Paper Group NANR 191
Embedding Senses for Efficient Graph-based Word Sense Disambiguation. Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking. UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED). Citation Analysis with Neural Attention Models. Efficient Neural Codes under Metabolic Co …
Embedding Senses for Efficient Graph-based Word Sense Disambiguation
| Title | Embedding Senses for Efficient Graph-based Word Sense Disambiguation |
| Authors | Luis Nieto Pi{~n}a, Richard Johansson |
| Abstract | |
| Tasks | Language Modelling, Word Sense Disambiguation |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1401/ |
| https://www.aclweb.org/anthology/W16-1401 | |
| PWC | https://paperswithcode.com/paper/embedding-senses-for-efficient-graph-based |
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Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking
| Title | Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking |
| Authors | Seokhwan Kim, Rafael Banchs, Haizhou Li |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1091/ |
| https://www.aclweb.org/anthology/P16-1091 | |
| PWC | https://paperswithcode.com/paper/exploring-convolutional-and-recurrent-neural |
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UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED)
| Title | UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED) |
| Authors | Jari Bj{"o}rne, Tapio Salakoski |
| Abstract | |
| Tasks | |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1142/ |
| https://www.aclweb.org/anthology/S16-1142 | |
| PWC | https://paperswithcode.com/paper/utu-at-semeval-2016-task-10-binary |
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Citation Analysis with Neural Attention Models
| Title | Citation Analysis with Neural Attention Models |
| Authors | Tsendsuren Munkhdalai, John P. Lalor, Hong Yu |
| Abstract | |
| Tasks | Information Retrieval, Question Answering, Sentiment Analysis |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-6109/ |
| https://www.aclweb.org/anthology/W16-6109 | |
| PWC | https://paperswithcode.com/paper/citation-analysis-with-neural-attention |
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Efficient Neural Codes under Metabolic Constraints
| Title | Efficient Neural Codes under Metabolic Constraints |
| Authors | Zhuo Wang, Xue-Xin Wei, Alan A. Stocker, Daniel D. Lee |
| Abstract | Neural codes are inevitably shaped by various kinds of biological constraints, \emph{e.g.} noise and metabolic cost. Here we formulate a coding framework which explicitly deals with noise and the metabolic costs associated with the neural representation of information, and analytically derive the optimal neural code for monotonic response functions and arbitrary stimulus distributions. For a single neuron, the theory predicts a family of optimal response functions depending on the metabolic budget and noise characteristics. Interestingly, the well-known histogram equalization solution can be viewed as a special case when metabolic resources are unlimited. For a pair of neurons, our theory suggests that under more severe metabolic constraints, ON-OFF coding is an increasingly more efficient coding scheme compared to ON-ON or OFF-OFF. The advantage could be as large as one-fold, substantially larger than the previous estimation. Some of these predictions could be generalized to the case of large neural populations. In particular, these analytical results may provide a theoretical basis for the predominant segregation into ON- and OFF-cells in early visual processing areas. Overall, we provide a unified framework for optimal neural codes with monotonic tuning curves in the brain, and makes predictions that can be directly tested with physiology experiments. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6470-efficient-neural-codes-under-metabolic-constraints |
| http://papers.nips.cc/paper/6470-efficient-neural-codes-under-metabolic-constraints.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-neural-codes-under-metabolic |
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Effect of Syntactic Features in Bangla Sentence Comprehension
| Title | Effect of Syntactic Features in Bangla Sentence Comprehension |
| Authors | Manjira Sinha, Tirthankar Dasgupta, Anupam Basu |
| Abstract | |
| Tasks | Language Acquisition, Reading Comprehension |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-6334/ |
| https://www.aclweb.org/anthology/W16-6334 | |
| PWC | https://paperswithcode.com/paper/effect-of-syntactic-features-in-bangla |
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Overview of the CL-SciSumm 2016 Shared Task
| Title | Overview of the CL-SciSumm 2016 Shared Task |
| Authors | Kokil Jaidka, Kumar Ch, Muthu rasekaran, Sajal Rustagi, Min-Yen Kan |
| Abstract | |
| Tasks | Document Summarization, Information Retrieval |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1511/ |
| https://www.aclweb.org/anthology/W16-1511 | |
| PWC | https://paperswithcode.com/paper/overview-of-the-cl-scisumm-2016-shared-task |
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CIST System for CL-SciSumm 2016 Shared Task
| Title | CIST System for CL-SciSumm 2016 Shared Task |
| Authors | Lei Li, Liyuan Mao, Yazhao Zhang, Junqi Chi, Taiwen Huang, Xiaoyue Cong, Heng Peng |
| Abstract | |
| Tasks | Document Summarization, Information Retrieval, Multi-Document Summarization, Text Summarization |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1518/ |
| https://www.aclweb.org/anthology/W16-1518 | |
| PWC | https://paperswithcode.com/paper/cist-system-for-cl-scisumm-2016-shared-task |
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An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text
| Title | An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text |
| Authors | Eric Yeh, John Niekrasz, Dayne Freitag, Richard Rohwer |
