Paper Group NANR 12
Interoperable annotation of (co)references in the Democrat project. Reference Scope Identification for Citances Using Convolutional Neural Networks. Optimistic posterior sampling for reinforcement learning: worst-case regret bounds. Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation. Normalization of Soc …
Interoperable annotation of (co)references in the Democrat project
| Title | Interoperable annotation of (co)references in the Democrat project |
| Authors | Lo{"\i}c Grobol, L, Fr{'e}d{'e}ric ragin, Serge Heiden |
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| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-7411/ |
| https://www.aclweb.org/anthology/W17-7411 | |
| PWC | https://paperswithcode.com/paper/interoperable-annotation-of-coreferences-in |
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Reference Scope Identification for Citances Using Convolutional Neural Networks
| Title | Reference Scope Identification for Citances Using Convolutional Neural Networks |
| Authors | Saurav Jha, Aanchal Chaurasia, Akhilesh Sudhakar, Anil Kumar Singh |
| Abstract | |
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| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7504/ |
| https://www.aclweb.org/anthology/W17-7504 | |
| PWC | https://paperswithcode.com/paper/reference-scope-identification-for-citances |
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Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
| Title | Optimistic posterior sampling for reinforcement learning: worst-case regret bounds |
| Authors | Shipra Agrawal, Randy Jia |
| Abstract | We present an algorithm based on posterior sampling (aka Thompson sampling) that achieves near-optimal worst-case regret bounds when the underlying Markov Decision Process (MDP) is communicating with a finite, though unknown, diameter. Our main result is a high probability regret upper bound of $\tilde{O}(D\sqrt{SAT})$ for any communicating MDP with $S$ states, $A$ actions and diameter $D$, when $T\ge S^5A$. Here, regret compares the total reward achieved by the algorithm to the total expected reward of an optimal infinite-horizon undiscounted average reward policy, in time horizon $T$. This result improves over the best previously known upper bound of $\tilde{O}(DS\sqrt{AT})$ achieved by any algorithm in this setting, and matches the dependence on $S$ in the established lower bound of $\Omega(\sqrt{DSAT})$ for this problem. Our techniques involve proving some novel results about the anti-concentration of Dirichlet distribution, which may be of independent interest. |
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| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6718-optimistic-posterior-sampling-for-reinforcement-learning-worst-case-regret-bounds |
| http://papers.nips.cc/paper/6718-optimistic-posterior-sampling-for-reinforcement-learning-worst-case-regret-bounds.pdf | |
| PWC | https://paperswithcode.com/paper/optimistic-posterior-sampling-for |
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Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation
| Title | Rule-based Reordering and Post-Processing for Indonesian-Korean Statistical Machine Translation |
| Authors | C Mawalim, y Olivia, Dessi Puji Lestari, Ayu Purwarianti |
| Abstract | |
| Tasks | Language Modelling, Machine Translation |
| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/Y17-1039/ |
| https://www.aclweb.org/anthology/Y17-1039 | |
| PWC | https://paperswithcode.com/paper/rule-based-reordering-and-post-processing-for |
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Normalization of Social Media Text using Deep Neural Networks
| Title | Normalization of Social Media Text using Deep Neural Networks |
| Authors | Ajay Shankar Tiwari, Sudip Kumar Naskar |
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| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7539/ |
| https://www.aclweb.org/anthology/W17-7539 | |
| PWC | https://paperswithcode.com/paper/normalization-of-social-media-text-using-deep |
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Downstream use of syntactic analysis: does representation matter?
