Paper Group NANR 61
DALILA: The Dialectal Arabic Linguistic Learning Assistant. Reducing lexical complexity as a tool to increase text accessibility for children with dyslexia. YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network. Korean TimeML and Korean TimeBank. Parameter estimation of Japanese predicate argu …
DALILA: The Dialectal Arabic Linguistic Learning Assistant
| Title | DALILA: The Dialectal Arabic Linguistic Learning Assistant |
| Authors | Salam Khalifa, Houda Bouamor, Nizar Habash |
| Abstract | Dialectal Arabic (DA) poses serious challenges for Natural Language Processing (NLP). The number and sophistication of tools and datasets in DA are very limited in comparison to Modern Standard Arabic (MSA) and other languages. MSA tools do not effectively model DA which makes the direct use of MSA NLP tools for handling dialects impractical. This is particularly a challenge for the creation of tools to support learning Arabic as a living language on the web, where authentic material can be found in both MSA and DA. In this paper, we present the Dialectal Arabic Linguistic Learning Assistant (DALILA), a Chrome extension that utilizes cutting-edge Arabic dialect NLP research to assist learners and non-native speakers in understanding text written in either MSA or DA. DALILA provides dialectal word analysis and English gloss corresponding to each word. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1175/ |
| https://www.aclweb.org/anthology/L16-1175 | |
| PWC | https://paperswithcode.com/paper/dalila-the-dialectal-arabic-linguistic |
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Reducing lexical complexity as a tool to increase text accessibility for children with dyslexia
| Title | Reducing lexical complexity as a tool to increase text accessibility for children with dyslexia |
| Authors | N{'u}ria Gala, Johannes Ziegler |
| Abstract | Lexical complexity plays a central role in readability, particularly for dyslexic children and poor readers because of their slow and laborious decoding and word recognition skills. Although some features to aid readability may be common to most languages (e.g., the majority of {`}easy{'} words are of low frequency), we believe that lexical complexity is mainly language-specific. In this paper, we define lexical complexity for French and we present a pilot study on the effects of text simplification in dyslexic children. The participants were asked to read out loud original and manually simplified versions of a standardized French text corpus and to answer comprehension questions after reading each text. The analysis of the results shows that the simplifications performed were beneficial in terms of reading speed and they reduced the number of reading errors (mainly lexical ones) without a loss in comprehension. Although the number of participants in this study was rather small (N=10), the results are promising and contribute to the development of applications in computational linguistics. | |
| Tasks | Reading Comprehension, Text Simplification |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4107/ |
| https://www.aclweb.org/anthology/W16-4107 | |
| PWC | https://paperswithcode.com/paper/reducing-lexical-complexity-as-a-tool-to |
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YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network
| Title | YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network |
| Authors | Yunchao He, Liang-Chih Yu, Chin-Sheng Yang, K. Robert Lai, Weiyi Liu |
| Abstract | |
| Tasks | Sentiment Analysis, Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1039/ |
| https://www.aclweb.org/anthology/S16-1039 | |
| PWC | https://paperswithcode.com/paper/yzu-nlp-team-at-semeval-2016-task-4-ordinal |
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Korean TimeML and Korean TimeBank
| Title | Korean TimeML and Korean TimeBank |
| Authors | Young-Seob Jeong, Won-Tae Joo, Hyun-Woo Do, Chae-Gyun Lim, Key-Sun Choi, Ho-Jin Choi |
| Abstract | Many emerging documents usually contain temporal information. Because the temporal information is useful for various applications, it became important to develop a system of extracting the temporal information from the documents. Before developing the system, it first necessary to define or design the structure of temporal information. In other words, it is necessary to design a language which defines how to annotate the temporal information. There have been some studies about the annotation languages, but most of them was applicable to only a specific target language (e.g., English). Thus, it is necessary to design an individual annotation language for each language. In this paper, we propose a revised version of Koreain Time Mark-up Language (K-TimeML), and also introduce a dataset, named Korean TimeBank, that is constructed basd on the K-TimeML. We believe that the new K-TimeML and Korean TimeBank will be used in many further researches about extraction of temporal information. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1055/ |
| https://www.aclweb.org/anthology/L16-1055 | |
| PWC | https://paperswithcode.com/paper/korean-timeml-and-korean-timebank |
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Parameter estimation of Japanese predicate argument structure analysis model using eye gaze information
| Title | Parameter estimation of Japanese predicate argument structure analysis model using eye gaze information |
| Authors | Ryosuke Maki, Hitoshi Nishikawa, Takenobu Tokunaga |
| Abstract | In this paper, we propose utilising eye gaze information for estimating parameters of a Japanese predicate argument structure (PAS) analysis model. We employ not only linguistic information in the text, but also the information of annotator eye gaze during their annotation process. We hypothesise that annotator{'}s frequent looks at certain candidates imply their plausibility of being the argument of the predicate. Based on this hypothesis, we consider annotator eye gaze for estimating the model parameters of the PAS analysis. The evaluation experiment showed that introducing eye gaze information increased the accuracy of the PAS analysis by 0.05 compared with the conventional methods. |
| Tasks | |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1269/ |
| https://www.aclweb.org/anthology/C16-1269 | |
| PWC | https://paperswithcode.com/paper/parameter-estimation-of-japanese-predicate |
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Enhancing Access to Online Education: Quality Machine Translation of MOOC Content
| Title | Enhancing Access to Online Education: Quality Machine Translation of MOOC Content |
| Authors | Valia Kordoni, Antal van den Bosch, Katia Lida Kermanidis, Vilelmini Sosoni, Kostadin Cholakov, Iris Hendrickx, Matthias Huck, Andy Way |
