Paper Group NANR 16
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets. Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena. Cross-Lingual Generation and Evaluation of a Wide-Coverage Lexical Semantic Resource. System Description of Supervised and Unsupervised Neural Machine Tra …
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets
| Title | Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets |
| Authors | Kushagra Singh, Indira Sen, Ponnurangam Kumaraguru |
| Abstract | While growing code-mixed content on Online Social Networks(OSN) provides a fertile ground for studying various aspects of code-mixing, the lack of automated text analysis tools render such studies challenging. To meet this challenge, a family of tools for analyzing code-mixed data such as language identifiers, parts-of-speech (POS) taggers, chunkers have been developed. Named Entity Recognition (NER) is an important text analysis task which is not only informative by itself, but is also needed for downstream NLP tasks such as semantic role labeling. In this work, we present an exploration of automatic NER of code-mixed data. We compare our method with existing off-the-shelf NER tools for social media content,and find that our systems outperforms the best baseline by 33.18 {%} (F1 score). |
| Tasks | Abuse Detection, Chunking, Language Identification, Named Entity Recognition, Semantic Role Labeling |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-3008/ |
| https://www.aclweb.org/anthology/P18-3008 | |
| PWC | https://paperswithcode.com/paper/language-identification-and-named-entity |
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Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena
| Title | Automatic and Manual Web Annotations in an Infrastructure to handle Fake News and other Online Media Phenomena |
| Authors | Georg Rehm, Julian Moreno-Schneider, Peter Bourgonje |
| Abstract | |
| Tasks | News Annotation, Rumour Detection |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1384/ |
| https://www.aclweb.org/anthology/L18-1384 | |
| PWC | https://paperswithcode.com/paper/automatic-and-manual-web-annotations-in-an |
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Cross-Lingual Generation and Evaluation of a Wide-Coverage Lexical Semantic Resource
| Title | Cross-Lingual Generation and Evaluation of a Wide-Coverage Lexical Semantic Resource |
| Authors | Attila Nov{'a}k, Borb{'a}la Nov{'a}k |
| Abstract | |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1007/ |
| https://www.aclweb.org/anthology/L18-1007 | |
| PWC | https://paperswithcode.com/paper/cross-lingual-generation-and-evaluation-of-a |
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System Description of Supervised and Unsupervised Neural Machine Translation Approaches from ``NL Processing’’ Team at DeepHack.Babel Task
| Title | System Description of Supervised and Unsupervised Neural Machine Translation Approaches from ``NL Processing’’ Team at DeepHack.Babel Task | |
| Authors | Ilya Gusev, Artem Oboturov |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-03-01 |
| URL | https://www.aclweb.org/anthology/W18-2206/ |
| https://www.aclweb.org/anthology/W18-2206 | |
| PWC | https://paperswithcode.com/paper/system-description-of-supervised-and |
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BlogSet-BR: A Brazilian Portuguese Blog Corpus
| Title | BlogSet-BR: A Brazilian Portuguese Blog Corpus |
| Authors | Henrique Santos, Vinicius Woloszyn, Renata Vieira |
| Abstract | |
| Tasks | Opinion Mining, Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1105/ |
| https://www.aclweb.org/anthology/L18-1105 | |
| PWC | https://paperswithcode.com/paper/blogset-br-a-brazilian-portuguese-blog-corpus |
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Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis
| Title | Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis |
| Authors | Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent |
| Abstract | Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions. For models with many parameters, the covari- ance matrix they are based on becomes gigantic, making them inapplicable in their original form. This has motivated research into both simple diagonal approxima- tions and more sophisticated factored approximations such as KFAC (Heskes, 2000; Martens & Grosse, 2015; Grosse & Martens, 2016). In the present work we draw inspiration from both to propose a novel approximation that is provably better than KFAC and amendable to cheap partial updates. It consists in tracking a diagonal variance, not in parameter coordinates, but in a Kronecker-factored eigenbasis, in which the diagonal approximation is likely to be more effective. