Paper Group NANR 62
Improving Text-to-Pictograph Translation Through Word Sense Disambiguation. Leveraging VerbNet to build Corpus-Specific Verb Clusters. It-disambiguation and source-aware language models for cross-lingual pronoun prediction. Towards Comparability of Linguistic Graph Banks for Semantic Parsing. Dynamic Entity Representation with Max-pooling Improves …
Improving Text-to-Pictograph Translation Through Word Sense Disambiguation
| Title | Improving Text-to-Pictograph Translation Through Word Sense Disambiguation |
| Authors | Leen Sevens, Gilles Jacobs, V, Vincent eghinste, Ineke Schuurman, Frank Van Eynde |
| Abstract | |
| Tasks | Common Sense Reasoning, Word Sense Disambiguation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/S16-2017/ |
| https://www.aclweb.org/anthology/S16-2017 | |
| PWC | https://paperswithcode.com/paper/improving-text-to-pictograph-translation |
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Leveraging VerbNet to build Corpus-Specific Verb Clusters
| Title | Leveraging VerbNet to build Corpus-Specific Verb Clusters |
| Authors | Daniel Peterson, Jordan Boyd-Graber, Martha Palmer, Daisuke Kawahara |
| Abstract | |
| Tasks | Semantic Role Labeling |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/S16-2012/ |
| https://www.aclweb.org/anthology/S16-2012 | |
| PWC | https://paperswithcode.com/paper/leveraging-verbnet-to-build-corpus-specific |
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It-disambiguation and source-aware language models for cross-lingual pronoun prediction
| Title | It-disambiguation and source-aware language models for cross-lingual pronoun prediction |
| Authors | Sharid Lo{'a}iciga, Liane Guillou, Christian Hardmeier |
| Abstract | |
| Tasks | Coreference Resolution, Language Modelling, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2351/ |
| https://www.aclweb.org/anthology/W16-2351 | |
| PWC | https://paperswithcode.com/paper/it-disambiguation-and-source-aware-language |
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Towards Comparability of Linguistic Graph Banks for Semantic Parsing
| Title | Towards Comparability of Linguistic Graph Banks for Semantic Parsing |
| Authors | Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinkov{'a}, Dan Flickinger, Jan Haji{\v{c}}, Angelina Ivanova, Zde{\v{n}}ka Ure{\v{s}}ov{'a} |
| Abstract | We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP{\textasciitilde}2016 and provides an update and extension to previous versions used as Semantic Dependency Parsing target representations in the 2014 and 2015 Semantic Evaluation Exercises. For a common core of English text, this third edition comprises semantic dependency graphs from four distinct frameworks, packaged in a unified abstract format and aligned at the sentence and token levels. SDP 2016 is the first general release of this resource and available for licensing from the Linguistic Data Consortium in May 2016. The data is accompanied by an open-source SDP utility toolkit and system results from previous contrastive parsing evaluations against these target representations. |
| Tasks | Dependency Parsing, Semantic Dependency Parsing, Semantic Parsing |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1630/ |
| https://www.aclweb.org/anthology/L16-1630 | |
| PWC | https://paperswithcode.com/paper/towards-comparability-of-linguistic-graph |
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Dynamic Entity Representation with Max-pooling Improves Machine Reading
| Title | Dynamic Entity Representation with Max-pooling Improves Machine Reading |
| Authors | Sosuke Kobayashi, Ran Tian, Naoaki Okazaki, Kentaro Inui |
| Abstract | |
| Tasks | Reading Comprehension |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1099/ |
| https://www.aclweb.org/anthology/N16-1099 | |
| PWC | https://paperswithcode.com/paper/dynamic-entity-representation-with-max |
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Ontology-Based Categorization of Bacteria and Habitat Entities using Information Retrieval Techniques
| Title | Ontology-Based Categorization of Bacteria and Habitat Entities using Information Retrieval Techniques |
| Authors | Mert Tiftikci, Hakan {\c{S}}ahin, Berfu B{"u}y{"u}k{"o}z, Alper Yay{\i}k{\c{c}}{\i}, Arzucan {"O}zg{"u}r |
| Abstract | |
| Tasks | Information Retrieval |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3007/ |
| https://www.aclweb.org/anthology/W16-3007 | |
| PWC | https://paperswithcode.com/paper/ontology-based-categorization-of-bacteria-and |
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Forecasting Word Model: Twitter-based Influenza Surveillance and Prediction
| Title | Forecasting Word Model: Twitter-based Influenza Surveillance and Prediction |
| Authors | Hayate Iso, Shoko Wakamiya, Eiji Aramaki |
| Abstract | Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events. Particularly, social media-based infectious disease surveillance has attracted increasing attention. In this work, we specifically examine influenza: a common topic of communication on social media. The fundamental theory of this work is that several words, such as symptom words (fever, headache, etc.), appear in advance of flu epidemic occurrence. Consequently, past word occurrence can contribute to estimation of the number of current patients. To employ such forecasting words, one can first estimate the optimal time lag for each word based on their cross correlation. Then one can build a linear model consisting of word frequencies at different time points for nowcasting and for forecasting influenza epidemics. Experimentally obtained results (using 7.7 million tweets of August 2012 {–} January 2016), the proposed model achieved the best nowcasting performance to date (correlation ratio 0.93) and practically sufficient forecasting performance (correlation ratio 0.91 in 1-week future prediction, and correlation ratio 0.77 in 3-weeks future prediction). This report is the first of the relevant literature to describe a model enabling prediction of future epidemics using Twitter. |
