Paper Group NANR 19
The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus. Automatic Identification of Narrative Diegesis and Point of View. An Information Foraging Approach to Determining the Number of Relevant Features. Aligning Texts and Knowledge Bases with Semantic Sentence Simplification. DeepLife: An Entity-aware Search, Analytics and Exploration Pl …
The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus
| Title | The ILMT-s2s Corpus ― A Multimodal Interlingual Map Task Corpus |
| Authors | Akira Hayakawa, Saturnino Luz, Loredana Cerrato, Nick Campbell |
| Abstract | This paper presents the multimodal Interlingual Map Task Corpus (ILMT-s2s corpus) collected at Trinity College Dublin, and discuss some of the issues related to the collection and analysis of the data. The corpus design is inspired by the HCRC Map Task Corpus which was initially designed to support the investigation of linguistic phenomena, and has been the focus of a variety of studies of communicative behaviour. The simplicity of the task, and the complexity of phenomena it can elicit, make the map task an ideal object of study. Although there are studies that used replications of the map task to investigate communication in computer mediated tasks, this ILMT-s2s corpus is, to the best of our knowledge, the first investigation of communicative behaviour in the presence of three additional {``}filters{''}: Automatic Speech Recognition (ASR), Machine Translation (MT) and Text To Speech (TTS) synthesis, where the instruction giver and the instruction follower speak different languages. This paper details the data collection setup and completed annotation of the ILMT-s2s corpus, and outlines preliminary results obtained from the data. | |
| Tasks | Machine Translation, Speech Recognition |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1096/ |
| https://www.aclweb.org/anthology/L16-1096 | |
| PWC | https://paperswithcode.com/paper/the-ilmt-s2s-corpus-a-a-multimodal |
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Automatic Identification of Narrative Diegesis and Point of View
| Title | Automatic Identification of Narrative Diegesis and Point of View |
| Authors | Joshua Eisenberg, Mark Finlayson |
| Abstract | |
| Tasks | Natural Language Inference |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-5705/ |
| https://www.aclweb.org/anthology/W16-5705 | |
| PWC | https://paperswithcode.com/paper/automatic-identification-of-narrative |
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An Information Foraging Approach to Determining the Number of Relevant Features
| Title | An Information Foraging Approach to Determining the Number of Relevant Features |
| Authors | Brian Connolly, Benjamin Glass, John Pestian |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2923/ |
| https://www.aclweb.org/anthology/W16-2923 | |
| PWC | https://paperswithcode.com/paper/an-information-foraging-approach-to |
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Aligning Texts and Knowledge Bases with Semantic Sentence Simplification
| Title | Aligning Texts and Knowledge Bases with Semantic Sentence Simplification |
| Authors | Yassine Mrabet, Pavlos Vougiouklis, Halil Kilicoglu, Claire Gardent, Dina Demner-Fushman, Jonathon Hare, Elena Simperl |
| Abstract | |
| Tasks | Information Retrieval, Text Generation, Text Simplification |
| Published | 2016-09-01 |
| URL | https://www.aclweb.org/anthology/W16-3506/ |
| https://www.aclweb.org/anthology/W16-3506 | |
| PWC | https://paperswithcode.com/paper/aligning-texts-and-knowledge-bases-with |
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DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences
| Title | DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences |
| Authors | Patrick Ernst, Amy Siu, Dragan Milchevski, Johannes Hoffart, Gerhard Weikum |
| Abstract | |
| Tasks | Entity Linking |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-4004/ |
| https://www.aclweb.org/anthology/P16-4004 | |
| PWC | https://paperswithcode.com/paper/deeplife-an-entity-aware-search-analytics-and |
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Neural Morphological Analysis: Encoding-Decoding Canonical Segments
| Title | Neural Morphological Analysis: Encoding-Decoding Canonical Segments |
| Authors | Katharina Kann, Ryan Cotterell, Hinrich Sch{"u}tze |
| Abstract | |
| Tasks | Keyword Spotting, Machine Translation, Morphological Analysis, Speech Recognition |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1097/ |
| https://www.aclweb.org/anthology/D16-1097 | |
| PWC | https://paperswithcode.com/paper/neural-morphological-analysis-encoding |
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Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)
| Title | Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP) |
| Authors | |
| Abstract | |
| Tasks | |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4200/ |
| https://www.aclweb.org/anthology/W16-4200 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-clinical-natural-language |
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Synset Ranking of Hindi WordNet
| Title | Synset Ranking of Hindi WordNet |
| Authors | Sudha Bhingardive, Rajita Shukla, Jaya Saraswati, Laxmi Kashyap, Dhirendra Singh, Pushpak Bhattacharyya |
| Abstract | Word Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining the sense of a word in a particular context. It has been observed that such approaches often find it difficult to beat the WordNet First Sense (WFS) baseline which assigns the sense irrespective of context. In this paper, we present our work on creating the WFS baseline for Hindi language by manually ranking the synsets of Hindi WordNet. A ranking tool is developed where human experts can see the frequency of the word senses in the sense-tagged corpora and have been asked to rank the senses of a word by using this information and also his/her intuition. The accuracy of WFS baseline is tested on several standard datasets. F-score is found to be 60{%}, 65{%} and 55{%} on Health, Tourism and News datasets respectively. The created rankings can also be used in other NLP applications viz., Machine Translation, Information Retrieval, Text Summarization, etc. |
