Paper Group NANR 63
Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse. Graphical Annotation for Syntax-Semantics Mapping. MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection. Analyzing Learner Understanding of Novel L2 Vocabulary. Facing the most difficult case of Semantic Role Labeling: A collaboration o …
Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse
| Title | Towards a Linguistic Ontology with an Emphasis on Reasoning and Knowledge Reuse |
| Authors | Artemis Parvizi, Matt Kohl, Meritxell Gonz{`a}lez, Roser Saur{'\i} |
| Abstract | The Dictionaries division at Oxford University Press (OUP) is aiming to model, integrate, and publish lexical content for 100 languages focussing on digitally under-represented languages. While there are multiple ontologies designed for linguistic resources, none had adequate features for meeting our requirements, chief of which was the capability to losslessly capture diverse features of many different languages in a dictionary format, while supplying a framework for inferring relations like translation, derivation, etc., between the data. Building on valuable features of existing models, and working with OUP monolingual and bilingual dictionary datasets, we have designed and implemented a new linguistic ontology. The ontology has been reviewed by a number of computational linguists, and we are working to move more dictionary data into it. We have also developed APIs to surface the linked data to dictionary websites. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1071/ |
| https://www.aclweb.org/anthology/L16-1071 | |
| PWC | https://paperswithcode.com/paper/towards-a-linguistic-ontology-with-an |
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Graphical Annotation for Syntax-Semantics Mapping
| Title | Graphical Annotation for Syntax-Semantics Mapping |
| Authors | K{^o}iti Hasida |
| Abstract | A potential work item (PWI) for ISO standard (MAP) about linguistic annotation concerning syntax-semantics mapping is discussed. MAP is a framework for graphical linguistic annotation to specify a mapping (set of combinations) between possible syntactic and semantic structures of the annotated linguistic data. Just like a UML diagram, a MAP diagram is formal, in the sense that it accurately specifies such a mapping. MAP provides a diagrammatic sort of concrete syntax for linguistic annotation far easier to understand than textual concrete syntax such as in XML, so that it could better facilitate collaborations among people involved in research, standardization, and practical use of linguistic data. MAP deals with syntactic structures including dependencies, coordinations, ellipses, transsentential constructions, and so on. Semantic structures treated by MAP are argument structures, scopes, coreferences, anaphora, discourse relations, dialogue acts, and so forth. In order to simplify explicit annotations, MAP allows partial descriptions, and assumes a few general rules on correspondence between syntactic and semantic compositions. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1644/ |
| https://www.aclweb.org/anthology/L16-1644 | |
| PWC | https://paperswithcode.com/paper/graphical-annotation-for-syntax-semantics |
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MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection
| Title | MED: The LMU System for the SIGMORPHON 2016 Shared Task on Morphological Reinflection |
| Authors | Katharina Kann, Hinrich Sch{"u}tze |
| Abstract | |
| Tasks | Machine Translation, Morphological Inflection, Question Answering |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2010/ |
| https://www.aclweb.org/anthology/W16-2010 | |
| PWC | https://paperswithcode.com/paper/med-the-lmu-system-for-the-sigmorphon-2016 |
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Analyzing Learner Understanding of Novel L2 Vocabulary
| Title | Analyzing Learner Understanding of Novel L2 Vocabulary |
| Authors | Rebecca Knowles, Adithya Renduchintala, Philipp Koehn, Jason Eisner |
| Abstract | |
| Tasks | Morphological Inflection |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/K16-1013/ |
| https://www.aclweb.org/anthology/K16-1013 | |
| PWC | https://paperswithcode.com/paper/analyzing-learner-understanding-of-novel-l2 |
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Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training
| Title | Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training |
| Authors | Quynh Ngoc Thi Do, Steven Bethard, Marie-Francine Moens |
| Abstract | We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-training is a successful traditional semi-supervised method, its application in SRL is very limited especially when a huge amount of labeled data is available. In this work, co-training is used together with word embeddings to improve the performance of a system trained on a large training dataset. We also introduce a semantic role labeling system with a simple learning architecture and effective inference that is easily adaptable to semi-supervised settings with new training data and/or new features. On the out-of-domain testing set of the standard benchmark CoNLL 2009 data our simple approach achieves high performance and improves state-of-the-art results. |
| Tasks | Semantic Role Labeling, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1121/ |
| https://www.aclweb.org/anthology/C16-1121 | |
| PWC | https://paperswithcode.com/paper/facing-the-most-difficult-case-of-semantic |
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EDISON: Feature Extraction for NLP, Simplified
| Title | EDISON: Feature Extraction for NLP, Simplified |
| Authors | Mark Sammons, Christos Christodoulopoulos, Parisa Kordjamshidi, Daniel Khashabi, Vivek Srikumar, Dan Roth |
| Abstract | When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for existing NLP tasks. We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures. These feature extractors populate simple data structures encoding the extracted features, which the package can also serialize to an intuitive JSON file format that can be easily mapped to formats used by ML packages. EDISON can also be used programmatically with JVM-based (Java/Scala) NLP software to provide the feature extractor input. The collection of feature extractors is organised hierarchically and a simple search interface is provided. In this paper we include examples that demonstrate the versatility and ease-of-use of the EDISON feature extraction suite to show that this can significantly reduce the time spent by developers on feature extraction design for NLP systems. The library is publicly hosted at https://github.com/IllinoisCogComp/illinois-cogcomp-nlp/, and we hope that other NLP researchers will contribute to the set of feature extractors. In this way, the community can help simplify reproduction of published results and the integration of ideas from diverse sources when developing new and improved NLP applications. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1645/ |
