Paper Group NANR 163
Structure-Blind Signal Recovery. Linguistic Benchmarks of Online News Article Quality. ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale. Distinguishing Past, On-going, and Future Events: The EventStatus Corpus. TranscRater: a Tool for Automatic Speech Recognition Quality Estimation. Predicting the Compositionality of Nominal Comp …
Structure-Blind Signal Recovery
| Title | Structure-Blind Signal Recovery |
| Authors | Dmitry Ostrovsky, Zaid Harchaoui, Anatoli Juditsky, Arkadi S. Nemirovski |
| Abstract | We consider the problem of recovering a signal observed in Gaussian noise. If the set of signals is convex and compact, and can be specified beforehand, one can use classical linear estimators that achieve a risk within a constant factor of the minimax risk. However, when the set is unspecified, designing an estimator that is blind to the hidden structure of the signal remains a challenging problem. We propose a new family of estimators to recover signals observed in Gaussian noise. Instead of specifying the set where the signal lives, we assume the existence of a well-performing linear estimator. Proposed estimators enjoy exact oracle inequalities and can be efficiently computed through convex optimization. We present several numerical illustrations that show the potential of the approach. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6063-structure-blind-signal-recovery |
| http://papers.nips.cc/paper/6063-structure-blind-signal-recovery.pdf | |
| PWC | https://paperswithcode.com/paper/structure-blind-signal-recovery |
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Linguistic Benchmarks of Online News Article Quality
| Title | Linguistic Benchmarks of Online News Article Quality |
| Authors | Ioannis Arapakis, Filipa Peleja, Barla Berkant, Joao Magalhaes |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1178/ |
| https://www.aclweb.org/anthology/P16-1178 | |
| PWC | https://paperswithcode.com/paper/linguistic-benchmarks-of-online-news-article |
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ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale
| Title | ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale |
| Authors | Andrea Esuli |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1011/ |
| https://www.aclweb.org/anthology/S16-1011 | |
| PWC | https://paperswithcode.com/paper/isti-cnr-at-semeval-2016-task-4 |
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Distinguishing Past, On-going, and Future Events: The EventStatus Corpus
| Title | Distinguishing Past, On-going, and Future Events: The EventStatus Corpus |
| Authors | Ruihong Huang, Ignacio Cases, Dan Jurafsky, Cleo Condoravdi, Ellen Riloff |
| Abstract | |
| Tasks | |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1005/ |
| https://www.aclweb.org/anthology/D16-1005 | |
| PWC | https://paperswithcode.com/paper/distinguishing-past-on-going-and-future |
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TranscRater: a Tool for Automatic Speech Recognition Quality Estimation
| Title | TranscRater: a Tool for Automatic Speech Recognition Quality Estimation |
| Authors | Shahab Jalalvand, Matteo Negri, Marco Turchi, José G. C. de Souza, Falavigna Daniele, Mohammed R. H. Qwaider |
| Abstract | |
| Tasks | Machine Translation, Question Answering, Speech Recognition |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/papers/P16-4008/p16-4008 |
| https://www.aclweb.org/anthology/P16-4008 | |
| PWC | https://paperswithcode.com/paper/transcrater-a-tool-for-automatic-speech |
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Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time
| Title | Predicting the Compositionality of Nominal Compounds: Giving Word Embeddings a Hard Time |
| Authors | Silvio Cordeiro, Carlos Ramisch, Marco Idiart, Aline Villavicencio |
| Abstract | |
| Tasks | Lemmatization, Machine Translation, Semantic Parsing, Word Embeddings, Word Sense Disambiguation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1187/ |
| https://www.aclweb.org/anthology/P16-1187 | |
| PWC | https://paperswithcode.com/paper/predicting-the-compositionality-of-nominal |
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Sentence Embedding Evaluation Using Pyramid Annotation
| Title | Sentence Embedding Evaluation Using Pyramid Annotation |
| Authors | Tal Baumel, Raphael Cohen, Michael Elhadad |
| Abstract | |
| Tasks | Natural Language Inference, Semantic Role Labeling, Sentence Embedding, Sentiment Analysis, Word Embeddings |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2526/ |
| https://www.aclweb.org/anthology/W16-2526 | |
| PWC | https://paperswithcode.com/paper/sentence-embedding-evaluation-using-pyramid |
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DART: a Dataset of Arguments and their Relations on Twitter
| Title | DART: a Dataset of Arguments and their Relations on Twitter |
| Authors | Tom Bosc, Elena Cabrio, Serena Villata |
| Abstract | The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other. |
| Tasks | Argument Mining |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1200/ |
| https://www.aclweb.org/anthology/L16-1200 | |
| PWC | https://paperswithcode.com/paper/dart-a-dataset-of-arguments-and-their |
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System Description of bjtu_nlp Neural Machine Translation System
| Title | System Description of bjtu_nlp Neural Machine Translation System |
| Authors | Shaotong Li, JinAn Xu, Yufeng Chen, Yujie Zhang |
