Paper Group NANR 139
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification. Book Reviews: Natural Language Processing for Social Media by Atefeh Farzindar and Diana Inkpen. A graphical framework to detect and categorize diverse opinions from online news. Active learning for detection of stance components …
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification
| Title | LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification |
| Authors | David Vilares, Yerai Doval, Miguel A. Alonso, Carlos G{'o}mez-Rodr{'\i}guez |
| Abstract | |
| Tasks | Opinion Mining, Sentiment Analysis, Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1009/ |
| https://www.aclweb.org/anthology/S16-1009 | |
| PWC | https://paperswithcode.com/paper/lys-at-semeval-2016-task-4-exploiting-neural |
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Book Reviews: Natural Language Processing for Social Media by Atefeh Farzindar and Diana Inkpen
| Title | Book Reviews: Natural Language Processing for Social Media by Atefeh Farzindar and Diana Inkpen |
| Authors | Annie Louis |
| Abstract | |
| Tasks | Information Retrieval, Part-Of-Speech Tagging |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/J16-4011/ |
| https://www.aclweb.org/anthology/J16-4011 | |
| PWC | https://paperswithcode.com/paper/book-reviews-natural-language-processing-for |
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A graphical framework to detect and categorize diverse opinions from online news
| Title | A graphical framework to detect and categorize diverse opinions from online news |
| Authors | Ankan Mullick, Pawan Goyal, Niloy Ganguly |
| Abstract | This paper proposes a graphical framework to extract opinionated sentences which highlight different contexts within a given news article by introducing the concept of diversity in a graphical model for opinion detection.We conduct extensive evaluations and find that the proposed modification leads to impressive improvement in performance and makes the final results of the model much more usable. The proposed method (OP-D) not only performs much better than the other techniques used for opinion detection as well as introducing diversity, but is also able to select opinions from different categories (Asher et al. 2009). By developing a classification model which categorizes the identified sentences into various opinion categories, we find that OP-D is able to push opinions from different categories uniformly among the top opinions. |
| Tasks | Opinion Mining |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4305/ |
| https://www.aclweb.org/anthology/W16-4305 | |
| PWC | https://paperswithcode.com/paper/a-graphical-framework-to-detect-and |
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Active learning for detection of stance components
| Title | Active learning for detection of stance components |
| Authors | Maria Skeppstedt, Magnus Sahlgren, Carita Paradis, Andreas Kerren |
| Abstract | Automatic detection of five language components, which are all relevant for expressing opinions and for stance taking, was studied: positive sentiment, negative sentiment, speculation, contrast and condition. A resource-aware approach was taken, which included manual annotation of 500 training samples and the use of limited lexical resources. Active learning was compared to random selection of training data, as well as to a lexicon-based method. Active learning was successful for the categories speculation, contrast and condition, but not for the two sentiment categories, for which results achieved when using active learning were similar to those achieved when applying a random selection of training data. This difference is likely due to a larger variation in how sentiment is expressed than in how speakers express the other three categories. This larger variation was also shown by the lower recall results achieved by the lexicon-based approach for sentiment than for the categories speculation, contrast and condition. |
| Tasks | Active Learning, Opinion Mining, Sentiment Analysis, Stance Detection |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4306/ |
| https://www.aclweb.org/anthology/W16-4306 | |
| PWC | https://paperswithcode.com/paper/active-learning-for-detection-of-stance |
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Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker
| Title | Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker |
| Authors | Delyth Prys, Gruffudd Prys, Dewi Bryn Jones |
| Abstract | This paper describes the use of a free, on-line language spelling and grammar checking aid as a vehicle for the collection of a significant (31 million words and rising) corpus of text for academic research in the context of less resourced languages where such data in sufficient quantities are often unavailable. It describes two versions of the corpus: the texts as submitted, prior to the correction process, and the texts following the user{'}s incorporation of any suggested changes. An overview of the corpus{'} contents is given and an analysis of use including usage statistics is also provided. Issues surrounding privacy and the anonymization of data are explored as is the data{'}s potential use for linguistic analysis, lexical research and language modelling. The method used for gathering this corpus is believed to be unique, and is a valuable addition to corpus studies in a minority language. |
| Tasks | Language Modelling |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1519/ |
| https://www.aclweb.org/anthology/L16-1519 | |
| PWC | https://paperswithcode.com/paper/cysill-ar-lein-a-corpus-of-written |
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Composition of Compound Nouns Using Distributional Semantics
| Title | Composition of Compound Nouns Using Distributional Semantics |
| Authors | Kyra Yee, Jugal Kalita |
| Abstract | |
| Tasks | |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-6304/ |
| https://www.aclweb.org/anthology/W16-6304 | |
| PWC | https://paperswithcode.com/paper/composition-of-compound-nouns-using |
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Synthesizing Compound Words for Machine Translation
| Title | Synthesizing Compound Words for Machine Translation |
| Authors | Austin Matthews, Eva Schlinger, Alon Lavie, Chris Dyer |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1103/ |
| https://www.aclweb.org/anthology/P16-1103 | |
| PWC | https://paperswithcode.com/paper/synthesizing-compound-words-for-machine |
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Reconstructing Ancient Literary Texts from Noisy Manuscripts
| Title | Reconstructing Ancient Literary Texts from Noisy Manuscripts |
| Authors | Moshe Koppel, Moty Michaely, Alex Tal |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0205/ |
| https://www.aclweb.org/anthology/W16-0205 | |