| Abstract | The present work is an overview of the TraMOOC (Translation for Massive Open Online Courses) research and innovation project, a machine translation approach for online educational content. More specifically, videolectures, assignments, and MOOC forum text is automatically translated from English into eleven European and BRIC languages. Unlike previous approaches to machine translation, the output quality in TraMOOC relies on a multimodal evaluation schema that involves crowdsourcing, error type markup, an error taxonomy for translation model comparison, and implicit evaluation via text mining, i.e. entity recognition and its performance comparison between the source and the translated text, and sentiment analysis on the students{'} forum posts. Finally, the evaluation output will result in more and better quality in-domain parallel data that will be fed back to the translation engine for higher quality output. The translation service will be incorporated into the Iversity MOOC platform and into the VideoLectures.net digital library portal. |
| Tasks | Machine Translation, Sentiment Analysis |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1003/ |
| https://www.aclweb.org/anthology/L16-1003 | |
| PWC | https://paperswithcode.com/paper/enhancing-access-to-online-education-quality |
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Topicality-Based Indices for Essay Scoring
| Title | Topicality-Based Indices for Essay Scoring |
| Authors | Beata Beigman Klebanov, Michael Flor, Binod Gyawali |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0507/ |
| https://www.aclweb.org/anthology/W16-0507 | |
| PWC | https://paperswithcode.com/paper/topicality-based-indices-for-essay-scoring |
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AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding
| Title | AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding |
| Authors | Sanjay S.P, An Kumar M, , Soman K P |
| Abstract | |
| Tasks | Complex Word Identification, Lexical Simplification, Text Simplification |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1159/ |
| https://www.aclweb.org/anthology/S16-1159 | |
| PWC | https://paperswithcode.com/paper/amritacen-at-semeval-2016-task-11-complex |
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Improving the Annotation of Sentence Specificity
| Title | Improving the Annotation of Sentence Specificity |
| Authors | Junyi Jessy Li, Bridget O{'}Daniel, Yi Wu, Wenli Zhao, Ani Nenkova |
| Abstract | We introduce improved guidelines for annotation of sentence specificity, addressing the issues encountered in prior work. Our annotation provides judgements of sentences in context. Rather than binary judgements, we introduce a specificity scale which accommodates nuanced judgements. Our augmented annotation procedure also allows us to define where in the discourse context the lack of specificity can be resolved. In addition, the cause of the underspecification is annotated in the form of free text questions. We present results from a pilot annotation with this new scheme and demonstrate good inter-annotator agreement. We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context. We find that missing details that are not resolved in the the prior context are more likely to trigger questions about the reason behind events, {}why{''} and {}how{''}. Our data is accessible at http://www.cis.upenn.edu/{\textasciitilde}nlp/corpora/lrec16spec.html |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1620/ |
| https://www.aclweb.org/anthology/L16-1620 | |
| PWC | https://paperswithcode.com/paper/improving-the-annotation-of-sentence |
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Domain-Specific Corpus Expansion with Focused Webcrawling
| Title | Domain-Specific Corpus Expansion with Focused Webcrawling |
| Authors | Steffen Remus, Chris Biemann |
| Abstract | This work presents a straightforward method for extending or creating in-domain web corpora by focused webcrawling. The focused webcrawler uses statistical N-gram language models to estimate the relatedness of documents and weblinks and needs as input only N-grams or plain texts of a predefined domain and seed URLs as starting points. Two experiments demonstrate that our focused crawler is able to stay focused in domain and language. The first experiment shows that the crawler stays in a focused domain, the second experiment demonstrates that language models trained on focused crawls obtain better perplexity scores on in-domain corpora. We distribute the focused crawler as open source software. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1572/ |
| https://www.aclweb.org/anthology/L16-1572 | |
| PWC | https://paperswithcode.com/paper/domain-specific-corpus-expansion-with-focused |
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LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers
| Title | LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers |
| Authors | Julien Tourille, Olivier Ferret, Aur{'e}lie N{'e}v{'e}ol, Xavier Tannier |
| Abstract | |
| Tasks | Entity Extraction, Relation Extraction, Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1175/ |
| https://www.aclweb.org/anthology/S16-1175 | |
| PWC | https://paperswithcode.com/paper/limsi-cot-at-semeval-2016-task-12-temporal |
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UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing
| Title | UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing |
| Authors | Xiaochang Peng, Daniel Gildea |
| Abstract | |
| Tasks | Amr Parsing, Machine Translation, Question Answering |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1183/ |
| https://www.aclweb.org/anthology/S16-1183 | |
| PWC | https://paperswithcode.com/paper/uofr-at-semeval-2016-task-8-learning |
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The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer
| Title | The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer |
| Authors | Johannes Bjerva, Johan Bos, Hessel Haagsma |
| Abstract | |
| Tasks | Semantic Parsing |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1182/ |
| https://www.aclweb.org/anthology/S16-1182 | |
| PWC | https://paperswithcode.com/paper/the-meaning-factory-at-semeval-2016-task-8 |
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GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives
| Title | GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives |
| Authors | Arman Cohan, Kevin Meurer, Nazli Goharian |
| Abstract | |
| Tasks | Information Retrieval |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1194/ |
| https://www.aclweb.org/anthology/S16-1194 | |
| PWC | https://paperswithcode.com/paper/guir-at-semeval-2016-task-12-temporal |
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KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records
| Title | KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records |
| Authors | Artuur Leeuwenberg, Marie-Francine Moens |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1199/ |
| https://www.aclweb.org/anthology/S16-1199 | |
| PWC | https://paperswithcode.com/paper/kuleuven-liir-at-semeval-2016-task-12 |
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