Paper Group NANR 112
Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words. Filling in the Blanks in Understanding Discourse Adverbials: Consistency, Conflict, and Context-Dependence in a Crowdsourced Elicitation Task. Orthographic and Morphological Correspondences between Related Slavic Languages as a Base for Modeling of Mutual Intelligibility. U …
Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words
| Title | Implicit Aspect Detection in Restaurant Reviews using Cooccurence of Words |
| Authors | Rrubaa Panchendrarajan, Nazick Ahamed, Brunthavan Murugaiah, Prakhash Sivakumar, Surangika Ranathunga, Akila Pemasiri |
| Abstract | |
| Tasks | Opinion Mining, Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0421/ |
| https://www.aclweb.org/anthology/W16-0421 | |
| PWC | https://paperswithcode.com/paper/implicit-aspect-detection-in-restaurant |
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Filling in the Blanks in Understanding Discourse Adverbials: Consistency, Conflict, and Context-Dependence in a Crowdsourced Elicitation Task
| Title | Filling in the Blanks in Understanding Discourse Adverbials: Consistency, Conflict, and Context-Dependence in a Crowdsourced Elicitation Task |
| Authors | Hannah Rohde, Anna Dickinson, Nathan Schneider, Christopher N. L. Clark, Annie Louis, Bonnie Webber |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-1707/ |
| https://www.aclweb.org/anthology/W16-1707 | |
| PWC | https://paperswithcode.com/paper/filling-in-the-blanks-in-understanding |
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Orthographic and Morphological Correspondences between Related Slavic Languages as a Base for Modeling of Mutual Intelligibility
| Title | Orthographic and Morphological Correspondences between Related Slavic Languages as a Base for Modeling of Mutual Intelligibility |
| Authors | Andrea Fischer, Kl{'a}ra J{'a}grov{'a}, Irina Stenger, Tania Avgustinova, Dietrich Klakow, Rol Marti, |
| Abstract | In an intercomprehension scenario, typically a native speaker of language L1 is confronted with output from an unknown, but related language L2. In this setting, the degree to which the receiver recognizes the unfamiliar words greatly determines communicative success. Despite exhibiting great string-level differences, cognates may be recognized very successfully if the receiver is aware of regular correspondences which allow to transform the unknown word into its familiar form. Modeling L1-L2 intercomprehension then requires the identification of all the regular correspondences between languages L1 and L2. We here present a set of linguistic orthographic correspondences manually compiled from comparative linguistics literature along with a set of statistically-inferred suggestions for correspondence rules. In order to do statistical inference, we followed the Minimum Description Length principle, which proposes to choose those rules which are most effective at describing the data. Our statistical model was able to reproduce most of our linguistic correspondences (88.5{%} for Czech-Polish and 75.7{%} for Bulgarian-Russian) and furthermore allowed to easily identify many more non-trivial correspondences which also cover aspects of morphology. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1665/ |
| https://www.aclweb.org/anthology/L16-1665 | |
| PWC | https://paperswithcode.com/paper/orthographic-and-morphological |
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Unsupervised Resolution of Acronyms and Abbreviations in Nursing Notes Using Document-Level Context Models
| Title | Unsupervised Resolution of Acronyms and Abbreviations in Nursing Notes Using Document-Level Context Models |
| Authors | Katrin Kirchhoff, Anne M. Turner |
| Abstract | |
| Tasks | Machine Translation, Text Simplification |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-6107/ |
| https://www.aclweb.org/anthology/W16-6107 | |
| PWC | https://paperswithcode.com/paper/unsupervised-resolution-of-acronyms-and |
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Exploring Query Expansion for Entity Searches in PubMed
| Title | Exploring Query Expansion for Entity Searches in PubMed |
| Authors | Chung-Chi Huang, Zhiyong Lu |
| Abstract | |
| Tasks | |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-6114/ |
| https://www.aclweb.org/anthology/W16-6114 | |
| PWC | https://paperswithcode.com/paper/exploring-query-expansion-for-entity-searches |
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LibN3L:A Lightweight Package for Neural NLP
| Title | LibN3L:A Lightweight Package for Neural NLP |
| Authors | Meishan Zhang, Jie Yang, Zhiyang Teng, Yue Zhang |
| Abstract | We present a light-weight machine learning tool for NLP research. The package supports operations on both discrete and dense vectors, facilitating implementation of linear models as well as neural models. It provides several basic layers which mainly aims for single-layer linear and non-linear transformations. By using these layers, we can conveniently implement linear models and simple neural models. Besides, this package also integrates several complex layers by composing those basic layers, such as RNN, Attention Pooling, LSTM and gated RNN. Those complex layers can be used to implement deep neural models directly. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1034/ |
| https://www.aclweb.org/anthology/L16-1034 | |
| PWC | https://paperswithcode.com/paper/libn3la-lightweight-package-for-neural-nlp |
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Keynote Lecture 1: Practical Use of Machine Translation in International Organizations
| Title | Keynote Lecture 1: Practical Use of Machine Translation in International Organizations |
| Authors | Bruno Pouliquen |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-6301/ |
| https://www.aclweb.org/anthology/W16-6301 | |
| PWC | https://paperswithcode.com/paper/keynote-lecture-1-practical-use-of-machine |
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Word Embedding Evaluation and Combination
| Title | Word Embedding Evaluation and Combination |
| Authors | Sahar Ghannay, Benoit Favre, Yannick Est{`e}ve, Nathalie Camelin |
