Paper Group NANR 206
Disaster Analysis using User-Generated Weather Report. Automated Discourse Analysis of Narrations by Adolescents with Autistic Spectrum Disorder. SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering. Asynchronous Parallel Greedy Coordinate Descent. Whose Nickname is This? Recognizing Politicians from Th …
Disaster Analysis using User-Generated Weather Report
| Title | Disaster Analysis using User-Generated Weather Report |
| Authors | Yasunobu Asakura, Masatsugu Hangyo, Mamoru Komachi |
| Abstract | Information extraction from user-generated text has gained much attention with the growth of the Web.Disaster analysis using information from social media provides valuable, real-time, geolocation information for helping people caught up these in disasters. However, it is not convenient to analyze texts posted on social media because disaster keywords match any texts that contain words. For collecting posts about a disaster from social media, we need to develop a classifier to filter posts irrelevant to disasters. Moreover, because of the nature of social media, we can take advantage of posts that come with GPS information. However, a post does not always refer to an event occurring at the place where it has been posted. Therefore, we propose a new task of classifying whether a flood disaster occurred, in addition to predicting the geolocation of events from user-generated text. We report the annotation of the flood disaster corpus and develop a classifier to demonstrate the use of this corpus for disaster analysis. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3906/ |
| https://www.aclweb.org/anthology/W16-3906 | |
| PWC | https://paperswithcode.com/paper/disaster-analysis-using-user-generated |
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Automated Discourse Analysis of Narrations by Adolescents with Autistic Spectrum Disorder
| Title | Automated Discourse Analysis of Narrations by Adolescents with Autistic Spectrum Disorder |
| Authors | Michaela Regneri, Diane King |
| Abstract | |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-1901/ |
| https://www.aclweb.org/anthology/W16-1901 | |
| PWC | https://paperswithcode.com/paper/automated-discourse-analysis-of-narrations-by |
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SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering
| Title | SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering |
| Authors | Tsvetomila Mihaylova, Pepa Gencheva, Martin Boyanov, Ivana Yovcheva, Todor Mihaylov, Momchil Hardalov, Yasen Kiprov, Daniel Balchev, Ivan Koychev, Preslav Nakov, Ivelina Nikolova, Galia Angelova |
| Abstract | |
| Tasks | Answer Selection, Community Question Answering, Question Answering, Question Similarity |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1129/ |
| https://www.aclweb.org/anthology/S16-1129 | |
| PWC | https://paperswithcode.com/paper/super-team-at-semeval-2016-task-3-building-a |
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Asynchronous Parallel Greedy Coordinate Descent
| Title | Asynchronous Parallel Greedy Coordinate Descent |
| Authors | Yang You, Xiangru Lian, Ji Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, James Demmel, Cho-Jui Hsieh |
| Abstract | n this paper, we propose and study an Asynchronous parallel Greedy Coordinate Descent (Asy-GCD) algorithm for minimizing a smooth function with bounded constraints. At each iteration, workers asynchronously conduct greedy coordinate descent updates on a block of variables. In the first part of the paper, we analyze the theoretical behavior of Asy-GCD and prove a linear convergence rate. In the second part, we develop an efficient kernel SVM solver based on Asy-GCD in the shared memory multi-core setting. Since our algorithm is fully asynchronous—each core does not need to idle and wait for the other cores—the resulting algorithm enjoys good speedup and outperforms existing multi-core kernel SVM solvers including asynchronous stochastic coordinate descent and multi-core LIBSVM. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6070-asynchronous-parallel-greedy-coordinate-descent |
| http://papers.nips.cc/paper/6070-asynchronous-parallel-greedy-coordinate-descent.pdf | |
| PWC | https://paperswithcode.com/paper/asynchronous-parallel-greedy-coordinate |
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Whose Nickname is This? Recognizing Politicians from Their Aliases
| Title | Whose Nickname is This? Recognizing Politicians from Their Aliases |
| Authors | Wei-Chung Wang, Hung-Chen Chen, Zhi-Kai Ji, Hui-I Hsiao, Yu-Shian Chiu, Lun-Wei Ku |
| Abstract | Using aliases to refer to public figures is one way to make fun of people, to express sarcasm, or even to sidestep legal issues when expressing opinions on social media. However, linking an alias back to the real name is difficult, as it entails phonemic, graphemic, and semantic challenges. In this paper, we propose a phonemic-based approach and inject semantic information to align aliases with politicians{'} Chinese formal names. The proposed approach creates an HMM model for each name to model its phonemes and takes into account document-level pairwise mutual information to capture the semantic relations to the alias. In this work we also introduce two new datasets consisting of 167 phonemic pairs and 279 mixed pairs of aliases and formal names. Experimental results show that the proposed approach models both phonemic and semantic information and outperforms previous work on both the phonemic and mixed datasets with the best top-1 accuracies of 0.78 and 0.59 respectively. |