| Tasks | Information Retrieval, Machine Translation, Text Summarization, Word Sense Disambiguation |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1485/ |
| https://www.aclweb.org/anthology/L16-1485 | |
| PWC | https://paperswithcode.com/paper/synset-ranking-of-hindi-wordnet |
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Proceedings of the 5th Workshop on Automated Knowledge Base Construction
| Title | Proceedings of the 5th Workshop on Automated Knowledge Base Construction |
| Authors | |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1300/ |
| https://www.aclweb.org/anthology/W16-1300 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-5th-workshop-on-automated |
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Speech Trax: A Bottom to the Top Approach for Speaker Tracking and Indexing in an Archiving Context
| Title | Speech Trax: A Bottom to the Top Approach for Speaker Tracking and Indexing in an Archiving Context |
| Authors | F{'e}licien Vallet, Jim Uro, J{'e}r{'e}my Andriamakaoly, Hakim Nabi, Mathieu Derval, Jean Carrive |
| Abstract | With the increasing amount of audiovisual and digital data deriving from televisual and radiophonic sources, professional archives such as INA, France{'}s national audiovisual institute, acknowledge a growing need for efficient indexing tools. In this paper, we describe the Speech Trax system that aims at analyzing the audio content of TV and radio documents. In particular, we focus on the speaker tracking task that is very valuable for indexing purposes. First, we detail the overall architecture of the system and show the results obtained on a large-scale experiment, the largest to our knowledge for this type of content (about 1,300 speakers). Then, we present the Speech Trax demonstrator that gathers the results of various automatic speech processing techniques on top of our speaker tracking system (speaker diarization, speech transcription, etc.). Finally, we provide insight on the obtained performances and suggest hints for future improvements. |
| Tasks | Speaker Diarization |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1318/ |
| https://www.aclweb.org/anthology/L16-1318 | |
| PWC | https://paperswithcode.com/paper/speech-trax-a-bottom-to-the-top-approach-for |
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Antecedent Selection for Sluicing: Structure and Content
| Title | Antecedent Selection for Sluicing: Structure and Content |
| Authors | Pranav Anand, Daniel Hardt |
| Abstract | |
| Tasks | Question Answering |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/papers/D16-1131/d16-1131 |
| https://www.aclweb.org/anthology/D16-1131 | |
| PWC | https://paperswithcode.com/paper/antecedent-selection-for-sluicing-structure |
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Wikipedia Titles As Noun Tag Predictors
| Title | Wikipedia Titles As Noun Tag Predictors |
| Authors | Armin Hoenen |
| Abstract | In this paper, we investigate a covert labeling cue, namely the probability that a title (by example of the Wikipedia titles) is a noun. If this probability is very large, any list such as or comparable to the Wikipedia titles can be used as a reliable word-class (or part-of-speech tag) predictor or noun lexicon. This may be especially useful in the case of Low Resource Languages (LRL) where labeled data is lacking and putatively for Natural Language Processing (NLP) tasks such as Word Sense Disambiguation, Sentiment Analysis and Machine Translation. Profitting from the ease of digital publication on the web as opposed to print, LRL speaker communities produce resources such as Wikipedia and Wiktionary, which can be used for an assessment. We provide statistical evidence for a strong noun bias for the Wikipedia titles from 2 corpora (English, Persian) and a dictionary (Japanese) and for a typologically balanced set of 17 languages including LRLs. Additionally, we conduct a small experiment on predicting noun tags for out-of-vocabulary items in part-of-speech tagging for English. |
| Tasks | Machine Translation, Part-Of-Speech Tagging, Sentiment Analysis, Word Sense Disambiguation |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1335/ |
| https://www.aclweb.org/anthology/L16-1335 | |
| PWC | https://paperswithcode.com/paper/wikipedia-titles-as-noun-tag-predictors |
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TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features
| Title | TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features |
| Authors | Francesco Ronzano, Ahmed Abura{'}ed, Luis Espinosa-Anke, Horacio Saggion |
| Abstract | |
| Tasks | Complex Word Identification, Lexical Simplification |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1157/ |
| https://www.aclweb.org/anthology/S16-1157 | |
| PWC | https://paperswithcode.com/paper/taln-at-semeval-2016-task-11-modelling |
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MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification
| Title | MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification |
| Authors | Marcos Zampieri, Liling Tan, Josef van Genabith |
| Abstract | |
| Tasks | Complex Word Identification, Lexical Simplification, Text Simplification |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1155/ |
| https://www.aclweb.org/anthology/S16-1155 | |
| PWC | https://paperswithcode.com/paper/macsaar-at-semeval-2016-task-11-zipfian-and |
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Don’t Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation
| Title | Don’t Mention the Shoe! A Learning to Rank Approach to Content Selection for Image Description Generation |
| Authors | Josiah Wang, Robert Gaizauskas |
| Abstract | |
| Tasks | Image Retrieval, Learning-To-Rank, Text Generation |
| Published | 2016-09-01 |
| URL | https://www.aclweb.org/anthology/W16-6631/ |
| https://www.aclweb.org/anthology/W16-6631 | |
| PWC | https://paperswithcode.com/paper/dont-mention-the-shoe-a-learning-to-rank |
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