Paper Group NANR 211
Modelling Valence and Arousal in Facebook posts. Explicit Fine grained Syntactic and Semantic Annotation of the Idafa Construction in Arabic. Integrating Morphological Desegmentation into Phrase-based Decoding. ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity. A Dataset for Open …
Modelling Valence and Arousal in Facebook posts
| Title | Modelling Valence and Arousal in Facebook posts |
| Authors | Daniel Preo{\c{t}}iuc-Pietro, H. Andrew Schwartz, Gregory Park, Johannes Eichstaedt, Margaret Kern, Lyle Ungar, Elisabeth Shulman |
| Abstract | |
| Tasks | Aspect-Based Sentiment Analysis, Emotion Classification, Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0404/ |
| https://www.aclweb.org/anthology/W16-0404 | |
| PWC | https://paperswithcode.com/paper/modelling-valence-and-arousal-in-facebook |
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Explicit Fine grained Syntactic and Semantic Annotation of the Idafa Construction in Arabic
| Title | Explicit Fine grained Syntactic and Semantic Annotation of the Idafa Construction in Arabic |
| Authors | Abdelati Hawwari, Mohammed Attia, Mahmoud Ghoneim, Mona Diab |
| Abstract | Idafa in traditional Arabic grammar is an umbrella construction that covers several phenomena including what is expressed in English as noun-noun compounds and Saxon and Norman genitives. Additionally, Idafa participates in some other constructions, such as quantifiers, quasi-prepositions, and adjectives. Identifying the various types of the Idafa construction (IC) is of importance to Natural Language processing (NLP) applications. Noun-Noun compounds exhibit special behavior in most languages impacting their semantic interpretation. Hence distinguishing them could have an impact on downstream NLP applications. The most comprehensive syntactic representation of the Arabic language is the LDC Arabic Treebank (ATB). In the ATB, ICs are not explicitly labeled and furthermore, there is no distinction between ICs of noun-noun relations and other traditional ICs. Hence, we devise a detailed syntactic and semantic typification process of the IC phenomenon in Arabic. We target the ATB as a platform for this classification. We render the ATB annotated with explicit IC labels but with the further semantic characterization which is useful for syntactic, semantic and cross language processing. Our typification of IC comprises 3 main syntactic IC types: FIC, GIC, and TIC, and they are further divided into 10 syntactic subclasses. The TIC group is further classified into semantic relations. We devise a method for automatic IC labeling and compare its yield against the CATiB treebank. Our evaluation shows that we achieve the same level of accuracy, but with the additional fine-grained classification into the various syntactic and semantic types. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1567/ |
| https://www.aclweb.org/anthology/L16-1567 | |
| PWC | https://paperswithcode.com/paper/explicit-fine-grained-syntactic-and-semantic |
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Integrating Morphological Desegmentation into Phrase-based Decoding
| Title | Integrating Morphological Desegmentation into Phrase-based Decoding |
| Authors | Mohammad Salameh, Colin Cherry, Grzegorz Kondrak |
| Abstract | |
| Tasks | Language Modelling |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1140/ |
| https://www.aclweb.org/anthology/N16-1140 | |
| PWC | https://paperswithcode.com/paper/integrating-morphological-desegmentation-into |
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ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity
| Title | ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity |
| Authors | Asli Eyecioglu, Bill Keller |
| Abstract | |
| Tasks | Language Modelling, Lemmatization, Natural Language Inference, Paraphrase Identification, Semantic Textual Similarity, Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1114/ |
| https://www.aclweb.org/anthology/S16-1114 | |
| PWC | https://paperswithcode.com/paper/asobek-at-semeval-2016-task-1-sentence |
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A Dataset for Open Event Extraction in English
| Title | A Dataset for Open Event Extraction in English |
| Authors | Kiem-Hieu Nguyen, Xavier Tannier, Olivier Ferret, Romaric Besan{\c{c}}on |
| Abstract | This article presents a corpus for development and testing of event schema induction systems in English. Schema induction is the task of learning templates with no supervision from unlabeled texts, and to group together entities corresponding to the same role in a template. Most of the previous work on this subject relies on the MUC-4 corpus. We describe the limits of using this corpus (size, non-representativeness, similarity of roles across templates) and propose a new, partially-annotated corpus in English which remedies some of these shortcomings. We make use of Wikinews to select the data inside the category Laws {&} Justice, and query Google search engine to retrieve different documents on the same events. Only Wikinews documents are manually annotated and can be used for evaluation, while the others can be used for unsupervised learning. We detail the methodology used for building the corpus and evaluate some existing systems on this new data. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1307/ |