| Tasks | Entity Linking, Named Entity Recognition, Transliteration |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3910/ |
| https://www.aclweb.org/anthology/W16-3910 | |
| PWC | https://paperswithcode.com/paper/whose-nickname-is-this-recognizing |
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Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators
| Title | Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators |
| Authors | Ajit Jain, Girish Kasiviswanathan, Ruihong Huang |
| Abstract | Accurate event detection in social media is very challenging because user generated contents are extremely noisy and sparse in content. Event indicators are generally words or phrases that act as a trigger that help us understand the semantics of the context they occur in. We present a weakly supervised approach that relies on using a single strong event indicator phrase as a seed to acquire a variety of additional event cues. We propose to leverage various types of implicit event indicators, such as props, actors and precursor events, to achieve precise event detection. We experimented with civil unrest events and show that the automatically learnt event indicators are effective in identifying specific types of events. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3911/ |
| https://www.aclweb.org/anthology/W16-3911 | |
| PWC | https://paperswithcode.com/paper/towards-accurate-event-detection-in-social |
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Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
| Title | Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016) |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4900/ |
| https://www.aclweb.org/anthology/W16-4900 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-3rd-workshop-on-natural |
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Extract Domain-specific Paraphrase from Monolingual Corpus for Automatic Evaluation of Machine Translation
| Title | Extract Domain-specific Paraphrase from Monolingual Corpus for Automatic Evaluation of Machine Translation |
| Authors | Lilin Zhang, Zhen Weng, Wenyan Xiao, Jianyi Wan, Zhiming Chen, Yiming Tan, Maoxi Li, Mingwen Wang |
| Abstract | |
| Tasks | Domain Adaptation, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2343/ |
| https://www.aclweb.org/anthology/W16-2343 | |
| PWC | https://paperswithcode.com/paper/extract-domain-specific-paraphrase-from |
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Extracting Structured Scholarly Information from the Machine Translation Literature
| Title | Extracting Structured Scholarly Information from the Machine Translation Literature |
| Authors | Eunsol Choi, Matic Horvat, Jonathan May, Kevin Knight, Daniel Marcu |
| Abstract | Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work. We introduce a structured, queryable representation for experimental results and a baseline system that automatically populates this representation. The representation can answer compositional questions such as: {}Which are the best published results reported on the NIST 09 Chinese to English dataset?{''} and {}What are the most important methods for speeding up phrase-based decoding?{''} Answering such questions usually involves lengthy literature surveys. Current machine reading for academic papers does not usually consider the actual experiments, but mostly focuses on understanding abstracts. We describe annotation work to create an initial hscientific paper; experimental results representationi corpus. The corpus is composed of 67 papers which were manually annotated with a structured representation of experimental results by domain experts. Additionally, we present a baseline algorithm that characterizes the difficulty of the inference task. |
| Tasks | Machine Translation, Reading Comprehension |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1067/ |
| https://www.aclweb.org/anthology/L16-1067 | |
| PWC | https://paperswithcode.com/paper/extracting-structured-scholarly-information |
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Analysis of Twitter Data for Postmarketing Surveillance in Pharmacovigilance
| Title | Analysis of Twitter Data for Postmarketing Surveillance in Pharmacovigilance |
| Authors | Julie Pain, Jessie Levacher, Adam Quinquenel, Anja Belz |
| Abstract | Postmarketing surveillance (PMS) has the vital aim to monitor effects of drugs after release for use by the general population, but suffers from under-reporting and limited coverage. Automatic methods for detecting drug effect reports, especially for social media, could vastly increase the scope of PMS. Very few automatic PMS methods are currently available, in particular for the messy text types encountered on Twitter. In this paper we describe first results for developing PMS methods specifically for tweets. We describe the corpus of 125,669 tweets we have created and annotated to train and test the tools. We find that generic tools perform well for tweet-level language identification and tweet-level sentiment analysis (both 0.94 F1-Score). For detection of effect mentions we are able to achieve 0.87 F1-Score, while effect-level adverse-vs.-beneficial analysis proves harder with an F1-Score of 0.64. Among other things, our results indicate that MetaMap semantic types provide a very promising basis for identifying drug effect mentions in tweets. |
| Tasks | Language Identification, Sentiment Analysis |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3914/ |
