Paper Group NANR 111
The Instantiation Discourse Relation: A Corpus Analysis of Its Properties and Improved Detection. Leveraging FrameNet to Improve Automatic Event Detection. Proceedings of the 2nd Deep Machine Translation Workshop. WTF-LOD - A New Resource for Large-Scale NER Evaluation. A Domain Adaptation Regularization for Denoising Autoencoders. Phonological Pun …
The Instantiation Discourse Relation: A Corpus Analysis of Its Properties and Improved Detection
| Title | The Instantiation Discourse Relation: A Corpus Analysis of Its Properties and Improved Detection |
| Authors | Junyi Jessy Li, Ani Nenkova |
| Abstract | |
| Tasks | Document Summarization, Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1141/ |
| https://www.aclweb.org/anthology/N16-1141 | |
| PWC | https://paperswithcode.com/paper/the-instantiation-discourse-relation-a-corpus |
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Leveraging FrameNet to Improve Automatic Event Detection
| Title | Leveraging FrameNet to Improve Automatic Event Detection |
| Authors | Shulin Liu, Yubo Chen, Shizhu He, Kang Liu, Jun Zhao |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1201/ |
| https://www.aclweb.org/anthology/P16-1201 | |
| PWC | https://paperswithcode.com/paper/leveraging-framenet-to-improve-automatic |
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Proceedings of the 2nd Deep Machine Translation Workshop
| Title | Proceedings of the 2nd Deep Machine Translation Workshop |
| Authors | |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/W16-6400/ |
| https://www.aclweb.org/anthology/W16-6400 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-2nd-deep-machine |
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WTF-LOD - A New Resource for Large-Scale NER Evaluation
| Title | WTF-LOD - A New Resource for Large-Scale NER Evaluation |
| Authors | Lubomir Otrusina, Pavel Smrz |
| Abstract | This paper introduces the Web TextFull linkage to Linked Open Data (WTF-LOD) dataset intended for large-scale evaluation of named entity recognition (NER) systems. First, we present the process of collecting data from the largest publically-available textual corpora, including Wikipedia dumps, monthly runs of the CommonCrawl, and ClueWeb09/12. We discuss similarities and differences of related initiatives such as WikiLinks and WikiReverse. Our work primarily focuses on links from {``}textfull{''} documents (links surrounded by a text that provides a useful context for entity linking), de-duplication of the data and advanced cleaning procedures. Presented statistics demonstrate that the collected data forms one of the largest available resource of its kind. They also prove suitability of the result for complex NER evaluation campaigns, including an analysis of the most ambiguous name mentions appearing in the data. | |
| Tasks | Entity Linking, Named Entity Recognition |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1525/ |
| https://www.aclweb.org/anthology/L16-1525 | |
| PWC | https://paperswithcode.com/paper/wtf-lod-a-new-resource-for-large-scale-ner |
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A Domain Adaptation Regularization for Denoising Autoencoders
| Title | A Domain Adaptation Regularization for Denoising Autoencoders |
| Authors | St{'e}phane Clinchant, Gabriela Csurka, Boris Chidlovskii |
| Abstract | |
| Tasks | Denoising, Document Ranking, Domain Adaptation, Machine Translation, Named Entity Recognition, Opinion Mining, Part-Of-Speech Tagging, Text Classification, Topic Models, Transfer Learning, Word Embeddings |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2005/ |
| https://www.aclweb.org/anthology/P16-2005 | |
| PWC | https://paperswithcode.com/paper/a-domain-adaptation-regularization-for |
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Phonological Pun-derstanding
| Title | Phonological Pun-derstanding |
| Authors | Aaron Jaech, Rik Koncel-Kedziorski, Mari Ostendorf |
| Abstract | |
| Tasks | Speech Recognition |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1079/ |
| https://www.aclweb.org/anthology/N16-1079 | |
| PWC | https://paperswithcode.com/paper/phonological-pun-derstanding |
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Normalized Log-Linear Interpolation of Backoff Language Models is Efficient
| Title | Normalized Log-Linear Interpolation of Backoff Language Models is Efficient |
| Authors | Kenneth Heafield, Chase Geigle, Sean Massung, Lane Schwartz |
| Abstract | |
| Tasks | Language Modelling |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1083/ |
| https://www.aclweb.org/anthology/P16-1083 | |
| PWC | https://paperswithcode.com/paper/normalized-log-linear-interpolation-of |
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Assembling Narratives with Associative Threads
| Title | Assembling Narratives with Associative Threads |
| Authors | Pierre-Luc Vaudry, Guy Lapalme |
| Abstract | |
| Tasks | Text Generation |
| Published | 2016-09-01 |
| URL | https://www.aclweb.org/anthology/W16-5501/ |
| https://www.aclweb.org/anthology/W16-5501 | |
| PWC | https://paperswithcode.com/paper/assembling-narratives-with-associative |
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Bag of What? Simple Noun Phrase Extraction for Text Analysis
| Title | Bag of What? Simple Noun Phrase Extraction for Text Analysis |
| Authors | H, Abram ler, Matthew Denny, Hanna Wallach, Brendan O{'}Connor |
| Abstract | |
| Tasks | Text Classification |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-5615/ |
| https://www.aclweb.org/anthology/W16-5615 | |
| PWC | https://paperswithcode.com/paper/bag-of-what-simple-noun-phrase-extraction-for |
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Part-of-speech Tagging of Code-mixed Social Media Content: Pipeline, Stacking and Joint Modelling
| Title | Part-of-speech Tagging of Code-mixed Social Media Content: Pipeline, Stacking and Joint Modelling |
| Authors | Utsab Barman, Joachim Wagner, Jennifer Foster |
| Abstract | |
| Tasks | Language Identification, Part-Of-Speech Tagging |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-5804/ |