| https://www.aclweb.org/anthology/L16-1307 | |
| PWC | https://paperswithcode.com/paper/a-dataset-for-open-event-extraction-in |
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Exploiting Mutual Benefits between Syntax and Semantic Roles using Neural Network
| Title | Exploiting Mutual Benefits between Syntax and Semantic Roles using Neural Network |
| Authors | Peng Shi, Zhiyang Teng, Yue Zhang |
| Abstract | |
| Tasks | Dependency Parsing, Multi-Task Learning, Semantic Role Labeling |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1098/ |
| https://www.aclweb.org/anthology/D16-1098 | |
| PWC | https://paperswithcode.com/paper/exploiting-mutual-benefits-between-syntax-and |
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Capturing Argument Relationship for Chinese Semantic Role Labeling
| Title | Capturing Argument Relationship for Chinese Semantic Role Labeling |
| Authors | Lei Sha, Sujian Li, Baobao Chang, Zhifang Sui, Tingsong Jiang |
| Abstract | |
| Tasks | Semantic Role Labeling |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1212/ |
| https://www.aclweb.org/anthology/D16-1212 | |
| PWC | https://paperswithcode.com/paper/capturing-argument-relationship-for-chinese |
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使用字典學習法於強健性語音辨識 (The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese]
| Title | 使用字典學習法於強健性語音辨識 (The Use of Dictionary Learning Approach for Robustness Speech Recognition) [In Chinese] |
| Authors | Bi-Cheng Yan, Chin-Hong Shih, Shih-Hung Liu, Berlin Chen |
| Abstract | |
| Tasks | Dictionary Learning, Speech Recognition |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/O16-3003/ |
| https://www.aclweb.org/anthology/O16-3003 | |
| PWC | https://paperswithcode.com/paper/a12c-aa-a-c314a14aeae3e34-e-the-use-of |
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Measuring Non-cooperation in Dialogue
| Title | Measuring Non-cooperation in Dialogue |
| Authors | Brian Pl{"u}ss, Paul Piwek |
| Abstract | This paper introduces a novel method for measuring non-cooperation in dialogue. The key idea is that linguistic non-cooperation can be measured in terms of the extent to which dialogue participants deviate from conventions regarding the proper introduction and discharging of conversational obligations (e.g., the obligation to respond to a question). Previous work on non cooperation has focused mainly on non-linguistic task-related non-cooperation or modelled non-cooperation in terms of special rules describing non-cooperative behaviours. In contrast, we start from rules for normal/correct dialogue behaviour - i.e., a dialogue game - which in principle can be derived from a corpus of cooperative dialogues, and provide a quantitative measure for the degree to which participants comply with these rules. We evaluated the model on a corpus of political interviews, with encouraging results. The model predicts accurately the degree of cooperation for one of the two dialogue game roles (interviewer) and also the relative cooperation for both roles (i.e., which interlocutor in the conversation was most cooperative). Being able to measure cooperation has applications in many areas from the analysis - manual, semi and fully automatic - of natural language interactions to human-like virtual personal assistants, tutoring agents, sophisticated dialogue systems, and role-playing virtual humans. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1181/ |
| https://www.aclweb.org/anthology/C16-1181 | |
| PWC | https://paperswithcode.com/paper/measuring-non-cooperation-in-dialogue |
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MetaMind Neural Machine Translation System for WMT 2016
| Title | MetaMind Neural Machine Translation System for WMT 2016 |
| Authors | James Bradbury, Richard Socher |
| Abstract | |
| Tasks | Data Augmentation, Machine Translation, Tokenization |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2308/ |
| https://www.aclweb.org/anthology/W16-2308 | |
| PWC | https://paperswithcode.com/paper/metamind-neural-machine-translation-system |
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VERSE: Event and Relation Extraction in the BioNLP 2016 Shared Task
| Title | VERSE: Event and Relation Extraction in the BioNLP 2016 Shared Task |
| Authors | Jake Lever, Steven JM Jones |
| Abstract | |
| Tasks | Question Answering, Relation Extraction |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-3005/ |
| https://www.aclweb.org/anthology/W16-3005 | |
