters
| Title | Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters |
| Authors | Fabio Celli, Evgeny Stepanov, Massimo Poesio, Giuseppe Riccardi |
| Abstract | On June 23rd 2016, UK held the referendum which ratified the exit from the EU. While most of the traditional pollsters failed to forecast the final vote, there were online systems that hit the result with high accuracy using opinion mining techniques and big data. Starting one month before, we collected and monitored millions of posts about the referendum from social media conversations, and exploited Natural Language Processing techniques to predict the referendum outcome. In this paper we discuss the methods used by traditional pollsters and compare it to the predictions based on different opinion mining techniques. We find that opinion mining based on agreement/disagreement classification works better than opinion mining based on polarity classification in the forecast of the referendum outcome. |
| Tasks | Opinion Mining, Sentiment Analysis |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4312/ |
| https://www.aclweb.org/anthology/W16-4312 | |
| PWC | https://paperswithcode.com/paper/predicting-brexit-classifying-agreement-is |
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UPPC - Urdu Paraphrase Plagiarism Corpus
| Title | UPPC - Urdu Paraphrase Plagiarism Corpus |
| Authors | Muhammad Sharjeel, Paul Rayson, Rao Muhammad Adeel Nawab |
| Abstract | Paraphrase plagiarism is a significant and widespread problem and research shows that it is hard to detect. Several methods and automatic systems have been proposed to deal with it. However, evaluation and comparison of such solutions is not possible because of the unavailability of benchmark corpora with manual examples of paraphrase plagiarism. To deal with this issue, we present the novel development of a paraphrase plagiarism corpus containing simulated (manually created) examples in the Urdu language - a language widely spoken around the world. This resource is the first of its kind developed for the Urdu language and we believe that it will be a valuable contribution to the evaluation of paraphrase plagiarism detection systems. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1289/ |
| https://www.aclweb.org/anthology/L16-1289 | |
| PWC | https://paperswithcode.com/paper/uppc-urdu-paraphrase-plagiarism-corpus |
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A Study of Valence & Argument Integration in Chinese Verb-Resultative Complement
| Title | A Study of Valence & Argument Integration in Chinese Verb-Resultative Complement |
| Authors | Anran Li |
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| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/Y16-3012/ |
| https://www.aclweb.org/anthology/Y16-3012 | |
| PWC | https://paperswithcode.com/paper/a-study-of-valence-a-argument-integration-in |
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Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)
| Title | Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH) |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4000/ |
| https://www.aclweb.org/anthology/W16-4000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-workshop-on-language-2 |
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Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction
| Title | Evaluating anaphora and coreference resolution to improve automatic keyphrase extraction |
| Authors | Marco Basaldella, Giorgia Chiaradia, Carlo Tasso |
| Abstract | In this paper we analyze the effectiveness of using linguistic knowledge from coreference and anaphora resolution for improving the performance for supervised keyphrase extraction. In order to verify the impact of these features, we define a baseline keyphrase extraction system and evaluate its performance on a standard dataset using different machine learning algorithms. Then, we consider new sets of features by adding combinations of the linguistic features we propose and we evaluate the new performance of the system. We also use anaphora and coreference resolution to transform the documents, trying to simulate the cohesion process performed by the human mind. We found that our approach has a slightly positive impact on the performance of automatic keyphrase extraction, in particular when considering the ranking of the results. |
| Tasks | Coreference Resolution, Language Modelling |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1077/ |
| https://www.aclweb.org/anthology/C16-1077 | |
| PWC | https://paperswithcode.com/paper/evaluating-anaphora-and-coreference |
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Detecting novel metaphor using selectional preference information
| Title | Detecting novel metaphor using selectional preference information |
| Authors | Hessel Haagsma, Johannes Bjerva |
| Abstract | |
| Tasks | Semantic Parsing, Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1102/ |
| https://www.aclweb.org/anthology/W16-1102 | |
| PWC | https://paperswithcode.com/paper/detecting-novel-metaphor-using-selectional |
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ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network
| Title | ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network |
| Authors | Br, Lauritz t, David Grimm, Mengfei Zhou, Yannick Versley |
| Abstract | |
| Tasks | Amr Parsing, Entity Linking, Machine Translation, Semantic Role Labeling |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1179/ |
| https://www.aclweb.org/anthology/S16-1179 | |
