Paper Group NANR 81
Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning. Longitudinal Studies of Variation Sets in Child-directed Speech. Distributional Thesauri for Information Retrieval and vice versa. Book Reviews: Semantic Similarity from Natural Language and Ontology Analysis by S'ebastien Harispe, Sylvie Ranwez, Stefan Janaqi …
Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning
| Title | Proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-1900/ |
| https://www.aclweb.org/anthology/W16-1900 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-7th-workshop-on-cognitive-1 |
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Longitudinal Studies of Variation Sets in Child-directed Speech
| Title | Longitudinal Studies of Variation Sets in Child-directed Speech |
| Authors | Mats Wir{'e}n, Kristina Nilsson Bj{"o}rkenstam, Gintar{.e} Grigonyt{.e}, Elisabet Eir Cortes |
| Abstract | |
| Tasks | Language Acquisition |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-1907/ |
| https://www.aclweb.org/anthology/W16-1907 | |
| PWC | https://paperswithcode.com/paper/longitudinal-studies-of-variation-sets-in |
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Distributional Thesauri for Information Retrieval and vice versa
| Title | Distributional Thesauri for Information Retrieval and vice versa |
| Authors | Vincent Claveau, Ewa Kijak |
| Abstract | Distributional thesauri are useful in many tasks of Natural Language Processing. In this paper, we address the problem of building and evaluating such thesauri with the help of Information Retrieval (IR) concepts. Two main contributions are proposed. First, following the work of [8], we show how IR tools and concepts can be used with success to build a thesaurus. Through several experiments and by evaluating directly the results with reference lexicons, we show that some IR models outperform state-of-the-art systems. Secondly, we use IR as an applicative framework to indirectly evaluate the generated thesaurus. Here again, this task-based evaluation validates the IR approach used to build the thesaurus. Moreover, it allows us to compare these results with those from the direct evaluation framework used in the literature. The observed differences bring these evaluation habits into question. |
| Tasks | Information Retrieval |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1588/ |
| https://www.aclweb.org/anthology/L16-1588 | |
| PWC | https://paperswithcode.com/paper/distributional-thesauri-for-information |
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Book Reviews: Semantic Similarity from Natural Language and Ontology Analysis by S'ebastien Harispe, Sylvie Ranwez, Stefan Janaqi, and Jacky Montmain
| Title | Book Reviews: Semantic Similarity from Natural Language and Ontology Analysis by S'ebastien Harispe, Sylvie Ranwez, Stefan Janaqi, and Jacky Montmain |
| Authors | Deyi Xiong |
| Abstract | |
| Tasks | Information Retrieval, Semantic Similarity, Semantic Textual Similarity |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/J16-4010/ |
| https://www.aclweb.org/anthology/J16-4010 | |
| PWC | https://paperswithcode.com/paper/book-reviews-semantic-similarity-from-natural |
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A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification
| Title | A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification |
| Authors | Lianhui Qin, Zhisong Zhang, Hai Zhao |
| Abstract | |
| Tasks | Feature Engineering, Implicit Discourse Relation Classification, Machine Translation, Question Answering, Relation Classification |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1246/ |
| https://www.aclweb.org/anthology/D16-1246 | |
| PWC | https://paperswithcode.com/paper/a-stacking-gated-neural-architecture-for |
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WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models
| Title | WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models |
| Authors | Xin Tang, Fei Li, Donghong Ji |
| Abstract | |
| Tasks | Feature Engineering, Machine Translation, Semantic Parsing |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1141/ |
| https://www.aclweb.org/anthology/S16-1141 | |
| PWC | https://paperswithcode.com/paper/whunlp-at-semeval-2016-task-dimsum-a-pilot |
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Node-based Induction of Tree-Substitution Grammars
| Title | Node-based Induction of Tree-Substitution Grammars |
| Authors | Rose Sloan |
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| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-3308/ |
| https://www.aclweb.org/anthology/W16-3308 | |
| PWC | https://paperswithcode.com/paper/node-based-induction-of-tree-substitution |
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Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling
| Title | Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling |
| Authors | Deyu Zhou, Tianmeng Gao, Yulan He |
| Abstract | |
| Tasks | Dimensionality Reduction, Semantic Parsing |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1026/ |
| https://www.aclweb.org/anthology/P16-1026 | |
| PWC | https://paperswithcode.com/paper/jointly-event-extraction-and-visualization-on |
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Graph-Based Translation Via Graph Segmentation
| Title | Graph-Based Translation Via Graph Segmentation |
| Authors | Liangyou Li, Andy Way, Qun Liu |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1010/ |
