Paper Group NANR 167
Adverse Drug Reaction Classification With Deep Neural Networks. INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words. Convergence of Syntactic Complexity in Conversation. A Novel Measure for Coherence in Statistical Topic Models. Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data. Efficient Data Selection …
Adverse Drug Reaction Classification With Deep Neural Networks
| Title | Adverse Drug Reaction Classification With Deep Neural Networks |
| Authors | Trung Huynh, Yulan He, Alistair Willis, Stefan Rueger |
| Abstract | We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1084/ |
| https://www.aclweb.org/anthology/C16-1084 | |
| PWC | https://paperswithcode.com/paper/adverse-drug-reaction-classification-with |
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INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words
| Title | INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words |
| Authors | Silvio Amir, Ramon Astudillo, Wang Ling, M{'a}rio J. Silva, Isabel Trancoso |
| Abstract | |
| Tasks | Feature Engineering, Sentiment Analysis, Twitter Sentiment Analysis, Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1036/ |
| https://www.aclweb.org/anthology/S16-1036 | |
| PWC | https://paperswithcode.com/paper/inesc-id-at-semeval-2016-task-4-a-reducing |
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Convergence of Syntactic Complexity in Conversation
| Title | Convergence of Syntactic Complexity in Conversation |
| Authors | Yang Xu, David Reitter |
| Abstract | |
| Tasks | Language Acquisition |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2072/ |
| https://www.aclweb.org/anthology/P16-2072 | |
| PWC | https://paperswithcode.com/paper/convergence-of-syntactic-complexity-in |
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A Novel Measure for Coherence in Statistical Topic Models
| Title | A Novel Measure for Coherence in Statistical Topic Models |
| Authors | Fred Morstatter, Huan Liu |
| Abstract | |
| Tasks | Topic Models |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2088/ |
| https://www.aclweb.org/anthology/P16-2088 | |
| PWC | https://paperswithcode.com/paper/a-novel-measure-for-coherence-in-statistical |
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Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data
| Title | Weakly Supervised Part-of-speech Tagging Using Eye-tracking Data |
| Authors | Maria Barrett, Joachim Bingel, Frank Keller, Anders S{\o}gaard |
| Abstract | |
| Tasks | Eye Tracking, Part-Of-Speech Tagging |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2094/ |
| https://www.aclweb.org/anthology/P16-2094 | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-part-of-speech-tagging |
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Efficient Data Selection for Bilingual Terminology Extraction from Comparable Corpora
| Title | Efficient Data Selection for Bilingual Terminology Extraction from Comparable Corpora |
| Authors | Amir Hazem, Emmanuel Morin |
| Abstract | Comparable corpora are the main alternative to the use of parallel corpora to extract bilingual lexicons. Although it is easier to build comparable corpora, specialized comparable corpora are often of modest size in comparison with corpora issued from the general domain. Consequently, the observations of word co-occurrences which are the basis of context-based methods are unreliable. We propose in this article to improve word co-occurrences of specialized comparable corpora and thus context representation by using general-domain data. This idea, which has been already used in machine translation task for more than a decade, is not straightforward for the task of bilingual lexicon extraction from specific-domain comparable corpora. We go against the mainstream of this task where many studies support the idea that adding out-of-domain documents decreases the quality of lexicons. Our empirical evaluation shows the advantages of this approach which induces a significant gain in the accuracy of extracted lexicons. |
| Tasks | Machine Translation, Topic Models, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1321/ |
| https://www.aclweb.org/anthology/C16-1321 | |
| PWC | https://paperswithcode.com/paper/efficient-data-selection-for-bilingual |
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Black Holes and White Rabbits: Metaphor Identification with Visual Features
| Title | Black Holes and White Rabbits: Metaphor Identification with Visual Features |
| Authors | Ekaterina Shutova, Douwe Kiela, Jean Maillard |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1020/ |
| https://www.aclweb.org/anthology/N16-1020 | |
| PWC | https://paperswithcode.com/paper/black-holes-and-white-rabbits-metaphor |
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Singleton Detection using Word Embeddings and Neural Networks
| Title | Singleton Detection using Word Embeddings and Neural Networks |
| Authors | Hessel Haagsma |
| Abstract | |
| Tasks | Coreference Resolution, Word Embeddings |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-3010/ |
| https://www.aclweb.org/anthology/P16-3010 | |
| PWC | https://paperswithcode.com/paper/singleton-detection-using-word-embeddings-and |
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Identifying Potential Adverse Drug Events in Tweets Using Bootstrapped Lexicons
| Title | Identifying Potential Adverse Drug Events in Tweets Using Bootstrapped Lexicons |