| PWC | https://paperswithcode.com/paper/verse-event-and-relation-extraction-in-the |
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Arabic to English Person Name Transliteration using Twitter
| Title | Arabic to English Person Name Transliteration using Twitter |
| Authors | Hamdy Mubarak, Ahmed Abdelali |
| Abstract | Social media outlets are providing new opportunities for harvesting valuable resources. We present a novel approach for mining data from Twitter for the purpose of building transliteration resources and systems. Such resources are crucial in translation and retrieval tasks. We demonstrate the benefits of the approach on Arabic to English transliteration. The contribution of this approach includes the size of data that can be collected and exploited within the span of a limited time; the approach is very generic and can be adopted to other languages and the ability of the approach to cope with new transliteration phenomena and trends. A statistical transliteration system built using this data improved a comparable system built from Wikipedia wikilinks data. |
| Tasks | Transliteration |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1054/ |
| https://www.aclweb.org/anthology/L16-1054 | |
| PWC | https://paperswithcode.com/paper/arabic-to-english-person-name-transliteration |
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Ensemble Classification of Grants using LDA-based Features
| Title | Ensemble Classification of Grants using LDA-based Features |
| Authors | Yannis Korkontzelos, Beverley Thomas, Makoto Miwa, Sophia Ananiadou |
| Abstract | Classifying research grants into useful categories is a vital task for a funding body to give structure to the portfolio for analysis, informing strategic planning and decision-making. Automating this classification process would save time and effort, providing the accuracy of the classifications is maintained. We employ five classification models to classify a set of BBSRC-funded research grants in 21 research topics based on unigrams, technical terms and Latent Dirichlet Allocation models. To boost precision, we investigate methods for combining their predictions into five aggregate classifiers. Evaluation confirmed that ensemble classification models lead to higher precision.It was observed that there is not a single best-performing aggregate method for all research topics. Instead, the best-performing method for a research topic depends on the number of positive training instances available for this topic. Subject matter experts considered the predictions of aggregate models to correct erroneous or incomplete manual assignments. |
| Tasks | Decision Making |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1205/ |
| https://www.aclweb.org/anthology/L16-1205 | |
| PWC | https://paperswithcode.com/paper/ensemble-classification-of-grants-using-lda |
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A Corpus-based Approach for Spanish-Chinese Language Learning
| Title | A Corpus-based Approach for Spanish-Chinese Language Learning |
| Authors | Shuyuan Cao, Iria da Cunha, Mikel Iruskieta |
| Abstract | Due to the huge population that speaks Spanish and Chinese, these languages occupy an important position in the language learning studies. Although there are some automatic translation systems that benefit the learning of both languages, there is enough space to create resources in order to help language learners. As a quick and effective resource that can give large amount language information, corpus-based learning is becoming more and more popular. In this paper we enrich a Spanish-Chinese parallel corpus automatically with part of-speech (POS) information and manually with discourse segmentation (following the Rhetorical Structure Theory (RST) (Mann and Thompson, 1988)). Two search tools allow the Spanish-Chinese language learners to carry out different queries based on tokens and lemmas. The parallel corpus and the research tools are available to the academic community. We propose some examples to illustrate how learners can use the corpus to learn Spanish and Chinese. |
| Tasks | |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4913/ |
| https://www.aclweb.org/anthology/W16-4913 | |
| PWC | https://paperswithcode.com/paper/a-corpus-based-approach-for-spanish-chinese |
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Unsupervised Document Classification with Informed Topic Models
| Title | Unsupervised Document Classification with Informed Topic Models |
| Authors | Timothy Miller, Dmitriy Dligach, Guergana Savova |
| Abstract | |
| Tasks | Active Learning, Document Classification, Topic Models |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2911/ |
| https://www.aclweb.org/anthology/W16-2911 | |
| PWC | https://paperswithcode.com/paper/unsupervised-document-classification-with |
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