Paper Group NANR 200
Overview of the Regulatory Network of Plant Seed Development (SeeDev) Task at the BioNLP Shared Task 2016.. Extracting Biomedical Event Using Feature Selection and Word Representation. Focused Evaluation for Image Description with Binary Forced-Choice Tasks. Corpus for Customer Purchase Behavior Prediction in Social Media. A Bilingual Attention Net …
Overview of the Regulatory Network of Plant Seed Development (SeeDev) Task at the BioNLP Shared Task 2016.
| Title | Overview of the Regulatory Network of Plant Seed Development (SeeDev) Task at the BioNLP Shared Task 2016. |
| Authors | Estelle Chaix, Bertr Dubreucq, , Abdelhak Fatihi, Dialekti Valsamou, Robert Bossy, Mouhamadou Ba, Louise Del{'e}ger, Pierre Zweigenbaum, Philippe Bessi{`e}res, Loic Lepiniec, Claire N{'e}dellec |
| Abstract | |
| Tasks | Entity Extraction |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3001/ |
| https://www.aclweb.org/anthology/W16-3001 | |
| PWC | https://paperswithcode.com/paper/overview-of-the-regulatory-network-of-plant |
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Extracting Biomedical Event Using Feature Selection and Word Representation
| Title | Extracting Biomedical Event Using Feature Selection and Word Representation |
| Authors | Xinyu He, Lishuang Li, Jieqiong Zheng, Meiyue Qin |
| Abstract | |
| Tasks | Edge Detection, Feature Selection |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3013/ |
| https://www.aclweb.org/anthology/W16-3013 | |
| PWC | https://paperswithcode.com/paper/extracting-biomedical-event-using-feature |
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Focused Evaluation for Image Description with Binary Forced-Choice Tasks
| Title | Focused Evaluation for Image Description with Binary Forced-Choice Tasks |
| Authors | Micah Hodosh, Julia Hockenmaier |
| Abstract | |
| Tasks | Image Captioning, Question Answering, Text Generation, Visual Question Answering |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3203/ |
| https://www.aclweb.org/anthology/W16-3203 | |
| PWC | https://paperswithcode.com/paper/focused-evaluation-for-image-description-with |
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Corpus for Customer Purchase Behavior Prediction in Social Media
| Title | Corpus for Customer Purchase Behavior Prediction in Social Media |
| Authors | Shigeyuki Sakaki, Francine Chen, M Korpusik, y, Yan-Ying Chen |
| Abstract | Many people post about their daily life on social media. These posts may include information about the purchase activity of people, and insights useful to companies can be derived from them: e.g. profile information of a user who mentioned something about their product. As a further advanced analysis, we consider extracting users who are likely to buy a product from the set of users who mentioned that the product is attractive. In this paper, we report our methodology for building a corpus for Twitter user purchase behavior prediction. First, we collected Twitter users who posted a want phrase + product name: e.g. {``}want a Xperia{''} as candidate want users, and also candidate bought users in the same way. Then, we asked an annotator to judge whether a candidate user actually bought a product. We also annotated whether tweets randomly sampled from want/bought user timelines are relevant or not to purchase. In this annotation, 58{%} of want user tweets and 35{%} of bought user tweets were annotated as relevant. Our data indicate that information embedded in timeline tweets can be used to predict purchase behavior of tweeted products. | |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1475/ |
| https://www.aclweb.org/anthology/L16-1475 | |
| PWC | https://paperswithcode.com/paper/corpus-for-customer-purchase-behavior |
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A Bilingual Attention Network for Code-switched Emotion Prediction
| Title | A Bilingual Attention Network for Code-switched Emotion Prediction |
| Authors | Zhongqing Wang, Yue Zhang, Sophia Lee, Shoushan Li, Guodong Zhou |
| Abstract | Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has emphasized on code-switching text. In this paper, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show that the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects qualitatively informative words. |
| Tasks | Emotion Recognition |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1153/ |
| https://www.aclweb.org/anthology/C16-1153 | |
| PWC | https://paperswithcode.com/paper/a-bilingual-attention-network-for-code |
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Boosting with Abstention