| Tasks | Future prediction |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1008/ |
| https://www.aclweb.org/anthology/C16-1008 | |
| PWC | https://paperswithcode.com/paper/forecasting-word-model-twitter-based |
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Extending Phrase-Based Translation with Dependencies by Using Graphs
| Title | Extending Phrase-Based Translation with Dependencies by Using Graphs |
| Authors | Liangyou Li, Andy Way, Qun Liu |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0602/ |
| https://www.aclweb.org/anthology/W16-0602 | |
| PWC | https://paperswithcode.com/paper/extending-phrase-based-translation-with |
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PersoNER: Persian Named-Entity Recognition
| Title | PersoNER: Persian Named-Entity Recognition |
| Authors | Hanieh Poostchi, Ehsan Zare Borzeshi, Mohammad Abdous, Massimo Piccardi |
| Abstract | Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network. |
| Tasks | Machine Translation, Named Entity Recognition, Question Answering |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1319/ |
| https://www.aclweb.org/anthology/C16-1319 | |
| PWC | https://paperswithcode.com/paper/personer-persian-named-entity-recognition |
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Developing an Unsupervised Grammar Checker for Filipino Using Hybrid N-grams as Grammar Rules
| Title | Developing an Unsupervised Grammar Checker for Filipino Using Hybrid N-grams as Grammar Rules |
| Authors | Matthew Phillip Go, Allan Borra |
| Abstract | |
| Tasks | |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/Y16-2008/ |
| https://www.aclweb.org/anthology/Y16-2008 | |
| PWC | https://paperswithcode.com/paper/developing-an-unsupervised-grammar-checker |
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Dynamic Generative model for Diachronic Sense Emergence Detection
| Title | Dynamic Generative model for Diachronic Sense Emergence Detection |
| Authors | Martin Emms, Arun Kumar Jayapal |
| Abstract | As time passes words can acquire meanings they did not previously have, such as the {}twitter post{'} usage of {}tweet{'}. We address how this can be detected from time-stamped raw text. We propose a generative model with senses dependent on times and context words dependent on senses but otherwise eternal, and a Gibbs sampler for estimation. We obtain promising parameter estimates for positive (resp. negative) cases of known sense emergence (resp non-emergence) and adapt the {}pseudo-word{'} technique (Schutze, 1992) to give a novel further evaluation via {}pseudo-neologisms{'}. The question of ground-truth is also addressed and a technique proposed to locate an emergence date for evaluation purposes. |
| Tasks | |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1129/ |
| https://www.aclweb.org/anthology/C16-1129 | |
| PWC | https://paperswithcode.com/paper/dynamic-generative-model-for-diachronic-sense |
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Universal Dependencies for Norwegian
| Title | Universal Dependencies for Norwegian |
| Authors | Lilja {\O}vrelid, Petter Hohle |
| Abstract | This article describes the conversion of the Norwegian Dependency Treebank to the Universal Dependencies scheme. This paper details the mapping of PoS tags, morphological features and dependency relations and provides a description of the structural changes made to NDT analyses in order to make it compliant with the UD guidelines. We further present PoS tagging and dependency parsing experiments which report first results for the processing of the converted treebank. The full converted treebank was made available with the 1.2 release of the UD treebanks. |
| Tasks | Dependency Parsing |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1250/ |
| https://www.aclweb.org/anthology/L16-1250 | |
| PWC | https://paperswithcode.com/paper/universal-dependencies-for-norwegian |
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TEITOK: Text-Faithful Annotated Corpora
| Title | TEITOK: Text-Faithful Annotated Corpora |
| Authors | Maarten Janssen |
| Abstract | TEITOK is a web-based framework for corpus creation, annotation, and distribution, that combines textual and linguistic annotation within a single TEI based XML document. TEITOK provides several built-in NLP tools to automatically (pre)process texts, and is highly customizable. It features multiple orthographic transcription layers, and a wide range of user-defined token-based annotations. For searching, TEITOK interfaces with a local CQP server. TEITOK can handle various types of additional resources including Facsimile images and linked audio files, making it possible to have a combined written/spoken corpus. It also has additional modules for PSDX syntactic annotation and several types of stand-off annotation. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1637/ |
| https://www.aclweb.org/anthology/L16-1637 | |
| PWC | https://paperswithcode.com/paper/teitok-text-faithful-annotated-corpora |
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CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures
| Title | CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures |
| Authors | Nasrin Mostafazadeh, Alyson Grealish, Nathanael Chambers, James Allen, V, Lucy erwende |
| Abstract | |
| Tasks | Question Answering, Text Summarization |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1007/ |
| https://www.aclweb.org/anthology/W16-1007 | |
| PWC | https://paperswithcode.com/paper/caters-causal-and-temporal-relation-scheme |
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N-gram language models for massively parallel devices
| Title | N-gram language models for massively parallel devices |
| Authors | Nikolay Bogoychev, Adam Lopez |
| Abstract | |
| Tasks | Language Modelling, Machine Translation, Optical Character Recognition, Speech Recognition |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1183/ |
| https://www.aclweb.org/anthology/P16-1183 | |
| PWC | https://paperswithcode.com/paper/n-gram-language-models-for-massively-parallel |
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