Paper Group NANR 214
Cross-Domain Mining of Argumentative Text through Distant Supervision. Sentiment Analysis in Twitter: A SemEval Perspective. Fully unsupervised low-dimensional representation of adverse drug reaction events through distributional semantics. Ubuntu-fr: A Large and Open Corpus for Multi-modal Analysis of Online Written Conversations. ANTUSD: A Large …
Cross-Domain Mining of Argumentative Text through Distant Supervision
| Title | Cross-Domain Mining of Argumentative Text through Distant Supervision |
| Authors | Khalid Al-Khatib, Henning Wachsmuth, Matthias Hagen, Jonas K{"o}hler, Benno Stein |
| Abstract | |
| Tasks | Abstract Argumentation, Decision Making |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1165/ |
| https://www.aclweb.org/anthology/N16-1165 | |
| PWC | https://paperswithcode.com/paper/cross-domain-mining-of-argumentative-text |
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Sentiment Analysis in Twitter: A SemEval Perspective
| Title | Sentiment Analysis in Twitter: A SemEval Perspective |
| Authors | Preslav Nakov |
| Abstract | |
| Tasks | Aspect-Based Sentiment Analysis, Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0427/ |
| https://www.aclweb.org/anthology/W16-0427 | |
| PWC | https://paperswithcode.com/paper/sentiment-analysis-in-twitter-a-semeval |
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Fully unsupervised low-dimensional representation of adverse drug reaction events through distributional semantics
| Title | Fully unsupervised low-dimensional representation of adverse drug reaction events through distributional semantics |
| Authors | Alicia P{'e}rez, Arantza Casillas, Koldo Gojenola |
| Abstract | Electronic health records show great variability since the same concept is often expressed with different terms, either scientific latin forms, common or lay variants and even vernacular naming. Deep learning enables distributional representation of terms in a vector-space, and therefore, related terms tend to be close in the vector space. Accordingly, embedding words through these vectors opens the way towards accounting for semantic relatedness through classical algebraic operations. In this work we propose a simple though efficient unsupervised characterization of Adverse Drug Reactions (ADRs). This approach exploits the embedding representation of the terms involved in candidate ADR events, that is, drug-disease entity pairs. In brief, the ADRs are represented as vectors that link the drug with the disease in their context through a recursive additive model. We discovered that a low-dimensional representation that makes use of the modulus and argument of the embedded representation of the ADR event shows correlation with the manually annotated class. Thus, it can be derived that this characterization results in to be beneficial for further classification tasks as predictive features. |
| Tasks | |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-5106/ |
| https://www.aclweb.org/anthology/W16-5106 | |
| PWC | https://paperswithcode.com/paper/fully-unsupervised-low-dimensional |
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Ubuntu-fr: A Large and Open Corpus for Multi-modal Analysis of Online Written Conversations
| Title | Ubuntu-fr: A Large and Open Corpus for Multi-modal Analysis of Online Written Conversations |
| Authors | Hern, Nicolas ez, Soufian Salim, Elizaveta Loginova Clouet |
| Abstract | We present a large, free, French corpus of online written conversations extracted from the Ubuntu platform{'}s forums, mailing lists and IRC channels. The corpus is meant to support multi-modality and diachronic studies of online written conversations. We choose to build the corpus around a robust metadata model based upon strong principles, such as the {``}stand off{''} annotation principle. We detail the model, we explain how the data was collected and processed - in terms of meta-data, text and conversation - and we detail the corpus{'}contents through a series of meaningful statistics. A portion of the corpus - about 4,700 sentences from emails, forum posts and chat messages sent in November 2014 - is annotated in terms of dialogue acts and sentiment. We discuss how we adapted our dialogue act taxonomy from the DIT++ annotation scheme and how the data was annotated, before presenting our results as well as a brief qualitative analysis of the annotated data. | |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1280/ |
| https://www.aclweb.org/anthology/L16-1280 | |
| PWC | https://paperswithcode.com/paper/ubuntu-fr-a-large-and-open-corpus-for-multi |
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ANTUSD: A Large Chinese Sentiment Dictionary
| Title | ANTUSD: A Large Chinese Sentiment Dictionary |
