Paper Group NANR 151
Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network. Morphologically Annotated Corpora and Morphological Analyzers for Moroccan and Sanaani Yemeni Arabic. A Joint Model of Orthography and Morphological Segmentation. A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures. LIMSI at Se …
Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network
| Title | Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network |
| Authors | Peilu Wang, Yao Qian, Frank K. Soong, Lei He, Hai Zhao |
| Abstract | |
| Tasks | Chunking, Dependency Parsing, Named Entity Recognition, Part-Of-Speech Tagging, Slot Filling |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1064/ |
| https://www.aclweb.org/anthology/N16-1064 | |
| PWC | https://paperswithcode.com/paper/learning-distributed-word-representations-for-1 |
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Morphologically Annotated Corpora and Morphological Analyzers for Moroccan and Sanaani Yemeni Arabic
| Title | Morphologically Annotated Corpora and Morphological Analyzers for Moroccan and Sanaani Yemeni Arabic |
| Authors | Faisal Al-Shargi, Aidan Kaplan, Esk, Ramy er, Nizar Habash, Owen Rambow |
| Abstract | We present new language resources for Moroccan and Sanaani Yemeni Arabic. The resources include corpora for each dialect which have been morphologically annotated, and morphological analyzers for each dialect which are derived from these corpora. These are the first sets of resources for Moroccan and Yemeni Arabic. The resources will be made available to the public. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1207/ |
| https://www.aclweb.org/anthology/L16-1207 | |
| PWC | https://paperswithcode.com/paper/morphologically-annotated-corpora-and |
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A Joint Model of Orthography and Morphological Segmentation
| Title | A Joint Model of Orthography and Morphological Segmentation |
| Authors | Ryan Cotterell, Tim Vieira, Hinrich Sch{"u}tze |
| Abstract | |
| Tasks | Keyword Spotting, Machine Translation, Morphological Analysis, Speech Recognition |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1080/ |
| https://www.aclweb.org/anthology/N16-1080 | |
| PWC | https://paperswithcode.com/paper/a-joint-model-of-orthography-and |
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A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures
| Title | A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures |
| Authors | Richard Eckart de Castilho, {'E}va M{'u}jdricza-Maydt, Seid Muhie Yimam, Silvana Hartmann, Iryna Gurevych, Anette Frank, Chris Biemann |
| Abstract | We introduce the third major release of WebAnno, a generic web-based annotation tool for distributed teams. New features in this release focus on semantic annotation tasks (e.g. semantic role labelling or event annotation) and allow the tight integration of semantic annotations with syntactic annotations. In particular, we introduce the concept of slot features, a novel constraint mechanism that allows modelling the interaction between semantic and syntactic annotations, as well as a new annotation user interface. The new features were developed and used in an annotation project for semantic roles on German texts. The paper briefly introduces this project and reports on experiences performing annotations with the new tool. On a comparative evaluation, our tool reaches significant speedups over WebAnno 2 for a semantic annotation task. |
| Tasks | Relation Extraction, Slot Filling |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4011/ |
| https://www.aclweb.org/anthology/W16-4011 | |
| PWC | https://paperswithcode.com/paper/a-web-based-tool-for-the-integrated |
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LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions
| Title | LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions |
| Authors | Cyril Grouin, V{'e}ronique Moriceau |
| Abstract | |
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| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1190/ |
| https://www.aclweb.org/anthology/S16-1190 | |
| PWC | https://paperswithcode.com/paper/limsi-at-semeval-2016-task-12-machine |
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Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes
| Title | Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes |
| Authors | Sarath P R, Manik R, an, Yoshiki Niwa |
| Abstract | |
| Tasks | Question Answering, Temporal Information Extraction, Text Classification |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1191/ |
| https://www.aclweb.org/anthology/S16-1191 | |
| PWC | https://paperswithcode.com/paper/hitachi-at-semeval-2016-task-12-a-hybrid |
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Selection method of an appropriate response in chat-oriented dialogue systems
| Title | Selection method of an appropriate response in chat-oriented dialogue systems |
| Authors | Hideaki Mori, Masahiro Araki |
| Abstract | |
| Tasks | Spoken Dialogue Systems |
| Published | 2016-09-01 |
