Paper Group NANR 193
From alignment of etymological data to phylogenetic inference via population genetics. EHU at the SIGMORPHON 2016 Shared Task. A Simple Proposal: Grapheme-to-Phoneme for Inflection. Automatic Classification of Tweets for Analyzing Communication Behavior of Museums. The Open Linguistics Working Group: Developing the Linguistic Linked Open Data Cloud …
From alignment of etymological data to phylogenetic inference via population genetics
| Title | From alignment of etymological data to phylogenetic inference via population genetics |
| Authors | Javad Nouri, Roman Yangarber |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-1905/ |
| https://www.aclweb.org/anthology/W16-1905 | |
| PWC | https://paperswithcode.com/paper/from-alignment-of-etymological-data-to |
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EHU at the SIGMORPHON 2016 Shared Task. A Simple Proposal: Grapheme-to-Phoneme for Inflection
| Title | EHU at the SIGMORPHON 2016 Shared Task. A Simple Proposal: Grapheme-to-Phoneme for Inflection |
| Authors | I{~n}aki Alegria, Izaskun Etxeberria |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2004/ |
| https://www.aclweb.org/anthology/W16-2004 | |
| PWC | https://paperswithcode.com/paper/ehu-at-the-sigmorphon-2016-shared-task-a |
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Automatic Classification of Tweets for Analyzing Communication Behavior of Museums
| Title | Automatic Classification of Tweets for Analyzing Communication Behavior of Museums |
| Authors | Nicolas Foucault, Antoine Courtin |
| Abstract | In this paper, we present a study on tweet classification which aims to define the communication behavior of the 103 French museums that participated in 2014 in the Twitter operation: MuseumWeek. The tweets were automatically classified in four communication categories: sharing experience, promoting participation, interacting with the community, and promoting-informing about the institution. Our classification is multi-class. It combines Support Vector Machines and Naive Bayes methods and is supported by a selection of eighteen subtypes of features of four different kinds: metadata information, punctuation marks, tweet-specific and lexical features. It was tested against a corpus of 1,095 tweets manually annotated by two experts in Natural Language Processing and Information Communication and twelve Community Managers of French museums. We obtained an state-of-the-art result of F1-score of 72{%} by 10-fold cross-validation. This result is very encouraging since is even better than some state-of-the-art results found in the tweet classification literature. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1480/ |
| https://www.aclweb.org/anthology/L16-1480 | |
| PWC | https://paperswithcode.com/paper/automatic-classification-of-tweets-for |
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The Open Linguistics Working Group: Developing the Linguistic Linked Open Data Cloud
| Title | The Open Linguistics Working Group: Developing the Linguistic Linked Open Data Cloud |
| Authors | John Philip McCrae, Christian Chiarcos, Francis Bond, Philipp Cimiano, Thierry Declerck, Gerard de Melo, Jorge Gracia, Sebastian Hellmann, Bettina Klimek, Steven Moran, Petya Osenova, Antonio Pareja-Lora, Jonathan Pool |
| Abstract | The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections. A major outcome of our work is the Linguistic Linked Open Data (LLOD) cloud, an LOD (sub-)cloud of linguistic resources, which covers various linguistic databases, lexicons, corpora, terminologies, and metadata repositories. We present and summarize five years of progress on the development of the cloud and of advancements in open data in linguistics, and we describe recent community activities. The paper aims to serve as a guideline to orient and involve researchers with the community and/or Linguistic Linked Open Data. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1386/ |
| https://www.aclweb.org/anthology/L16-1386 | |
| PWC | https://paperswithcode.com/paper/the-open-linguistics-working-group-developing |
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Statistics-Based Lexical Choice for NLG from Quantitative Information
| Title | Statistics-Based Lexical Choice for NLG from Quantitative Information |
| Authors | Xiao Li, Kees van Deemter, Chenghua Lin |
| Abstract | |
| Tasks | Text Generation |
| Published | 2016-09-01 |
| URL | https://www.aclweb.org/anthology/W16-6618/ |
| https://www.aclweb.org/anthology/W16-6618 | |
| PWC | https://paperswithcode.com/paper/statistics-based-lexical-choice-for-nlg-from |
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Mapping Estimation for Discrete Optimal Transport
| Title | Mapping Estimation for Discrete Optimal Transport |
| Authors | Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard |
| Abstract | We are interested in the computation of the transport map of an Optimal Transport problem. Most of the computational approaches of Optimal Transport use the Kantorovich relaxation of the problem to learn a probabilistic coupling $\mgamma$ but do not address the problem of learning the underlying transport map $\funcT$ linked to the original Monge problem. Consequently, it lowers the potential usage of such methods in contexts where out-of-samples computations are mandatory. In this paper we propose a new way to jointly learn the coupling and an approximation of the transport map. We use a jointly convex formulation which can be efficiently optimized. Additionally, jointly learning the coupling and the transport map allows to smooth the result of the Optimal Transport and generalize it to out-of-samples examples. Empirically, we show the interest and the relevance of our method in two tasks: domain adaptation and image editing. |
| Tasks | Domain Adaptation |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6312-mapping-estimation-for-discrete-optimal-transport |
| http://papers.nips.cc/paper/6312-mapping-estimation-for-discrete-optimal-transport.pdf | |
| PWC | https://paperswithcode.com/paper/mapping-estimation-for-discrete-optimal |
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Learning to Answer Questions from Wikipedia Infoboxes
| Title | Learning to Answer Questions from Wikipedia Infoboxes |
| Authors | Alvaro Morales, Varot Premtoon, Cordelia Avery, Sue Felshin, Boris Katz |
| Abstract | |