| Authors | Shih-Ming Wang, Lun-Wei Ku |
| Abstract | This paper introduces the augmented NTU sentiment dictionary, abbreviated as ANTUSD, which is constructed by collecting sentiment stats of words in several sentiment annotation work. A total of 26,021 words were collected in ANTUSD. For each word, the CopeOpi numerical sentiment score and the number of positive annotation, neutral annotation, negative annotation, non-opinionated annotation, and not-a-word annotation are provided. Words and their sentiment information in ANTUSD have been linked to the Chinese ontology E-HowNet to provide rich semantic information. We demonstrate the usage of ANTUSD in polarity classification of words, and the results show that a superior f-score 98.21 is achieved, which supports the usefulness of the ANTUSD. ANTUSD can be freely obtained through application from NLPSA lab, Academia Sinica: http://academiasinicanlplab.github.io/ |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1428/ |
| https://www.aclweb.org/anthology/L16-1428 | |
| PWC | https://paperswithcode.com/paper/antusd-a-large-chinese-sentiment-dictionary |
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Processing English Island Sentences by Korean EFL Learners
| Title | Processing English Island Sentences by Korean EFL Learners |
| Authors | Yeonkyung Park, Yong-hun Lee |
| Abstract | |
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| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/Y16-2005/ |
| https://www.aclweb.org/anthology/Y16-2005 | |
| PWC | https://paperswithcode.com/paper/processing-english-island-sentences-by-korean |
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Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network
| Title | Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network |
| Authors | Jianfei Yu, Jing Jiang |
| Abstract | Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence and the shortest dependency path representations of relation instances and uses mirror instances to perform pairwise relation classification. We evaluate our proposed models on the SemEval-2010 Task 8 dataset. The empirical results show that with two additional features, our model achieves the state-of-the-art result of F1 score of 85.7. |
| Tasks | Knowledge Base Population, Opinion Mining, Question Answering, Relation Classification, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1223/ |
| https://www.aclweb.org/anthology/C16-1223 | |
| PWC | https://paperswithcode.com/paper/pairwise-relation-classification-with-mirror |
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A Study of Imitation Learning Methods for Semantic Role Labeling
| Title | A Study of Imitation Learning Methods for Semantic Role Labeling |
| Authors | Travis Wolfe, Mark Dredze, Benjamin Van Durme |
| Abstract | |
| Tasks | Imitation Learning, Semantic Role Labeling, Structured Prediction |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/W16-5905/ |
| https://www.aclweb.org/anthology/W16-5905 | |
| PWC | https://paperswithcode.com/paper/a-study-of-imitation-learning-methods-for |
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Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data
| Title | Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data |
| Authors | Raviteja Vemulapalli, Rama Chellappa |
| Abstract | Recently, skeleton-based human action recognition has been receiving significant attention from various research communities due to the availability of depth sensors and real-time depth-based 3D skeleton estimation algorithms. In this work, we use rolling maps for recognizing human actions from 3D skeletal data. The rolling map is a well-defined mathematical concept that has not been explored much by the vision community. First, we represent each skeleton using the relative 3D rotations between various body parts. Since 3D rotations are members of the special orthogonal group SO3, our skeletal representation becomes a point in the Lie group SO3 × … × SO3, which is also a Riemannian manifold. Then, using this representation, we model human actions as curves in this Lie group. Since classification of curves in this non-Euclidean space is a difficult task, we unwrap the action curves onto the Lie algebra so3 × … × so3 (which is a vector space) by combining the logarithm map with rolling maps, and perform classification in the Lie algebra. Experimental results on three action datasets show that the proposed approach performs equally well or better when compared to state-of-the-art. |
| Tasks | Skeleton Based Action Recognition, Temporal Action Localization |
| Published | 2016-06-27 |
| URL | https://doi.org/10.1109/CVPR.2016.484 |
| http://ravitejav.weebly.com/uploads/2/4/7/2/24725306/rolling.pdf | |