| https://www.aclweb.org/anthology/P16-1010 | |
| PWC | https://paperswithcode.com/paper/graph-based-translation-via-graph |
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Praat on the Web: An Upgrade of Praat for Semi-Automatic Speech Annotation
| Title | Praat on the Web: An Upgrade of Praat for Semi-Automatic Speech Annotation |
| Authors | M{'o}nica Dom{'\i}nguez, Iv{'a}n Latorre, Mireia Farr{'u}s, Joan Codina-Filb{`a}, Leo Wanner |
| Abstract | This paper presents an implementation of the widely used speech analysis tool Praat as a web application with an extended functionality for feature annotation. In particular, Praat on the Web addresses some of the central limitations of the original Praat tool and provides (i) enhanced visualization of annotations in a dedicated window for feature annotation at interval and point segments, (ii) a dynamic scripting composition exemplified with a modular prosody tagger, and (iii) portability and an operational web interface. Speech annotation tools with such a functionality are key for exploring large corpora and designing modular pipelines. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-2046/ |
| https://www.aclweb.org/anthology/C16-2046 | |
| PWC | https://paperswithcode.com/paper/praat-on-the-web-an-upgrade-of-praat-for-semi |
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J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features
| Title | J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features |
| Authors | Dat Ba Nguyen, Martin Theobald, Gerhard Weikum |
| Abstract | Methods for Named Entity Recognition and Disambiguation (NERD) perform NER and NED in two separate stages. Therefore, NED may be penalized with respect to precision by NER false positives, and suffers in recall from NER false negatives. Conversely, NED does not fully exploit information computed by NER such as types of mentions. This paper presents J-NERD, a new approach to perform NER and NED jointly, by means of a probabilistic graphical model that captures mention spans, mention types, and the mapping of mentions to entities in a knowledge base. We present experiments with different kinds of texts from the CoNLL{'}03, ACE{'}05, and ClueWeb{'}09-FACC1 corpora. J-NERD consistently outperforms state-of-the-art competitors in end-to-end NERD precision, recall, and F1. |
| Tasks | Named Entity Recognition |
| Published | 2016-01-01 |
| URL | https://www.aclweb.org/anthology/Q16-1016/ |
| https://www.aclweb.org/anthology/Q16-1016 | |
| PWC | https://paperswithcode.com/paper/j-nerd-joint-named-entity-recognition-and |
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Feature-Rich Twitter Named Entity Recognition and Classification
| Title | Feature-Rich Twitter Named Entity Recognition and Classification |
| Authors | Utpal Kumar Sikdar, Bj{"o}rn Gamb{"a}ck |
| Abstract | Twitter named entity recognition is the process of identifying proper names and classifying them into some predefined labels/categories. The paper introduces a Twitter named entity system using a supervised machine learning approach, namely Conditional Random Fields. A large set of different features was developed and the system was trained using these. The Twitter named entity task can be divided into two parts: i) Named entity extraction from tweets and ii) Twitter name classification into ten different types. For Twitter named entity recognition on unseen test data, our system obtained the second highest F1 score in the shared task: 63.22{%}. The system performance on the classification task was worse, with an F1 measure of 40.06{%} on unseen test data, which was the fourth best of the ten systems participating in the shared task. |
| Tasks | Entity Extraction, Machine Translation, Named Entity Recognition, Question Answering |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3922/ |
| https://www.aclweb.org/anthology/W16-3922 | |
| PWC | https://paperswithcode.com/paper/feature-rich-twitter-named-entity-recognition |
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Evaluating Sequence Alignment for Learning Inflectional Morphology
| Title | Evaluating Sequence Alignment for Learning Inflectional Morphology |
| Authors | David King |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2008/ |
| https://www.aclweb.org/anthology/W16-2008 | |
| PWC | https://paperswithcode.com/paper/evaluating-sequence-alignment-for-learning |
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Re-assessing the Impact of SMT Techniques with Human Evaluation: a Case Study on English—Croatian
| Title | Re-assessing the Impact of SMT Techniques with Human Evaluation: a Case Study on English—Croatian |
| Authors | Antonio Toral, Raphael Rubino, Gema Ram{'\i}rez-S{'a}nchez |
| Abstract | |
| Tasks | Language Modelling, Machine Translation |
| Published | 2016-01-01 |
| URL | https://www.aclweb.org/anthology/W16-3423/ |
| https://www.aclweb.org/anthology/W16-3423 | |
| PWC | https://paperswithcode.com/paper/re-assessing-the-impact-of-smt-techniques |
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Morphological Segmentation Can Improve Syllabification
| Title | Morphological Segmentation Can Improve Syllabification |
| Authors | Garrett Nicolai, Lei Yao, Grzegorz Kondrak |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2016/ |
| https://www.aclweb.org/anthology/W16-2016 | |
| PWC | https://paperswithcode.com/paper/morphological-segmentation-can-improve |
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