Paper Group NANR 164
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network. Automatic Features for Essay Scoring – An Empirical Study. Natural Language Processing for Intelligent Access to Scientific Information. A New Psychometric-inspired Evaluation Metric for Chinese Word Segmentation. An Embedding Model for Predicting Roll-Call …
Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network
| Title | Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network |
| Authors | Jifan Chen, Qi Zhang, Pengfei Liu, Xipeng Qiu, Xuanjing Huang |
| Abstract | |
| Tasks | Opinion Mining, Word Embeddings |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1163/ |
| https://www.aclweb.org/anthology/P16-1163 | |
| PWC | https://paperswithcode.com/paper/implicit-discourse-relation-detection-via-a |
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Automatic Features for Essay Scoring – An Empirical Study
| Title | Automatic Features for Essay Scoring – An Empirical Study |
| Authors | Fei Dong, Yue Zhang |
| Abstract | |
| Tasks | Domain Adaptation, Feature Engineering |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1115/ |
| https://www.aclweb.org/anthology/D16-1115 | |
| PWC | https://paperswithcode.com/paper/automatic-features-for-essay-scoring-a-an |
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Natural Language Processing for Intelligent Access to Scientific Information
| Title | Natural Language Processing for Intelligent Access to Scientific Information |
| Authors | Horacio Saggion, Francesco Ronzano |
| Abstract | During the last decade the amount of scientific information available on-line increased at an unprecedented rate. As a consequence, nowadays researchers are overwhelmed by an enormous and continuously growing number of articles to consider when they perform research activities like the exploration of advances in specific topics, peer reviewing, writing and evaluation of proposals. Natural Language Processing Technology represents a key enabling factor in providing scientists with intelligent patterns to access to scientific information. Extracting information from scientific papers, for example, can contribute to the development of rich scientific knowledge bases which can be leveraged to support intelligent knowledge access and question answering. Summarization techniques can reduce the size of long papers to their essential content or automatically generate state-of-the-art-reviews. Paraphrase or textual entailment techniques can contribute to the identification of relations across different scientific textual sources. This tutorial provides an overview of the most relevant tasks related to the processing of scientific documents, including but not limited to the in-depth analysis of the structure of the scientific articles, their semantic interpretation, content extraction and summarization. |
| Tasks | Natural Language Inference, Question Answering |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-3003/ |
| https://www.aclweb.org/anthology/C16-3003 | |
| PWC | https://paperswithcode.com/paper/natural-language-processing-for-intelligent |
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A New Psychometric-inspired Evaluation Metric for Chinese Word Segmentation
| Title | A New Psychometric-inspired Evaluation Metric for Chinese Word Segmentation |
| Authors | Peng Qian, Xipeng Qiu, Xuanjing Huang |
| Abstract | |
| Tasks | Chinese Word Segmentation, Feature Engineering |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1206/ |
| https://www.aclweb.org/anthology/P16-1206 | |
| PWC | https://paperswithcode.com/paper/a-new-psychometric-inspired-evaluation-metric |
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An Embedding Model for Predicting Roll-Call Votes
| Title | An Embedding Model for Predicting Roll-Call Votes |
| Authors | Peter Kraft, Hirsh Jain, Alex Rush, er M. |
| Abstract | |
| Tasks | Word Embeddings |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1221/ |
| https://www.aclweb.org/anthology/D16-1221 | |
| PWC | https://paperswithcode.com/paper/an-embedding-model-for-predicting-roll-call |
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A Pseudo-Bayesian Algorithm for Robust PCA
| Title | A Pseudo-Bayesian Algorithm for Robust PCA |
| Authors | Tae-Hyun Oh, Yasuyuki Matsushita, In Kweon, David Wipf |
| Abstract | Commonly used in many applications, robust PCA represents an algorithmic attempt to reduce the sensitivity of classical PCA to outliers. The basic idea is to learn a decomposition of some data matrix of interest into low rank and sparse components, the latter representing unwanted outliers. Although the resulting problem is typically NP-hard, convex relaxations provide a computationally-expedient alternative with theoretical support. However, in practical regimes performance guarantees break down and a variety of non-convex alternatives, including Bayesian-inspired models, have been proposed to boost estimation quality. Unfortunately though, without additional a priori knowledge none of these methods can significantly expand the critical operational range such that exact principal subspace recovery is possible. Into this mix we propose a novel pseudo-Bayesian algorithm that explicitly compensates for design weaknesses in many existing non-convex approaches leading to state-of-the-art performance with a sound analytical foundation. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6435-a-pseudo-bayesian-algorithm-for-robust-pca |
| http://papers.nips.cc/paper/6435-a-pseudo-bayesian-algorithm-for-robust-pca.pdf | |
| PWC | https://paperswithcode.com/paper/a-pseudo-bayesian-algorithm-for-robust-pca |
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LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning
| Title | LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning |
| Authors | Andrew Bennett, Timothy Baldwin, Jey Han Lau, Diana McCarthy, Francis Bond |
| Abstract | |
| Tasks | Lexical Simplification, Natural Language Inference, Word Sense Disambiguation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1143/ |