| URL | https://www.aclweb.org/anthology/W16-3629/ |
| https://www.aclweb.org/anthology/W16-3629 | |
| PWC | https://paperswithcode.com/paper/selection-method-of-an-appropriate-response |
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What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
| Title | What’s in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams |
| Authors | Peter Jansen, Niranjan Balasubramanian, Mihai Surdeanu, Peter Clark |
| Abstract | QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which supports a fine-grained characterization of the challenges. In particular, we model the requirements based on appropriate sources of evidence to be used for the QA task. We create requirements by first identifying suitable sentences in a knowledge base that support the correct answer, then use these to build explanations, filling in any necessary missing information. These explanations are used to create a fine-grained categorization of the requirements. Using these requirements, we compare a retrieval and an inference solver on 212 questions. The analysis validates the gains of the inference solver, demonstrating that it answers more questions requiring complex inference, while also providing insights into the relative strengths of the solvers and knowledge sources. We release the annotated questions and explanations as a resource with broad utility for science exam QA, including determining knowledge base construction targets, as well as supporting information aggregation in automated inference. |
| Tasks | Question Answering |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1278/ |
| https://www.aclweb.org/anthology/C16-1278 | |
| PWC | https://paperswithcode.com/paper/whats-in-an-explanation-characterizing |
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``All I know about politics is what I read in Twitter’': Weakly Supervised Models for Extracting Politicians’ Stances From Twitter
| Title | ``All I know about politics is what I read in Twitter’': Weakly Supervised Models for Extracting Politicians’ Stances From Twitter | |
| Authors | Kristen Johnson, Dan Goldwasser |
| Abstract | During the 2016 United States presidential election, politicians have increasingly used Twitter to express their beliefs, stances on current political issues, and reactions concerning national and international events. Given the limited length of tweets and the scrutiny politicians face for what they choose or neglect to say, they must craft and time their tweets carefully. The content and delivery of these tweets is therefore highly indicative of a politician{'}s stances. We present a weakly supervised method for extracting how issues are framed and temporal activity patterns on Twitter for popular politicians and issues of the 2016 election. These behavioral components are combined into a global model which collectively infers the most likely stance and agreement patterns among politicians, with respective accuracies of 86.44{%} and 84.6{%} on average. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1279/ |
| https://www.aclweb.org/anthology/C16-1279 | |
| PWC | https://paperswithcode.com/paper/all-i-know-about-politics-is-what-i-read-in |
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Leveraging Multiple Domains for Sentiment Classification
| Title | Leveraging Multiple Domains for Sentiment Classification |
| Authors | Fan Yang, Arjun Mukherjee, Yifan Zhang |
| Abstract | Sentiment classification becomes more and more important with the rapid growth of user generated content. However, sentiment classification task usually comes with two challenges: first, sentiment classification is highly domain-dependent and training sentiment classifier for every domain is inefficient and often impractical; second, since the quantity of labeled data is important for assessing the quality of classifier, it is hard to evaluate classifiers when labeled data is limited for certain domains. To address the challenges mentioned above, we focus on learning high-level features that are able to generalize across domains, so a global classifier can benefit with a simple combination of documents from multiple domains. In this paper, the proposed model incorporates both sentiment polarity and unlabeled data from multiple domains and learns new feature representations. Our model doesn{'}t require labels from every domain, which means the learned feature representation can be generalized for sentiment domain adaptation. In addition, the learned feature representation can be used as classifier since our model defines the meaning of feature value and arranges high-level features in a prefixed order, so it is not necessary to train another classifier on top of the new features. Empirical evaluations demonstrate our model outperforms baselines and yields competitive results to other state-of-the-art works on benchmark datasets. |
| Tasks | Domain Adaptation, Opinion Mining, Sentiment Analysis |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1280/ |
| https://www.aclweb.org/anthology/C16-1280 | |