| https://www.aclweb.org/anthology/W16-3914 | |
| PWC | https://paperswithcode.com/paper/analysis-of-twitter-data-for-postmarketing |
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Patterns of Terminological Variation in Post-editing and of Cognate Use in Machine Translation in Contrast to Human Translation
| Title | Patterns of Terminological Variation in Post-editing and of Cognate Use in Machine Translation in Contrast to Human Translation |
| Authors | Oliver {\v{C}}ulo, Jean Nitzke |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-01-01 |
| URL | https://www.aclweb.org/anthology/W16-3401/ |
| https://www.aclweb.org/anthology/W16-3401 | |
| PWC | https://paperswithcode.com/paper/patterns-of-terminological-variation-in-post |
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Normalized Spectral Map Synchronization
| Title | Normalized Spectral Map Synchronization |
| Authors | Yanyao Shen, Qixing Huang, Nati Srebro, Sujay Sanghavi |
| Abstract | The algorithmic advancement of synchronizing maps is important in order to solve a wide range of practice problems with possible large-scale dataset. In this paper, we provide theoretical justifications for spectral techniques for the map synchronization problem, i.e., it takes as input a collection of objects and noisy maps estimated between pairs of objects, and outputs clean maps between all pairs of objects. We show that a simple normalized spectral method that projects the blocks of the top eigenvectors of a data matrix to the map space leads to surprisingly good results. As the noise is modelled naturally as random permutation matrix, this algorithm NormSpecSync leads to competing theoretical guarantees as state-of-the-art convex optimization techniques, yet it is much more efficient. We demonstrate the usefulness of our algorithm in a couple of applications, where it is optimal in both complexity and exactness among existing methods. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6128-normalized-spectral-map-synchronization |
| http://papers.nips.cc/paper/6128-normalized-spectral-map-synchronization.pdf | |
| PWC | https://paperswithcode.com/paper/normalized-spectral-map-synchronization |
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Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets
| Title | Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets |
| Authors | Billal Belainine, Alexs Fonseca, ro, Fatiha Sadat |
| Abstract | In social networks services like Twitter, users are overwhelmed with huge amount of social data, most of which are short, unstructured and highly noisy. Identifying accurate information from this huge amount of data is indeed a hard task. Classification of tweets into organized form will help the user to easily access these required information. Our first contribution relates to filtering parts of speech and preprocessing this kind of highly noisy and short data. Our second contribution concerns the named entity recognition (NER) in tweets. Thus, the adaptation of existing language tools for natural languages, noisy and not accurate language tweets, is necessary. Our third contribution involves segmentation of hashtags and a semantic enrichment using a combination of relations from WordNet, which helps the performance of our classification system, including disambiguation of named entities, abbreviations and acronyms. Graph theory is used to cluster the words extracted from WordNet and tweets, based on the idea of connected components. We test our automatic classification system with four categories: politics, economy, sports and the medical field. We evaluate and compare several automatic classification systems using part or all of the items described in our contributions and found that filtering by part of speech and named entity recognition dramatically increase the classification precision to 77.3 {%}. Moreover, a classification system incorporating segmentation of hashtags and semantic enrichment by two relations from WordNet, synonymy and hyperonymy, increase classification precision up to 83.4 {%}. |
| Tasks | Named Entity Recognition |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3915/ |
| https://www.aclweb.org/anthology/W16-3915 | |
| PWC | https://paperswithcode.com/paper/named-entity-recognition-and-hashtag |
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Learning to Jointly Predict Ellipsis and Comparison Structures
| Title | Learning to Jointly Predict Ellipsis and Comparison Structures |
| Authors | Bakhsh, Omid eh, Alexis Cornelia Wellwood, James Allen |
| Abstract | |
| Tasks | Question Answering, Reading Comprehension, Semantic Parsing, Sentiment Analysis, Structured Prediction |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/K16-1007/ |
| https://www.aclweb.org/anthology/K16-1007 | |
| PWC | https://paperswithcode.com/paper/learning-to-jointly-predict-ellipsis-and |
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Extracting token-level signals of syntactic processing from fMRI - with an application to PoS induction
| Title | Extracting token-level signals of syntactic processing from fMRI - with an application to PoS induction |
| Authors | Joachim Bingel, Maria Barrett, Anders S{\o}gaard |
| Abstract | |
| Tasks | Sentence Compression |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1071/ |
| https://www.aclweb.org/anthology/P16-1071 | |
| PWC | https://paperswithcode.com/paper/extracting-token-level-signals-of-syntactic |
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