| https://www.aclweb.org/anthology/L16-1645 | |
| PWC | https://paperswithcode.com/paper/edison-feature-extraction-for-nlp-simplified |
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Semi-Supervised Learning of Sequence Models with Method of Moments
| Title | Semi-Supervised Learning of Sequence Models with Method of Moments |
| Authors | Zita Marinho, Andr{'e} F. T. Martins, Shay B. Cohen, Noah A. Smith |
| Abstract | |
| Tasks | Part-Of-Speech Tagging |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1028/ |
| https://www.aclweb.org/anthology/D16-1028 | |
| PWC | https://paperswithcode.com/paper/semi-supervised-learning-of-sequence-models |
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Syndromic Surveillance using Generic Medical Entities on Twitter
| Title | Syndromic Surveillance using Generic Medical Entities on Twitter |
| Authors | Pin Huang, Andrew MacKinlay, Antonio Jimeno Yepes |
| Abstract | |
| Tasks | Named Entity Recognition |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/U16-1004/ |
| https://www.aclweb.org/anthology/U16-1004 | |
| PWC | https://paperswithcode.com/paper/syndromic-surveillance-using-generic-medical |
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Support Super-Vector Machines in Automatic Speech Emotion Recognition
| Title | Support Super-Vector Machines in Automatic Speech Emotion Recognition |
| Authors | Chia-Ying Chen, Chia-Ping Chen |
| Abstract | |
| Tasks | Emotion Recognition, Speech Emotion Recognition |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/O16-1015/ |
| https://www.aclweb.org/anthology/O16-1015 | |
| PWC | https://paperswithcode.com/paper/support-super-vector-machines-in-automatic |
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標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese]
| Title | 標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese] |
| Authors | Tung-Han Wu, Chia-Ping Chen |
| Abstract | |
| Tasks | Emotion Recognition, Speech Emotion Recognition |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/O16-1023/ |
| https://www.aclweb.org/anthology/O16-1023 | |
| PWC | https://paperswithcode.com/paper/e-a14ecceae3ce34-ec3ce34-ea1a12eeffects-of |
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Enhancing PTB Universal Dependencies for Grammar-Based Surface Realization
| Title | Enhancing PTB Universal Dependencies for Grammar-Based Surface Realization |
| Authors | David L. King, Michael White |
| Abstract | |
| Tasks | Text Generation |
| Published | 2016-09-01 |
| URL | https://www.aclweb.org/anthology/W16-6638/ |
| https://www.aclweb.org/anthology/W16-6638 | |
| PWC | https://paperswithcode.com/paper/enhancing-ptb-universal-dependencies-for |
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Video Event Detection by Exploiting Word Dependencies from Image Captions
| Title | Video Event Detection by Exploiting Word Dependencies from Image Captions |
| Authors | Sang Phan, Yusuke Miyao, Duy-Dinh Le, Shin{'}ichi Satoh |
| Abstract | Video event detection is a challenging problem in information and multimedia retrieval. Different from single action detection, event detection requires a richer level of semantic information from video. In order to overcome this challenge, existing solutions often represent videos using high level features such as concepts. However, concept-based representation can be confusing because it does not encode the relationship between concepts. This issue can be addressed by exploiting the co-occurrences of the concepts, however, it often leads to a very huge number of possible combinations. In this paper, we propose a new approach to obtain the relationship between concepts by exploiting the syntactic dependencies between words in the image captions. The main advantage of this approach is that it significantly reduces the number of informative combinations between concepts. We conduct extensive experiments to analyze the effectiveness of using the new dependency representation for event detection on two large-scale TRECVID Multimedia Event Detection 2013 and 2014 datasets. Experimental results show that i) Dependency features are more discriminative than concept-based features. ii) Dependency features can be combined with our current event detection system to further improve the performance. For instance, the relative improvement can be as far as 8.6{%} on the MEDTEST14 10Ex setting. |
| Tasks | Action Detection, Image Captioning |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1313/ |
| https://www.aclweb.org/anthology/C16-1313 | |
| PWC | https://paperswithcode.com/paper/video-event-detection-by-exploiting-word |
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How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents
| Title | How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents |
| Authors | Anders Bj{"o}rkelund, Agnieszka Fale{'n}ska, Wolfgang Seeker, Jonas Kuhn |
| Abstract | |
| Tasks | Boundary Detection, Dependency Parsing, Speech Recognition, Transition-Based Dependency Parsing |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1181/ |
| https://www.aclweb.org/anthology/P16-1181 | |
| PWC | https://paperswithcode.com/paper/how-to-train-dependency-parsers-with-inexact |
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Transition-based dependency parsing with topological fields
| Title | Transition-based dependency parsing with topological fields |
| Authors | Dani{"e}l de Kok, Erhard Hinrichs |
| Abstract | |
| Tasks | Dependency Parsing, Transition-Based Dependency Parsing |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2001/ |
| https://www.aclweb.org/anthology/P16-2001 | |
| PWC | https://paperswithcode.com/paper/transition-based-dependency-parsing-with |
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Multi-view and multi-task training of RST discourse parsers
| Title | Multi-view and multi-task training of RST discourse parsers |
| Authors | Chlo{'e} Braud, Barbara Plank, Anders S{\o}gaard |
| Abstract | We experiment with different ways of training LSTM networks to predict RST discourse trees. The main challenge for RST discourse parsing is the limited amounts of training data. We combat this by regularizing our models using task supervision from related tasks as well as alternative views on discourse structures. We show that a simple LSTM sequential discourse parser takes advantage of this multi-view and multi-task framework with 12-15{%} error reductions over our baseline (depending on the metric) and results that rival more complex state-of-the-art parsers. |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1179/ |
| https://www.aclweb.org/anthology/C16-1179 | |
| PWC | https://paperswithcode.com/paper/multi-view-and-multi-task-training-of-rst |
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