| PWC | https://paperswithcode.com/paper/rolling-rotations-for-recognizing-human-1 |
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UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
| Title | UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis |
| Authors | Tom{'a}{\v{s}} Hercig, Tom{'a}{\v{s}} Brychc{'\i}n, Luk{'a}{\v{s}} Svoboda, Michal Konkol |
| Abstract | |
| Tasks | Aspect-Based Sentiment Analysis, Sentiment Analysis |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1055/ |
| https://www.aclweb.org/anthology/S16-1055 | |
| PWC | https://paperswithcode.com/paper/uwb-at-semeval-2016-task-5-aspect-based |
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The Uppsala Corpus of Student Writings: Corpus Creation, Annotation, and Analysis
| Title | The Uppsala Corpus of Student Writings: Corpus Creation, Annotation, and Analysis |
| Authors | Be{'a}ta Megyesi, Jesper N{"a}sman, Anne Palm{'e}r |
| Abstract | The Uppsala Corpus of Student Writings consists of Swedish texts produced as part of a national test of students ranging in age from nine (in year three of primary school) to nineteen (the last year of upper secondary school) who are studying either Swedish or Swedish as a second language. National tests have been collected since 1996. The corpus currently consists of 2,500 texts containing over 1.5 million tokens. Parts of the texts have been annotated on several linguistic levels using existing state-of-the-art natural language processing tools. In order to make the corpus easy to interpret for scholars in the humanities, we chose the CoNLL format instead of an XML-based representation. Since spelling and grammatical errors are common in student writings, the texts are automatically corrected while keeping the original tokens in the corpus. Each token is annotated with part-of-speech and morphological features as well as syntactic structure. The main purpose of the corpus is to facilitate the systematic and quantitative empirical study of the writings of various student groups based on gender, geographic area, age, grade awarded or a combination of these, synchronically or diachronically. The intention is for this to be a monitor corpus, currently under development. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1509/ |
| https://www.aclweb.org/anthology/L16-1509 | |
| PWC | https://paperswithcode.com/paper/the-uppsala-corpus-of-student-writings-corpus |
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Cross-domain Text Classification with Multiple Domains and Disparate Label Sets
| Title | Cross-domain Text Classification with Multiple Domains and Disparate Label Sets |
| Authors | Himanshu Sharad Bhatt, Manjira Sinha, Shourya Roy |
| Abstract | |
| Tasks | Cross-Domain Text Classification, Sentiment Analysis, Text Classification, Transfer Learning |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1155/ |
| https://www.aclweb.org/anthology/P16-1155 | |
| PWC | https://paperswithcode.com/paper/cross-domain-text-classification-with |
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Dialog-based Language Learning
| Title | Dialog-based Language Learning |
| Authors | Jason E. Weston |
| Abstract | A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of (Weston et al., 2015) and large-scale question answering from (Dodge et al., 2015). We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher’s response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all. |
| Tasks | Machine Translation, Question Answering |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6264-dialog-based-language-learning |
| http://papers.nips.cc/paper/6264-dialog-based-language-learning.pdf | |
| PWC | https://paperswithcode.com/paper/dialog-based-language-learning-1 |
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Constrained Multi-Task Learning for Automated Essay Scoring
| Title | Constrained Multi-Task Learning for Automated Essay Scoring |
| Authors | Ronan Cummins, Meng Zhang, Ted Briscoe |
| Abstract | |
| Tasks | Learning-To-Rank, Multi-Task Learning |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1075/ |
| https://www.aclweb.org/anthology/P16-1075 | |
| PWC | https://paperswithcode.com/paper/constrained-multi-task-learning-for-automated |
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News Citation Recommendation with Implicit and Explicit Semantics
| Title | News Citation Recommendation with Implicit and Explicit Semantics |
| Authors | Hao Peng, Jing Liu, Chin-Yew Lin |
| Abstract | |
| Tasks | Learning-To-Rank, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1037/ |
| https://www.aclweb.org/anthology/P16-1037 | |
| PWC | https://paperswithcode.com/paper/news-citation-recommendation-with-implicit |
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