| Title | Boosting with Abstention |
| Authors | Corinna Cortes, Giulia Desalvo, Mehryar Mohri |
| Abstract | We present a new boosting algorithm for the key scenario of binary classification with abstention where the algorithm can abstain from predicting the label of a point, at the price of a fixed cost. At each round, our algorithm selects a pair of functions, a base predictor and a base abstention function. We define convex upper bounds for the natural loss function associated to this problem, which we prove to be calibrated with respect to the Bayes solution. Our algorithm benefits from general margin-based learning guarantees which we derive for ensembles of pairs of base predictor and abstention functions, in terms of the Rademacher complexities of the corresponding function classes. We give convergence guarantees for our algorithm along with a linear-time weak-learning algorithm for abstention stumps. We also report the results of several experiments suggesting that our algorithm provides a significant improvement in practice over two confidence-based algorithms. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6336-boosting-with-abstention |
| http://papers.nips.cc/paper/6336-boosting-with-abstention.pdf | |
| PWC | https://paperswithcode.com/paper/boosting-with-abstention |
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Using Related Languages to Enhance Statistical Language Models
| Title | Using Related Languages to Enhance Statistical Language Models |
| Authors | Anna Currey, Alina Karakanta, Jon Dehdari |
| Abstract | |
| Tasks | Domain Adaptation, Information Retrieval, Language Modelling, Machine Translation, Speech Recognition |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-2017/ |
| https://www.aclweb.org/anthology/N16-2017 | |
| PWC | https://paperswithcode.com/paper/using-related-languages-to-enhance |
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The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations
| Title | The CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations |
| Authors | Enrico Santus, Anna Gladkova, Stefan Evert, Aless Lenci, ro |
| Abstract | The shared task of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex-V) aims at providing a common benchmark for testing current corpus-based methods for the identification of lexical semantic relations (synonymy, antonymy, hypernymy, part-whole meronymy) and at gaining a better understanding of their respective strengths and weaknesses. The shared task uses a challenging dataset extracted from EVALution 1.0, which contains word pairs holding the above-mentioned relations as well as semantically unrelated control items (random). The task is split into two subtasks: (i) identification of related word pairs vs. unrelated ones; (ii) classification of the word pairs according to their semantic relation. This paper describes the subtasks, the dataset, the evaluation metrics, the seven participating systems and their results. The best performing system in subtask 1 is GHHH (F1 = 0.790), while the best system in subtask 2 is LexNet (F1 = 0.445). The dataset and the task description are available at \url{https://sites.google.com/site/cogalex2016/home/shared-task}. |
| Tasks | Language Acquisition, Paraphrase Generation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-5309/ |
| https://www.aclweb.org/anthology/W16-5309 | |
| PWC | https://paperswithcode.com/paper/the-cogalex-v-shared-task-on-the-corpus-based |
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Building a Bagpipe with a Bag and a Pipe: Exploring Conceptual Combination in Vision
| Title | Building a Bagpipe with a Bag and a Pipe: Exploring Conceptual Combination in Vision |
| Authors | S Pezzelle, ro, Ravi Shekhar, Raffaella Bernardi |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3208/ |
| https://www.aclweb.org/anthology/W16-3208 | |
| PWC | https://paperswithcode.com/paper/building-a-bagpipe-with-a-bag-and-a-pipe |
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``Look, some Green Circles!'': Learning to Quantify from Images
| Title | ``Look, some Green Circles!'': Learning to Quantify from Images | |
| Authors | Ionut Sorodoc, Angeliki Lazaridou, Gemma Boleda, Aur{'e}lie Herbelot, S Pezzelle, ro, Raffaella Bernardi |
| Abstract | |
| Tasks | Question Answering, Visual Question Answering |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3211/ |
| https://www.aclweb.org/anthology/W16-3211 | |
| PWC | https://paperswithcode.com/paper/alook-some-green-circlesa-learning-to |
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Text2voronoi: An Image-driven Approach to Differential Diagnosis