| Authors | Eric Benzschawel |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-3003/ |
| https://www.aclweb.org/anthology/P16-3003 | |
| PWC | https://paperswithcode.com/paper/identifying-potential-adverse-drug-events-in |
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English-to-Japanese Translation vs. Dictation vs. Post-editing: Comparing Translation Modes in a Multilingual Setting
| Title | English-to-Japanese Translation vs. Dictation vs. Post-editing: Comparing Translation Modes in a Multilingual Setting |
| Authors | Michael Carl, Akiko Aizawa, Masaru Yamada |
| Abstract | Speech-enabled interfaces have the potential to become one of the most efficient and ergonomic environments for human-computer interaction and for text production. However, not much research has been carried out to investigate in detail the processes and strategies involved in the different modes of text production. This paper introduces and evaluates a corpus of more than 55 hours of English-to-Japanese user activity data that were collected within the ENJA15 project, in which translators were observed while writing and speaking translations (translation dictation) and during machine translation post-editing. The transcription of the spoken data, keyboard logging and eye-tracking data were recorded with Translog-II, post-processed and integrated into the CRITT Translation Process Research-DB (TPR-DB), which is publicly available under a creative commons license. The paper presents the ENJA15 data as part of a large multilingual Chinese, Danish, German, Hindi and Spanish translation process data collection of more than 760 translation sessions. It compares the ENJA15 data with the other language pairs and reviews some of its particularities. |
| Tasks | Eye Tracking, Machine Translation |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1635/ |
| https://www.aclweb.org/anthology/L16-1635 | |
| PWC | https://paperswithcode.com/paper/english-to-japanese-translation-vs-dictation |
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Near-Optimal Smoothing of Structured Conditional Probability Matrices
| Title | Near-Optimal Smoothing of Structured Conditional Probability Matrices |
| Authors | Moein Falahatgar, Mesrob I. Ohannessian, Alon Orlitsky |
| Abstract | Utilizing the structure of a probabilistic model can significantly increase its learning speed. Motivated by several recent applications, in particular bigram models in language processing, we consider learning low-rank conditional probability matrices under expected KL-risk. This choice makes smoothing, that is the careful handling of low-probability elements, paramount. We derive an iterative algorithm that extends classical non-negative matrix factorization to naturally incorporate additive smoothing and prove that it converges to the stationary points of a penalized empirical risk. We then derive sample-complexity bounds for the global minimizer of the penalized risk and show that it is within a small factor of the optimal sample complexity. This framework generalizes to more sophisticated smoothing techniques, including absolute-discounting. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6199-near-optimal-smoothing-of-structured-conditional-probability-matrices |
| http://papers.nips.cc/paper/6199-near-optimal-smoothing-of-structured-conditional-probability-matrices.pdf | |
| PWC | https://paperswithcode.com/paper/near-optimal-smoothing-of-structured |
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Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations
| Title | Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations |
| Authors | Gavin Abercrombie, Dirk Hovy |
| Abstract | |
| Tasks | Anomaly Detection, Sarcasm Detection, Sentiment Analysis |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-3016/ |
| https://www.aclweb.org/anthology/P16-3016 | |
| PWC | https://paperswithcode.com/paper/putting-sarcasm-detection-into-context-the |
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An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network
| Title | An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network |
| Authors | Y Xia, i, Zhongyu Wei, Yang Liu |
| Abstract | |
| Tasks | Machine Translation, Representation Learning, Sentence Classification, Word Embeddings |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-3019/ |
| https://www.aclweb.org/anthology/P16-3019 | |
| PWC | https://paperswithcode.com/paper/an-efficient-cross-lingual-model-for-sentence |
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POLYGLOT: Multilingual Semantic Role Labeling with Unified Labels
| Title | POLYGLOT: Multilingual Semantic Role Labeling with Unified Labels |
| Authors | Alan Akbik, Yunyao Li |
| Abstract | |
| Tasks | Question Answering, Semantic Role Labeling |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-4001/ |
| https://www.aclweb.org/anthology/P16-4001 | |
| PWC | https://paperswithcode.com/paper/polyglot-multilingual-semantic-role-labeling |
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A Web-framework for ODIN Annotation
| Title | A Web-framework for ODIN Annotation |
| Authors | Ryan Georgi, Michael Wayne Goodman, Fei Xia |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-4006/ |
| https://www.aclweb.org/anthology/P16-4006 | |
| PWC | https://paperswithcode.com/paper/a-web-framework-for-odin-annotation |
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