| Abstract | Word embeddings have been successfully used in several natural language processing tasks (NLP) and speech processing. Different approaches have been introduced to calculate word embeddings through neural networks. In the literature, many studies focused on word embedding evaluation, but for our knowledge, there are still some gaps. This paper presents a study focusing on a rigorous comparison of the performances of different kinds of word embeddings. These performances are evaluated on different NLP and linguistic tasks, while all the word embeddings are estimated on the same training data using the same vocabulary, the same number of dimensions, and other similar characteristics. The evaluation results reported in this paper match those in the literature, since they point out that the improvements achieved by a word embedding in one task are not consistently observed across all tasks. For that reason, this paper investigates and evaluates approaches to combine word embeddings in order to take advantage of their complementarity, and to look for the effective word embeddings that can achieve good performances on all tasks. As a conclusion, this paper provides new perceptions of intrinsic qualities of the famous word embedding families, which can be different from the ones provided by works previously published in the scientific literature. |
| Tasks | Word Embeddings |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1046/ |
| https://www.aclweb.org/anthology/L16-1046 | |
| PWC | https://paperswithcode.com/paper/word-embedding-evaluation-and-combination |
| Repo | |
| Framework | |
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
| Title | Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) |
| Authors | |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1000/ |
| https://www.aclweb.org/anthology/P16-1000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-54th-annual-meeting-of-the |
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Constraint Grammar-based conversion of Dependency Treebanks
| Title | Constraint Grammar-based conversion of Dependency Treebanks |
| Authors | Eckhard Bick |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-6314/ |
| https://www.aclweb.org/anthology/W16-6314 | |
| PWC | https://paperswithcode.com/paper/constraint-grammar-based-conversion-of |
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Sentence Based Discourse Classification for Hindi Story Text-to-Speech (TTS) System
| Title | Sentence Based Discourse Classification for Hindi Story Text-to-Speech (TTS) System |
| Authors | Kumud Tripathi, Parakrant Sarkar, K. Sreenivasa Rao |
| Abstract | |
| Tasks | Text Classification |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-6307/ |
| https://www.aclweb.org/anthology/W16-6307 | |
| PWC | https://paperswithcode.com/paper/sentence-based-discourse-classification-for |
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| Framework | |
Pairing Wikipedia Articles Across Languages
| Title | Pairing Wikipedia Articles Across Languages |
| Authors | Marcus Klang, Pierre Nugues |
| Abstract | Wikipedia has become a reference knowledge source for scores of NLP applications. One of its invaluable features lies in its multilingual nature, where articles on a same entity or concept can have from one to more than 200 different versions. The interlinking of language versions in Wikipedia has undergone a major renewal with the advent of Wikidata, a unified scheme to identify entities and their properties using unique numbers. However, as the interlinking is still manually carried out by thousands of editors across the globe, errors may creep in the assignment of entities. In this paper, we describe an optimization technique to match automatically language versions of articles, and hence entities, that is only based on bags of words and anchors. We created a dataset of all the articles on persons we extracted from Wikipedia in six languages: English, French, German, Russian, Spanish, and Swedish. We report a correct match of at least 94.3{%} on each pair. |
| Tasks | Question Answering, Word Alignment |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4410/ |
| https://www.aclweb.org/anthology/W16-4410 | |
| PWC | https://paperswithcode.com/paper/pairing-wikipedia-articles-across-languages |
| Repo | |
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Lecture Translator - Speech translation framework for simultaneous lecture translation
| Title | Lecture Translator - Speech translation framework for simultaneous lecture translation |
| Authors | Markus M{"u}ller, Thai Son Nguyen, Jan Niehues, Eunah Cho, Bastian Kr{"u}ger, Thanh-Le Ha, Kevin Kilgour, Matthias Sperber, Mohammed Mediani, Sebastian St{"u}ker, Alex Waibel |
| Abstract | |
| Tasks | Machine Translation, Speech Recognition |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-3017/ |
| https://www.aclweb.org/anthology/N16-3017 | |
| PWC | https://paperswithcode.com/paper/lecture-translator-speech-translation |
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Discourse Parsing with Attention-based Hierarchical Neural Networks
| Title | Discourse Parsing with Attention-based Hierarchical Neural Networks |
| Authors | Qi Li, Tianshi Li, Baobao Chang |
| Abstract | |
| Tasks | Document Summarization, Feature Engineering, Question Answering, Sentiment Analysis |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1035/ |
| https://www.aclweb.org/anthology/D16-1035 | |
| PWC | https://paperswithcode.com/paper/discourse-parsing-with-attention-based |
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Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning
| Title | Grammatical Error Detection Based on Machine Learning for Mandarin as Second Language Learning |
| Authors | Jui-Feng Yeh, Tsung-Wei Hsu, Chan-Kun Yeh |
| Abstract | Mandarin is not simple language for foreigner. Even using Mandarin as the mother tongue, they have to spend more time to learn when they were child. The following issues are the reason why causes learning problem. First, the word is envolved by Hieroglyphic. So a character can express meanings independently, but become a word has another semantic. Second, the Mandarin{'}s grammars have flexible rule and special usage. Therefore, the common grammatical errors can classify to missing, redundant, selection and disorder. In this paper, we proposed the structure of the Recurrent Neural Networks using Long Short-term memory (RNN-LSTM). It can detect the error type from the foreign learner writing. The features based on the word vector and part-of-speech vector. In the test data found that our method in the detection level of recall better than the others, even as high as 0.9755. That is because we give the possibility of greater choice in detecting errors. |
| Tasks | Chinese Word Segmentation, Dependency Parsing, Grammatical Error Detection, Part-Of-Speech Tagging |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4918/ |
| https://www.aclweb.org/anthology/W16-4918 | |
| PWC | https://paperswithcode.com/paper/grammatical-error-detection-based-on-machine |
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