| Title | Text2voronoi: An Image-driven Approach to Differential Diagnosis |
| Authors | Alex Mehler, er, Tolga Uslu, Wahed Hemati |
| Abstract | |
| Tasks | Text Categorization |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3212/ |
| https://www.aclweb.org/anthology/W16-3212 | |
| PWC | https://paperswithcode.com/paper/text2voronoi-an-image-driven-approach-to |
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Interfacing Sentential and Discourse TAG-based Grammars
| Title | Interfacing Sentential and Discourse TAG-based Grammars |
| Authors | Laurence Danlos, Aleks Maskharashvili, re, Sylvain Pogodalla |
| Abstract | |
| Tasks | Relation Extraction |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-3303/ |
| https://www.aclweb.org/anthology/W16-3303 | |
| PWC | https://paperswithcode.com/paper/interfacing-sentential-and-discourse-tag |
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Modelling Discourse in STAG: Subordinate Conjunctions and Attributing Phrases
| Title | Modelling Discourse in STAG: Subordinate Conjunctions and Attributing Phrases |
| Authors | Timoth{'e}e Bernard, Laurence Danlos |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-3304/ |
| https://www.aclweb.org/anthology/W16-3304 | |
| PWC | https://paperswithcode.com/paper/modelling-discourse-in-stag-subordinate |
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Corpus and dictionary development for classifiers/quantifiers towards a French-Japanese machine translation
| Title | Corpus and dictionary development for classifiers/quantifiers towards a French-Japanese machine translation |
| Authors | Mutsuko Tomokiyo, Christian Boitet |
| Abstract | Although quantifiers/classifiers expressions occur frequently in everyday communications or written documents, there is no description for them in classical bilingual paper dictionaries, nor in machine-readable dictionaries. The paper describes a corpus and dictionary development for quantifiers/classifiers, and their usage in the framework of French-Japanese machine translation (MT). They often cause problems of lexical ambiguity and of set phrase recognition during analysis, in particular for a long-distance language pair like French and Japanese. For the development of a dictionary aiming at ambiguity resolution for expressions including quantifiers and classifiers which may be ambiguous with common nouns, we have annotated our corpus with UWs (interlingual lexemes) of UNL (Universal Networking Language) found on the UNL-jp dictionary. The extraction of potential classifiers/quantifiers from corpus is made by UNLexplorer web service. Keywords : classifiers, quantifiers, phraseology study, corpus annotation, UNL (Universal Networking Language), UWs dictionary, Tori Bank, French-Japanese machine translation (MT). |
| Tasks | Machine Translation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-5324/ |
| https://www.aclweb.org/anthology/W16-5324 | |
| PWC | https://paperswithcode.com/paper/corpus-and-dictionary-development-for |
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Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters
| Title | Query Translation for Cross-Language Information Retrieval using Multilingual Word Clusters |
| Authors | Paheli Bhattacharya, Pawan Goyal, Sudeshna Sarkar |
| Abstract | In Cross-Language Information Retrieval, finding the appropriate translation of the source language query has always been a difficult problem to solve. We propose a technique towards solving this problem with the help of multilingual word clusters obtained from multilingual word embeddings. We use word embeddings of the languages projected to a common vector space on which a community-detection algorithm is applied to find clusters such that words that represent the same concept from different languages fall in the same group. We utilize these multilingual word clusters to perform query translation for Cross-Language Information Retrieval for three languages - English, Hindi and Bengali. We have experimented with the FIRE 2012 and Wikipedia datasets and have shown improvements over several standard methods like dictionary-based method, a transliteration-based model and Google Translate. |
| Tasks | Community Detection, Information Retrieval, Machine Translation, Multilingual Word Embeddings, Transliteration, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3716/ |
| https://www.aclweb.org/anthology/W16-3716 | |
| PWC | https://paperswithcode.com/paper/query-translation-for-cross-language |
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