| https://www.aclweb.org/anthology/P16-1143 | |
| PWC | https://paperswithcode.com/paper/lexsemtm-a-semantic-dataset-based-on-all |
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Open Data Vocabularies for Assigning Usage Rights to Data Resources from Translation Projects
| Title | Open Data Vocabularies for Assigning Usage Rights to Data Resources from Translation Projects |
| Authors | David Lewis, Kaniz Fatema, Alfredo Maldonado, Brian Walshe, Arturo Calvo |
| Abstract | An assessment of the intellectual property requirements for data used in machine-aided translation is provided based on a recent EC-funded legal review. This is compared against the capabilities offered by current linked open data standards from the W3C for publishing and sharing translation memories from translation projects, and proposals for adequately addressing the intellectual property needs of stakeholders in translation projects using open data vocabularies are suggested. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1253/ |
| https://www.aclweb.org/anthology/L16-1253 | |
| PWC | https://paperswithcode.com/paper/open-data-vocabularies-for-assigning-usage |
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On Approximately Searching for Similar Word Embeddings
| Title | On Approximately Searching for Similar Word Embeddings |
| Authors | Kohei Sugawara, Hayato Kobayashi, Masajiro Iwasaki |
| Abstract | |
| Tasks | Word Embeddings |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1214/ |
| https://www.aclweb.org/anthology/P16-1214 | |
| PWC | https://paperswithcode.com/paper/on-approximately-searching-for-similar-word |
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Graph-based Dependency Parsing with Bidirectional LSTM
| Title | Graph-based Dependency Parsing with Bidirectional LSTM |
| Authors | Wenhui Wang, Baobao Chang |
| Abstract | |
| Tasks | Dependency Parsing, Feature Engineering, Feature Selection, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1218/ |
| https://www.aclweb.org/anthology/P16-1218 | |
| PWC | https://paperswithcode.com/paper/graph-based-dependency-parsing-with |
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TransG : A Generative Model for Knowledge Graph Embedding
| Title | TransG : A Generative Model for Knowledge Graph Embedding |
| Authors | Han Xiao, Minlie Huang, Xiaoyan Zhu |
| Abstract | |
| Tasks | Dimensionality Reduction, Graph Embedding, Knowledge Graph Embedding, Question Answering |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1219/ |
| https://www.aclweb.org/anthology/P16-1219 | |
| PWC | https://paperswithcode.com/paper/transg-a-generative-model-for-knowledge-graph |
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Vector-space topic models for detecting Alzheimer’s disease
| Title | Vector-space topic models for detecting Alzheimer’s disease |
| Authors | Maria Yancheva, Frank Rudzicz |
| Abstract | |
| Tasks | Topic Models |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1221/ |
| https://www.aclweb.org/anthology/P16-1221 | |
| PWC | https://paperswithcode.com/paper/vector-space-topic-models-for-detecting |
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Large Margin Discriminant Dimensionality Reduction in Prediction Space
| Title | Large Margin Discriminant Dimensionality Reduction in Prediction Space |
| Authors | Mohammad Saberian, Jose Costa Pereira, Can Xu, Jian Yang, Nuno Nvasconcelos |
| Abstract | In this paper we establish a duality between boosting and SVM, and use this to derive a novel discriminant dimensionality reduction algorithm. In particular, using the multiclass formulation of boosting and SVM we note that both use a combination of mapping and linear classification to maximize the multiclass margin. In SVM this is implemented using a pre-defined mapping (induced by the kernel) and optimizing the linear classifiers. In boosting the linear classifiers are pre-defined and the mapping (predictor) is learned through combination of weak learners. We argue that the intermediate mapping, e.g. boosting predictor, is preserving the discriminant aspects of the data and by controlling the dimension of this mapping it is possible to achieve discriminant low dimensional representations for the data. We use the aforementioned duality and propose a new method, Large Margin Discriminant Dimensionality Reduction (LADDER) that jointly learns the mapping and the linear classifiers in an efficient manner. This leads to a data-driven mapping which can embed data into any number of dimensions. Experimental results show that this embedding can significantly improve performance on tasks such as hashing and image/scene classification. |
| Tasks | Dimensionality Reduction, Scene Classification |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6458-large-margin-discriminant-dimensionality-reduction-in-prediction-space |
| http://papers.nips.cc/paper/6458-large-margin-discriminant-dimensionality-reduction-in-prediction-space.pdf | |
| PWC | https://paperswithcode.com/paper/large-margin-discriminant-dimensionality |
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Learning Multiview Embeddings of Twitter Users
| Title | Learning Multiview Embeddings of Twitter Users |
| Authors | Adrian Benton, Raman Arora, Mark Dredze |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2003/ |
| https://www.aclweb.org/anthology/P16-2003 | |
| PWC | https://paperswithcode.com/paper/learning-multiview-embeddings-of-twitter |
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Improving Statistical Machine Translation Performance by Oracle-BLEU Model Re-estimation
| Title | Improving Statistical Machine Translation Performance by Oracle-BLEU Model Re-estimation |
| Authors | Praveen Dakwale, Christof Monz |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2007/ |
| https://www.aclweb.org/anthology/P16-2007 | |
| PWC | https://paperswithcode.com/paper/improving-statistical-machine-translation-4 |
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