| PWC | https://paperswithcode.com/paper/leveraging-multiple-domains-for-sentiment |
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Evaluation of the KIT Lecture Translation System
| Title | Evaluation of the KIT Lecture Translation System |
| Authors | Markus M{"u}ller, Sarah F{"u}nfer, Sebastian St{"u}ker, Alex Waibel |
| Abstract | To attract foreign students is among the goals of the Karlsruhe Institute of Technology (KIT). One obstacle to achieving this goal is that lectures at KIT are usually held in German which many foreign students are not sufficiently proficient in, as, e.g., opposed to English. While the students from abroad are learning German during their stay at KIT, it is challenging to become proficient enough in it in order to follow a lecture. As a solution to this problem we offer our automatic simultaneous lecture translation. It translates German lectures into English in real time. While not as good as human interpreters, the system is available at a price that KIT can afford in order to offer it in potentially all lectures. In order to assess whether the quality of the system we have conducted a user study. In this paper we present this study, the way it was conducted and its results. The results indicate that the quality of the system has passed a threshold as to be able to support students in their studies. The study has helped to identify the most crucial weaknesses of the systems and has guided us which steps to take next. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1293/ |
| https://www.aclweb.org/anthology/L16-1293 | |
| PWC | https://paperswithcode.com/paper/evaluation-of-the-kit-lecture-translation |
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Fast Inference for Interactive Models of Text
| Title | Fast Inference for Interactive Models of Text |
| Authors | Jeffrey Lund, Paul Felt, Kevin Seppi, Eric Ringger |
| Abstract | Probabilistic models are a useful means for analyzing large text corpora. Integrating such models with human interaction enables many new use cases. However, adding human interaction to probabilistic models requires inference algorithms which are both fast and accurate. We explore the use of Iterated Conditional Modes as a fast alternative to Gibbs sampling or variational EM. We demonstrate superior performance both in run time and model quality on three different models of text including a DP Mixture of Multinomials for web search result clustering, the Interactive Topic Model, and M OM R ESP , a multinomial crowdsourcing model. |
| Tasks | Topic Models |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1282/ |
| https://www.aclweb.org/anthology/C16-1282 | |
| PWC | https://paperswithcode.com/paper/fast-inference-for-interactive-models-of-text |
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SemEval-2016 Task 9: Chinese Semantic Dependency Parsing
| Title | SemEval-2016 Task 9: Chinese Semantic Dependency Parsing |
| Authors | Wanxiang Che, Yanqiu Shao, Ting Liu, Yu Ding |
| Abstract | |
| Tasks | Dependency Parsing, Semantic Dependency Parsing |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1167/ |
| https://www.aclweb.org/anthology/S16-1167 | |
| PWC | https://paperswithcode.com/paper/semeval-2016-task-9-chinese-semantic |
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On the contribution of word embeddings to temporal relation classification
| Title | On the contribution of word embeddings to temporal relation classification |
| Authors | Paramita Mirza, Sara Tonelli |
| Abstract | Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities. This occurs frequently in inter-sentential relations, whose entities are not connected via direct syntactic relations making classification even more difficult. In these cases, resorting to features that focus on the semantic content of the event words may be very beneficial for inferring implicit relations. Specifically, while morpho-syntactic and context features are considered sufficient for classifying event-timex pairs, we believe that exploiting distributional semantic information about event words can benefit supervised classification of other types of pairs. In this work, we assess the impact of using word embeddings as features for event words in classifying temporal relations of event-event pairs and event-DCT (document creation time) pairs. |
| Tasks | Relation Classification, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1265/ |
| https://www.aclweb.org/anthology/C16-1265 | |
| PWC | https://paperswithcode.com/paper/on-the-contribution-of-word-embeddings-to |
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Terminology Extraction with Term Variant Detection
| Title | Terminology Extraction with Term Variant Detection |
| Authors | Damien Cram, B{'e}atrice Daille |
| Abstract | |
| Tasks | Information Retrieval |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-4003/ |
| https://www.aclweb.org/anthology/P16-4003 | |
| PWC | https://paperswithcode.com/paper/terminology-extraction-with-term-variant |
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