| Title | Downstream use of syntactic analysis: does representation matter? |
| Authors | Lilja {\O}vrelid |
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| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-7602/ |
| https://www.aclweb.org/anthology/W17-7602 | |
| PWC | https://paperswithcode.com/paper/downstream-use-of-syntactic-analysis-does |
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Document Level Novelty Detection: Textual Entailment Lends a Helping Hand
| Title | Document Level Novelty Detection: Textual Entailment Lends a Helping Hand |
| Authors | Tanik Saikh, Tirthankar Ghosal, Asif Ekbal, Pushpak Bhattacharyya |
| Abstract | |
| Tasks | Natural Language Inference |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/W17-7517/ |
| https://www.aclweb.org/anthology/W17-7517 | |
| PWC | https://paperswithcode.com/paper/document-level-novelty-detection-textual |
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Query-based summarization using MDL principle
| Title | Query-based summarization using MDL principle |
| Authors | Marina Litvak, Natalia Vanetik |
| Abstract | Query-based text summarization is aimed at extracting essential information that answers the query from original text. The answer is presented in a minimal, often predefined, number of words. In this paper we introduce a new unsupervised approach for query-based extractive summarization, based on the minimum description length (MDL) principle that employs Krimp compression algorithm (Vreeken et al., 2011). The key idea of our approach is to select frequent word sets related to a given query that compress document sentences better and therefore describe the document better. A summary is extracted by selecting sentences that best cover query-related frequent word sets. The approach is evaluated based on the DUC 2005 and DUC 2006 datasets which are specifically designed for query-based summarization (DUC, 2005 2006). It competes with the best results. |
| Tasks | Language Modelling, Query-Based Extractive Summarization, Text Summarization |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1004/ |
| https://www.aclweb.org/anthology/W17-1004 | |
| PWC | https://paperswithcode.com/paper/query-based-summarization-using-mdl-principle |
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LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting
| Title | LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting |
| Authors | El Moatez Billah Nagoudi, J{'e}r{'e}my Ferrero, Didier Schwab |
| Abstract | This article describes our proposed system named LIM-LIG. This system is designed for SemEval 2017 Task1: Semantic Textual Similarity (Track1). LIM-LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Arabic sentences. The main idea is to exploit the word representations as vectors in a multidimensional space to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. LIM-LIG system achieves a Pearson{'}s correlation of 0.74633, ranking 2nd among all participants in the Arabic monolingual pairs STS task organized within the SemEval 2017 evaluation campaign |
| Tasks | Information Retrieval, Machine Translation, Paraphrase Identification, Part-Of-Speech Tagging, Semantic Similarity, Semantic Textual Similarity, Word Sense Disambiguation |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2017/ |
| https://www.aclweb.org/anthology/S17-2017 | |
| PWC | https://paperswithcode.com/paper/lim-lig-at-semeval-2017-task1-enhancing-the |
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STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble
| Title | STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble |
| Authors | Sarah Kohail, Amr Rekaby Salama, Chris Biemann |
| Abstract | This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised approach, which estimates a word alignment-based similarity score, and supervised approach, which combines dependency graph similarity and coverage features with lexical similarity measures using regression methods. We also present a way on ensembling both models. Out of 84 submitted runs, our team best multi-lingual run has been ranked 12th in overall performance with correlation of 0.61, 7th among 31 participating teams. |
| Tasks | Graph Similarity, Information Retrieval, Natural Language Inference, Paraphrase Identification, Semantic Textual Similarity, Word Alignment |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2025/ |
| https://www.aclweb.org/anthology/S17-2025 | |
| PWC | https://paperswithcode.com/paper/sts-uhh-at-semeval-2017-task-1-scoring |
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Sentence Alignment using Unfolding Recursive Autoencoders
| Title | Sentence Alignment using Unfolding Recursive Autoencoders |
| Authors | Jeenu Grover, Pabitra Mitra |
| Abstract | In this paper, we propose a novel two step algorithm for sentence alignment in monolingual corpora using Unfolding Recursive Autoencoders. First, we use unfolding recursive auto-encoders (RAE) to learn feature vectors for phrases in syntactical tree of the sentence. To compare two sentences we use a similarity matrix which has dimensions proportional to the size of the two sentences. Since the similarity matrix generated to compare two sentences has varying dimension due to different sentence lengths, a dynamic pooling layer is used to map it to a matrix of fixed dimension. The resulting matrix is used to calculate the similarity scores between the two sentences. The second step of the algorithm captures the contexts in which the sentences occur in the document by using a dynamic programming algorithm for global alignment. |