| Abstract | This paper presents our machine translation system that developed for the WAT2016 evalua-tion tasks of ja-en, ja-zh, en-ja, zh-ja, JPCja-en, JPCja-zh, JPCen-ja, JPCzh-ja. We build our system based on encoder{–}decoder framework by integrating recurrent neural network (RNN) and gate recurrent unit (GRU), and we also adopt an attention mechanism for solving the problem of information loss. Additionally, we propose a simple translation-specific approach to resolve the unknown word translation problem. Experimental results show that our system performs better than the baseline statistical machine translation (SMT) systems in each task. Moreover, it shows that our proposed approach of unknown word translation performs effec-tively improvement of translation results. |
| Tasks | Machine Translation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4608/ |
| https://www.aclweb.org/anthology/W16-4608 | |
| PWC | https://paperswithcode.com/paper/system-description-of-bjtu_nlp-neural-machine |
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Double Topic Shifts in Open Domain Conversations: Natural Language Interface for a Wikipedia-based Robot Application
| Title | Double Topic Shifts in Open Domain Conversations: Natural Language Interface for a Wikipedia-based Robot Application |
| Authors | Kristiina Jokinen, Graham Wilcock |
| Abstract | The paper describes topic shifting in dialogues with a robot that provides information from Wiki-pedia. The work focuses on a double topical construction of dialogue coherence which refers to discourse coherence on two levels: the evolution of dialogue topics via the interaction between the user and the robot system, and the creation of discourse topics via the content of the Wiki-pedia article itself. The user selects topics that are of interest to her, and the system builds a list of potential topics, anticipated to be the next topic, by the links in the article and by the keywords extracted from the article. The described system deals with Wikipedia articles, but could easily be adapted to other digital information providing systems. |
| Tasks | Chatbot, Goal-Oriented Dialogue Systems, Question Answering |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4408/ |
| https://www.aclweb.org/anthology/W16-4408 | |
| PWC | https://paperswithcode.com/paper/double-topic-shifts-in-open-domain |
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Evaluating Sentiment Analysis in the Context of Securities Trading
| Title | Evaluating Sentiment Analysis in the Context of Securities Trading |
| Authors | Siavash Kazemian, Shunan Zhao, Gerald Penn |
| Abstract | |
| Tasks | Sentiment Analysis |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1197/ |
| https://www.aclweb.org/anthology/P16-1197 | |
| PWC | https://paperswithcode.com/paper/evaluating-sentiment-analysis-in-the-context |
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How Do I Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles
| Title | How Do I Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles |
| Authors | Yu-Lun Hsieh, Yung-Chun Chang, Chun-Han Chu, Wen-Lian Hsu |
| Abstract | |
| Tasks | Emotion Classification, Opinion Mining, Sentiment Analysis |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-6211/ |
| https://www.aclweb.org/anthology/W16-6211 | |
| PWC | https://paperswithcode.com/paper/how-do-i-look-publicity-mining-from |
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Weakly Supervised Tweet Stance Classification by Relational Bootstrapping
| Title | Weakly Supervised Tweet Stance Classification by Relational Bootstrapping |
| Authors | Javid Ebrahimi, Dejing Dou, Daniel Lowd |
| Abstract | |
| Tasks | Relational Reasoning, Text Classification |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1105/ |
| https://www.aclweb.org/anthology/D16-1105 | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-tweet-stance-classification |
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Exploiting Source-side Monolingual Data in Neural Machine Translation
| Title | Exploiting Source-side Monolingual Data in Neural Machine Translation |
| Authors | Jiajun Zhang, Chengqing Zong |
| Abstract | |
| Tasks | Machine Translation, Multi-Task Learning |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1160/ |
| https://www.aclweb.org/anthology/D16-1160 | |
| PWC | https://paperswithcode.com/paper/exploiting-source-side-monolingual-data-in |
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Exploitation of Co-reference in Distributional Semantics
| Title | Exploitation of Co-reference in Distributional Semantics |
| Authors | Dominik Schlechtweg |
| Abstract | The aim of distributional semantics is to model the similarity of the meaning of words via the words they occur with. Thereby, it relies on the distributional hypothesis implying that similar words have similar contexts. Deducing meaning from the distribution of words is interesting as it can be done automatically on large amounts of freely available raw text. It is because of this convenience that most current state-of-the-art-models of distributional semantics operate on raw text, although there have been successful attempts to integrate other kinds of―e.g., syntactic―information to improve distributional semantic models. In contrast, less attention has been paid to semantic information in the research community. One reason for this is that the extraction of semantic information from raw text is a complex, elaborate matter and in great parts not yet satisfyingly solved. Recently, however, there have been successful attempts to integrate a certain kind of semantic information, i.e., co-reference. Two basically different kinds of information contributed by co-reference with respect to the distribution of words will be identified. We will then focus on one of these and examine its general potential to improve distributional semantic models as well as certain more specific hypotheses. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1022/ |
| https://www.aclweb.org/anthology/L16-1022 | |
| PWC | https://paperswithcode.com/paper/exploitation-of-co-reference-in |
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