| Tasks | Answer Selection, Open-Domain Question Answering, Question Answering |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1199/ |
| https://www.aclweb.org/anthology/D16-1199 | |
| PWC | https://paperswithcode.com/paper/learning-to-answer-questions-from-wikipedia |
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`Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English
| Title | `Calling on the classical phone’: a distributional model of adjective-noun errors in learners’ English | |
| Authors | Aur{'e}lie Herbelot, Ekaterina Kochmar |
| Abstract | In this paper we discuss three key points related to error detection (ED) in learners{'} English. We focus on content word ED as one of the most challenging tasks in this area, illustrating our claims on adjective{–}noun (AN) combinations. In particular, we (1) investigate the role of context in accurately capturing semantic anomalies and implement a system based on distributional topic coherence, which achieves state-of-the-art accuracy on a standard test set; (2) thoroughly investigate our system{'}s performance across individual adjective classes, concluding that a class-dependent approach is beneficial to the task; (3) discuss the data size bottleneck in this area, and highlight the challenges of automatic error generation for content words. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1093/ |
| https://www.aclweb.org/anthology/C16-1093 | |
| PWC | https://paperswithcode.com/paper/acalling-on-the-classical-phonea-a |
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JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines
| Title | JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines |
| Authors | Braja Gopal Patra, Dipankar Das, B, Sivaji yopadhyay |
| Abstract | |
| Tasks | Information Retrieval, Natural Language Inference, Opinion Mining, Sentiment Analysis, Stance Detection, Text Summarization |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1071/ |
| https://www.aclweb.org/anthology/S16-1071 | |
| PWC | https://paperswithcode.com/paper/ju_nlp-at-semeval-2016-task-6-detecting |
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Endangered Language Documentation: Bootstrapping a Chatino Speech Corpus, Forced Aligner, ASR
| Title | Endangered Language Documentation: Bootstrapping a Chatino Speech Corpus, Forced Aligner, ASR |
| Authors | Malgorzata {'C}avar, Damir {'C}avar, Hilaria Cruz |
| Abstract | This project approaches the problem of language documentation and revitalization from a rather untraditional angle. To improve and facilitate language documentation of endangered languages, we attempt to use corpus linguistic methods and speech and language technologies to reduce the time needed for transcription and annotation of audio and video language recordings. The paper demonstrates this approach on the example of the endangered and seriously under-resourced variety of Eastern Chatino (CTP). We show how initial speech corpora can be created that can facilitate the development of speech and language technologies for under-resourced languages by utilizing Forced Alignment tools to time align transcriptions. Time-aligned transcriptions can be used to train speech corpora and utilize automatic speech recognition tools for the transcription and annotation of untranscribed data. Speech technologies can be used to reduce the time and effort necessary for transcription and annotation of large collections of audio and video recordings in digital language archives, addressing the transcription bottleneck problem that most language archives and many under-documented languages are confronted with. This approach can increase the availability of language resources from low-resourced and endangered languages to speech and language technology research and development. |
| Tasks | Speech Recognition |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1632/ |
| https://www.aclweb.org/anthology/L16-1632 | |
| PWC | https://paperswithcode.com/paper/endangered-language-documentation |
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The Virginia Tech System at CoNLL-2016 Shared Task on Shallow Discourse Parsing
| Title | The Virginia Tech System at CoNLL-2016 Shared Task on Shallow Discourse Parsing |
| Authors | Ch, Prashant rasekar, Xuan Zhang, Saurabh Chakravarty, Arijit Ray, John Krulick, Alla Rozovskaya |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/K16-2016/ |
| https://www.aclweb.org/anthology/K16-2016 | |
| PWC | https://paperswithcode.com/paper/the-virginia-tech-system-at-conll-2016-shared |
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Improving Pronoun Translation by Modeling Coreference Uncertainty
| Title | Improving Pronoun Translation by Modeling Coreference Uncertainty |
| Authors | Ngoc Quang Luong, Andrei Popescu-Belis |
| Abstract | |
| Tasks | Coreference Resolution, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2202/ |
| https://www.aclweb.org/anthology/W16-2202 | |
| PWC | https://paperswithcode.com/paper/improving-pronoun-translation-by-modeling |
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Alignment-Based Neural Machine Translation
| Title | Alignment-Based Neural Machine Translation |
| Authors | Tamer Alkhouli, Gabriel Bretschner, Jan-Thorsten Peter, Mohammed Hethnawi, Andreas Guta, Hermann Ney |
| Abstract | |
| Tasks | Machine Translation, Speech Recognition, Word Alignment |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2206/ |
| https://www.aclweb.org/anthology/W16-2206 | |
| PWC | https://paperswithcode.com/paper/alignment-based-neural-machine-translation |
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Incremental Acquisition of Verb Hypothesis Space towards Physical World Interaction
| Title | Incremental Acquisition of Verb Hypothesis Space towards Physical World Interaction |
| Authors | Lanbo She, Joyce Chai |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1011/ |
| https://www.aclweb.org/anthology/P16-1011 | |
| PWC | https://paperswithcode.com/paper/incremental-acquisition-of-verb-hypothesis |
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PROMT Translation Systems for WMT 2016 Translation Tasks
| Title | PROMT Translation Systems for WMT 2016 Translation Tasks |
| Authors | Alex Molchanov, er, Fedor Bykov |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2319/ |
| https://www.aclweb.org/anthology/W16-2319 | |
| PWC | https://paperswithcode.com/paper/promt-translation-systems-for-wmt-2016 |
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