| PWC | https://paperswithcode.com/paper/reconstructing-ancient-literary-texts-from |
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Cross-lingual projection for class-based language models
| Title | Cross-lingual projection for class-based language models |
| Authors | Beat Gfeller, Vlad Schogol, Keith Hall |
| Abstract | |
| Tasks | Language Modelling, Named Entity Recognition, Speech Recognition |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2014/ |
| https://www.aclweb.org/anthology/P16-2014 | |
| PWC | https://paperswithcode.com/paper/cross-lingual-projection-for-class-based |
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Reference Bias in Monolingual Machine Translation Evaluation
| Title | Reference Bias in Monolingual Machine Translation Evaluation |
| Authors | Marina Fomicheva, Lucia Specia |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2013/ |
| https://www.aclweb.org/anthology/P16-2013 | |
| PWC | https://paperswithcode.com/paper/reference-bias-in-monolingual-machine |
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Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary
| Title | Comparison of Grapheme-to-Phoneme Conversion Methods on a Myanmar Pronunciation Dictionary |
| Authors | Ye Kyaw Thu, Win Pa Pa, Yoshinori Sagisaka, Naoto Iwahashi |
| Abstract | Grapheme-to-Phoneme (G2P) conversion is the task of predicting the pronunciation of a word given its graphemic or written form. It is a highly important part of both automatic speech recognition (ASR) and text-to-speech (TTS) systems. In this paper, we evaluate seven G2P conversion approaches: Adaptive Regularization of Weight Vectors (AROW) based structured learning (S-AROW), Conditional Random Field (CRF), Joint-sequence models (JSM), phrase-based statistical machine translation (PBSMT), Recurrent Neural Network (RNN), Support Vector Machine (SVM) based point-wise classification, Weighted Finite-state Transducers (WFST) on a manually tagged Myanmar phoneme dictionary. The G2P bootstrapping experimental results were measured with both automatic phoneme error rate (PER) calculation and also manual checking in terms of voiced/unvoiced, tones, consonant and vowel errors. The result shows that CRF, PBSMT and WFST approaches are the best performing methods for G2P conversion on Myanmar language. |
| Tasks | Active Learning, Machine Translation, Speech Recognition |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3702/ |
| https://www.aclweb.org/anthology/W16-3702 | |
| PWC | https://paperswithcode.com/paper/comparison-of-grapheme-to-phoneme-conversion |
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Predicting sentential semantic compatibility for aggregation in text-to-text generation
| Title | Predicting sentential semantic compatibility for aggregation in text-to-text generation |
| Authors | Victor Chenal, Jackie Chi Kit Cheung |
| Abstract | We examine the task of aggregation in the context of text-to-text generation. We introduce a new aggregation task which frames the process as grouping input sentence fragments into clusters that are to be expressed as a single output sentence. We extract datasets for this task from a corpus using an automatic extraction process. Based on the results of a user study, we develop two gold-standard clusterings and corresponding evaluation methods for each dataset. We present a hierarchical clustering framework for predicting aggregation decisions on this task, which outperforms several baselines and can serve as a reference in future work. |
| Tasks | Sentence Compression, Text Generation, Text Simplification |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1101/ |
| https://www.aclweb.org/anthology/C16-1101 | |
| PWC | https://paperswithcode.com/paper/predicting-sentential-semantic-compatibility |
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Parallel Sentence Compression
| Title | Parallel Sentence Compression |
| Authors | Julia Ive, Fran{\c{c}}ois Yvon |
| Abstract | Sentence compression is a way to perform text simplification and is usually handled in a monolingual setting. In this paper, we study ways to extend sentence compression in a bilingual context, where the goal is to obtain parallel compressions of parallel sentences. This can be beneficial for a series of multilingual natural language processing (NLP) tasks. We compare two ways to take bilingual information into account when compressing parallel sentences. Their efficiency is contrasted on a parallel corpus of News articles. |
| Tasks | Machine Translation, Semantic Role Labeling, Sentence Compression, Text Simplification |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1142/ |
| https://www.aclweb.org/anthology/C16-1142 | |
| PWC | https://paperswithcode.com/paper/parallel-sentence-compression |
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Hybrid Question Answering over Knowledge Base and Free Text
| Title | Hybrid Question Answering over Knowledge Base and Free Text |
| Authors | Kun Xu, Yansong Feng, Songfang Huang, Dongyan Zhao |
| Abstract | Recent trend in question answering (QA) systems focuses on using structured knowledge bases (KBs) to find answers. While these systems are able to provide more precise answers than information retrieval (IR) based QA systems, the natural incompleteness of KB inevitably limits the question scope that the system can answer. In this paper, we present a hybrid question answering (hybrid-QA) system which exploits both structured knowledge base and free text to answer a question. The main challenge is to recognize the meaning of a question using these two resources, i.e., structured KB and free text. To address this, we map relational phrases to KB predicates and textual relations simultaneously, and further develop an integer linear program (ILP) model to infer on these candidates and provide a globally optimal solution. Experiments on benchmark datasets show that our system can benefit from both structured KB and free text, outperforming the state-of-the-art systems. |
| Tasks | Information Retrieval, Question Answering, Semantic Parsing |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1226/ |
| https://www.aclweb.org/anthology/C16-1226 | |
| PWC | https://paperswithcode.com/paper/hybrid-question-answering-over-knowledge-base |
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Context-aware Argumentative Relation Mining
| Title | Context-aware Argumentative Relation Mining |
| Authors | Huy Nguyen, Diane Litman |
| Abstract | |
| Tasks | Argument Mining, Document Summarization, Opinion Mining, Relation Classification |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1107/ |
| https://www.aclweb.org/anthology/P16-1107 | |
| PWC | https://paperswithcode.com/paper/context-aware-argumentative-relation-mining |
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