| https://www.aclweb.org/anthology/W16-5804 | |
| PWC | https://paperswithcode.com/paper/part-of-speech-tagging-of-code-mixed-social |
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Context-aware Natural Language Generation for Spoken Dialogue Systems
| Title | Context-aware Natural Language Generation for Spoken Dialogue Systems |
| Authors | Hao Zhou, Minlie Huang, Xiaoyan Zhu |
| Abstract | Natural language generation (NLG) is an important component of question answering(QA) systems which has a significant impact on system quality. Most tranditional QA systems based on templates or rules tend to generate rigid and stylised responses without the natural variation of human language. Furthermore, such methods need an amount of work to generate the templates or rules. To address this problem, we propose a Context-Aware LSTM model for NLG. The model is completely driven by data without manual designed templates or rules. In addition, the context information, including the question to be answered, semantic values to be addressed in the response, and the dialogue act type during interaction, are well approached in the neural network model, which enables the model to produce variant and informative responses. The quantitative evaluation and human evaluation show that CA-LSTM obtains state-of-the-art performance. |
| Tasks | Dialogue Generation, Question Answering, Spoken Dialogue Systems, Text Generation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1191/ |
| https://www.aclweb.org/anthology/C16-1191 | |
| PWC | https://paperswithcode.com/paper/context-aware-natural-language-generation-for |
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Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering
| Title | Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering |
| Authors | Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah |
| Abstract | We introduce the framework of {\em blind regression} motivated by {\em matrix completion} for recommendation systems: given $m$ users, $n$ movies, and a subset of user-movie ratings, the goal is to predict the unobserved user-movie ratings given the data, i.e., to complete the partially observed matrix. Following the framework of non-parametric statistics, we posit that user $u$ and movie $i$ have features $x_1(u)$ and $x_2(i)$ respectively, and their corresponding rating $y(u,i)$ is a noisy measurement of $f(x_1(u), x_2(i))$ for some unknown function $f$. In contrast with classical regression, the features $x = (x_1(u), x_2(i))$ are not observed, making it challenging to apply standard regression methods to predict the unobserved ratings. Inspired by the classical Taylor’s expansion for differentiable functions, we provide a prediction algorithm that is consistent for all Lipschitz functions. In fact, the analysis through our framework naturally leads to a variant of collaborative filtering, shedding insight into the widespread success of collaborative filtering in practice. Assuming each entry is sampled independently with probability at least $\max(m^{-1+\delta},n^{-1/2+\delta})$ with $\delta > 0$, we prove that the expected fraction of our estimates with error greater than $\epsilon$ is less than $\gamma^2 / \epsilon^2$ plus a polynomially decaying term, where $\gamma^2$ is the variance of the additive entry-wise noise term. Experiments with the MovieLens and Netflix datasets suggest that our algorithm provides principled improvements over basic collaborative filtering and is competitive with matrix factorization methods. |
| Tasks | Latent Variable Models, Matrix Completion, Recommendation Systems |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6108-blind-regression-nonparametric-regression-for-latent-variable-models-via-collaborative-filtering |
| http://papers.nips.cc/paper/6108-blind-regression-nonparametric-regression-for-latent-variable-models-via-collaborative-filtering.pdf | |
| PWC | https://paperswithcode.com/paper/blind-regression-nonparametric-regression-for |
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A Dutch Dysarthric Speech Database for Individualized Speech Therapy Research
| Title | A Dutch Dysarthric Speech Database for Individualized Speech Therapy Research |
| Authors | Emre Yilmaz, Mario Ganzeboom, Lilian Beijer, Catia Cucchiarini, Helmer Strik |
| Abstract | We present a new Dutch dysarthric speech database containing utterances of neurological patients with Parkinson{'}s disease, traumatic brain injury and cerebrovascular accident. The speech content is phonetically and linguistically diversified by using numerous structured sentence and word lists. Containing more than 6 hours of mildly to moderately dysarthric speech, this database can be used for research on dysarthria and for developing and testing speech-to-text systems designed for medical applications. Current activities aimed at extending this database are also discussed. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1127/ |
| https://www.aclweb.org/anthology/L16-1127 | |
| PWC | https://paperswithcode.com/paper/a-dutch-dysarthric-speech-database-for |
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Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis
| Title | Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis |
| Authors | |
| Abstract | |
| Tasks | |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-6100/ |
| https://www.aclweb.org/anthology/W16-6100 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-seventh-international |
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Visualizing the Content of a Children’s Story in a Virtual World: Lessons Learned
| Title | Visualizing the Content of a Children’s Story in a Virtual World: Lessons Learned |
| Authors | Quynh Ngoc Thi Do, Steven Bethard, Marie-Francine Moens |
| Abstract | |
| Tasks | Language Modelling, Semantic Role Labeling |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-6009/ |
| https://www.aclweb.org/anthology/W16-6009 | |
| PWC | https://paperswithcode.com/paper/visualizing-the-content-of-a-childrenas-story |
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