| Abstract | We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode information according to ad hoc schemas and avail themselves of visual cues in place of natural language grammar, presenting problems for standard information extraction algorithms. Unlike previous work in table extraction, which assumes a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of naturally occurring structure types. Our approach has three main parts. First, we collect and annotate a a diverse sample of {``}naturally{''} occurring structures from several sources. Second, we use probabilistic text segmentation techniques, featurized by skip bigrams over spatial and token category cues, to automatically identify contiguous regions of structured text that share a common schema. Finally, we identify the records and fields within each structured region using a combination of distributional similarity and sequence alignment methods, guided by minimal supervision in the form of a single annotated record. We evaluate the last two components individually, and conclude with a discussion of further work. | |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1327/ |
| https://www.aclweb.org/anthology/L16-1327 | |
| PWC | https://paperswithcode.com/paper/an-annotated-corpus-and-method-for-analysis |
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Trainable Citation-enhanced Summarization of Scientific Articles
| Title | Trainable Citation-enhanced Summarization of Scientific Articles |
| Authors | Horacio Saggion, Ahmed AbuRa{'}ed, Francesco Ronzano |
| Abstract | |
| Tasks | Document Summarization, Information Retrieval, Opinion Mining, Text Summarization |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1520/ |
| https://www.aclweb.org/anthology/W16-1520 | |
| PWC | https://paperswithcode.com/paper/trainable-citation-enhanced-summarization-of |
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基於深層遞迴類神經網路之多通道電視回聲消除系統(Multi-Channel Television Echo Cancellation based on Deep Recurrent Neural Networks)[In Chinese]
| Title | 基於深層遞迴類神經網路之多通道電視回聲消除系統(Multi-Channel Television Echo Cancellation based on Deep Recurrent Neural Networks)[In Chinese] |
| Authors | Hung Huang, Wei-Jung Hung, Yuan-Fu Liao |
| Abstract | |
| Tasks | |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/O16-1032/ |
| https://www.aclweb.org/anthology/O16-1032 | |
| PWC | https://paperswithcode.com/paper/ao14aee-eccc2e-a1aeeeeae2ec3cmulti-channel |
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ARRAU: Linguistically-Motivated Annotation of Anaphoric Descriptions
| Title | ARRAU: Linguistically-Motivated Annotation of Anaphoric Descriptions |
| Authors | Olga Uryupina, Ron Artstein, Antonella Bristot, Federica Cavicchio, Kepa Rodriguez, Massimo Poesio |
| Abstract | This paper presents a second release of the ARRAU dataset: a multi-domain corpus with thorough linguistically motivated annotation of anaphora and related phenomena. Building upon the first release almost a decade ago, a considerable effort had been invested in improving the data both quantitatively and qualitatively. Thus, we have doubled the corpus size, expanded the selection of covered phenomena to include referentiality and genericity and designed and implemented a methodology for enforcing the consistency of the manual annotation. We believe that the new release of ARRAU provides a valuable material for ongoing research in complex cases of coreference as well as for a variety of related tasks. The corpus is publicly available through LDC. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1326/ |
| https://www.aclweb.org/anthology/L16-1326 | |
| PWC | https://paperswithcode.com/paper/arrau-linguistically-motivated-annotation-of |
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Domain Adaptation in MT Using Titles in Wikipedia as a Parallel Corpus: Resources and Evaluation
| Title | Domain Adaptation in MT Using Titles in Wikipedia as a Parallel Corpus: Resources and Evaluation |
| Authors | Gorka Labaka, I{~n}aki Alegria, Kepa Sarasola |
| Abstract | This paper presents how an state-of-the-art SMT system is enriched by using an extra in-domain parallel corpora extracted from Wikipedia. We collect corpora from parallel titles and from parallel fragments in comparable articles from Wikipedia. We carried out an evaluation with a double objective: evaluating the quality of the extracted data and evaluating the improvement due to the domain-adaptation. We think this can be very useful for languages with limited amount of parallel corpora, where in-domain data is crucial to improve the performance of MT sytems. The experiments on the Spanish-English language pair improve a baseline trained with the Europarl corpus in more than 2 points of BLEU when translating in the Computer Science domain. |
| Tasks | Domain Adaptation |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1351/ |
| https://www.aclweb.org/anthology/L16-1351 | |
| PWC | https://paperswithcode.com/paper/domain-adaptation-in-mt-using-titles-in |
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An Evaluation of Parser Robustness for Ungrammatical Sentences
| Title | An Evaluation of Parser Robustness for Ungrammatical Sentences |
| Authors | Homa B. Hashemi, Rebecca Hwa |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1182/ |
| https://www.aclweb.org/anthology/D16-1182 | |
| PWC | https://paperswithcode.com/paper/an-evaluation-of-parser-robustness-for |
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A Bayesian Model of Diachronic Meaning Change
| Title | A Bayesian Model of Diachronic Meaning Change |
| Authors | Lea Frermann, Mirella Lapata |
| Abstract | Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering. We present a dynamic Bayesian model of diachronic meaning change, which infers temporal word representations as a set of senses and their prevalence. Unlike previous work, we explicitly model language change as a smooth, gradual process. We experimentally show that this modeling decision is beneficial: our model performs competitively on meaning change detection tasks whilst inducing discernible word senses and their development over time. Application of our model to the SemEval-2015 temporal classification benchmark datasets further reveals that it performs on par with highly optimized task-specific systems. |
| Tasks | Information Retrieval, Question Answering |
| Published | 2016-01-01 |
| URL | https://www.aclweb.org/anthology/Q16-1003/ |
| https://www.aclweb.org/anthology/Q16-1003 | |
| PWC | https://paperswithcode.com/paper/a-bayesian-model-of-diachronic-meaning-change |
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