| Tasks | Information Retrieval, Machine Translation, Paraphrase Identification, Question Answering, Text Summarization |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2503/ |
| https://www.aclweb.org/anthology/W17-2503 | |
| PWC | https://paperswithcode.com/paper/sentence-alignment-using-unfolding-recursive |
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LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network
| Title | LABDA at SemEval-2017 Task 10: Extracting Keyphrases from Scientific Publications by combining the BANNER tool and the UMLS Semantic Network |
| Authors | Isabel Segura-Bedmar, Crist{'o}bal Col{'o}n-Ruiz, Paloma Mart{'\i}nez |
| Abstract | This paper describes the system presented by the LABDA group at SemEval 2017 Task 10 ScienceIE, specifically for the subtasks of identification and classification of keyphrases from scientific articles. For the task of identification, we use the BANNER tool, a named entity recognition system, which is based on conditional random fields (CRF) and has obtained successful results in the biomedical domain. To classify keyphrases, we study the UMLS semantic network and propose a possible linking between the keyphrase types and the UMLS semantic groups. Based on this semantic linking, we create a dictionary for each keyphrase type. Then, a feature indicating if a token is found in one of these dictionaries is incorporated to feature set used by the BANNER tool. The final results on the test dataset show that our system still needs to be improved, but the conditional random fields and, consequently, the BANNER system can be used as a first approximation to identify and classify keyphrases. |
| Tasks | Named Entity Recognition |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2164/ |
| https://www.aclweb.org/anthology/S17-2164 | |
| PWC | https://paperswithcode.com/paper/labda-at-semeval-2017-task-10-extracting |
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A data-driven approach to verbal multiword expression detection. PARSEME Shared Task system description paper
| Title | A data-driven approach to verbal multiword expression detection. PARSEME Shared Task system description paper |
| Authors | Tiberiu Boros, Sonia Pipa, Verginica Barbu Mititelu, Dan Tufis |
| Abstract | {``}Multiword expressions{''} are groups of words acting as a morphologic, syntactic and semantic unit in linguistic analysis. Verbal multiword expressions represent the subgroup of multiword expressions, namely that in which a verb is the syntactic head of the group considered in its canonical (or dictionary) form. All multiword expressions are a great challenge for natural language processing, but the verbal ones are particularly interesting for tasks such as parsing, as the verb is the central element in the syntactic organization of a sentence. In this paper we introduce our data-driven approach to verbal multiword expressions which was objectively validated during the PARSEME shared task on verbal multiword expressions identification. We tested our approach on 12 languages, and we provide detailed information about corpora composition, feature selection process, validation procedure and performance on all languages. | |
| Tasks | Feature Selection, Lemmatization, Tokenization |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1716/ |
| https://www.aclweb.org/anthology/W17-1716 | |
| PWC | https://paperswithcode.com/paper/a-data-driven-approach-to-verbal-multiword |
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LIMSI@CoNLL’17: UD Shared Task
| Title | LIMSI@CoNLL’17: UD Shared Task |
| Authors | Lauriane Aufrant, Guillaume Wisniewski, Fran{\c{c}}ois Yvon |
| Abstract | This paper describes LIMSI{'}s submission to the CoNLL 2017 UD Shared Task, which is focused on small treebanks, and how to improve low-resourced parsing only by ad hoc combination of multiple views and resources. We present our approach for low-resourced parsing, together with a detailed analysis of the results for each test treebank. We also report extensive analysis experiments on model selection for the PUD treebanks, and on annotation consistency among UD treebanks. |
| Tasks | Model Selection, Tokenization |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/K17-3017/ |
| https://www.aclweb.org/anthology/K17-3017 | |
| PWC | https://paperswithcode.com/paper/limsiconll17-ud-shared-task |
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Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games
| Title | Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games |
| Authors | Anmol Gulati, Kumar Krishna Agrawal |
| Abstract | Acquiring language provides a ubiquitous mode of communication, across humans and robots. To this effect, distributional representations of words based on co-occurrence statistics, have provided significant advancements ranging across machine translation to comprehension. In this paper, we study the suitability of using general purpose word-embeddings for language learning in robots. We propose using text-based games as a proxy to evaluating word embedding on real robots. Based in a risk-reward setting, we review the effectiveness of the embeddings in navigating tasks in fantasy games, as an approximation to their performance on more complex scenarios, like language assisted robot navigation. |
| Tasks | Machine Translation, Robot Navigation, Self-Driving Cars, Word Embeddings |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5305/ |
| https://www.aclweb.org/anthology/W17-5305 | |
| PWC | https://paperswithcode.com/paper/playing-with-embeddings-evaluating-embeddings |
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