| PWC | https://paperswithcode.com/paper/icl-hd-at-semeval-2016-task-8-meaning |
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NEAL: A Neurally Enhanced Approach to Linking Citation and Reference
| Title | NEAL: A Neurally Enhanced Approach to Linking Citation and Reference |
| Authors | Tadashi Nomoto |
| Abstract | |
| Tasks | Information Retrieval |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1519/ |
| https://www.aclweb.org/anthology/W16-1519 | |
| PWC | https://paperswithcode.com/paper/neal-a-neurally-enhanced-approach-to-linking |
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Long-distance anaphors and the blocking effect revisited-An East Asian perspective
| Title | Long-distance anaphors and the blocking effect revisited-An East Asian perspective |
| Authors | Hyunjun Park |
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| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/Y16-2007/ |
| https://www.aclweb.org/anthology/Y16-2007 | |
| PWC | https://paperswithcode.com/paper/long-distance-anaphors-and-the-blocking |
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Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
| Title | Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks |
| Authors | Jad Kabbara, Jackie Chi Kit Cheung |
| Abstract | |
| Tasks | Machine Translation, Text Generation |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-6010/ |
| https://www.aclweb.org/anthology/W16-6010 | |
| PWC | https://paperswithcode.com/paper/stylistic-transfer-in-natural-language |
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Automatic Tweet Generation From Traffic Incident Data
| Title | Automatic Tweet Generation From Traffic Incident Data |
| Authors | Khoa Tran, Fred Popowich |
| Abstract | |
| Tasks | Text Generation |
| Published | 2016-09-01 |
| URL | https://www.aclweb.org/anthology/W16-3512/ |
| https://www.aclweb.org/anthology/W16-3512 | |
| PWC | https://paperswithcode.com/paper/automatic-tweet-generation-from-traffic |
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PotTS: The Potsdam Twitter Sentiment Corpus
| Title | PotTS: The Potsdam Twitter Sentiment Corpus |
| Authors | Uladzimir Sidarenka |
| Abstract | In this paper, we introduce a novel comprehensive dataset of 7,992 German tweets, which were manually annotated by two human experts with fine-grained opinion relations. A rich annotation scheme used for this corpus includes such sentiment-relevant elements as opinion spans, their respective sources and targets, emotionally laden terms with their possible contextual negations and modifiers. Various inter-annotator agreement studies, which were carried out at different stages of work on these data (at the initial training phase, upon an adjudication step, and after the final annotation run), reveal that labeling evaluative judgements in microblogs is an inherently difficult task even for professional coders. These difficulties, however, can be alleviated by letting the annotators revise each other{'}s decisions. Once rechecked, the experts can proceed with the annotation of further messages, staying at a fairly high level of agreement. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1181/ |
| https://www.aclweb.org/anthology/L16-1181 | |
| PWC | https://paperswithcode.com/paper/potts-the-potsdam-twitter-sentiment-corpus |
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A posteriori error bounds for joint matrix decomposition problems
| Title | A posteriori error bounds for joint matrix decomposition problems |
| Authors | Nicolo Colombo, Nikos Vlassis |
| Abstract | Joint matrix triangularization is often used for estimating the joint eigenstructure of a set M of matrices, with applications in signal processing and machine learning. We consider the problem of approximate joint matrix triangularization when the matrices in M are jointly diagonalizable and real, but we only observe a set M’ of noise perturbed versions of the matrices in M. Our main result is a first-order upper bound on the distance between any approximate joint triangularizer of the matrices in M’ and any exact joint triangularizer of the matrices in M. The bound depends only on the observable matrices in M’ and the noise level. In particular, it does not depend on optimization specific properties of the triangularizer, such as its proximity to critical points, that are typical of existing bounds in the literature. To our knowledge, this is the first a posteriori bound for joint matrix decomposition. We demonstrate the bound on synthetic data for which the ground truth is known. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6424-a-posteriori-error-bounds-for-joint-matrix-decomposition-problems |
| http://papers.nips.cc/paper/6424-a-posteriori-error-bounds-for-joint-matrix-decomposition-problems.pdf | |
| PWC | https://paperswithcode.com/paper/a-posteriori-error-bounds-for-joint-matrix |
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Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF
| Title | Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF |
| Authors | Rosa Tsegaye Aga, Lucas Drumond, Christian Wartena, Lars Schmidt-Thieme |
| Abstract | Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multi-relational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results. |
| Tasks | Semantic Similarity, Semantic Textual Similarity |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1255/ |
| https://www.aclweb.org/anthology/C16-1255 | |
| PWC | https://paperswithcode.com/paper/integrating-distributional-and-lexical |
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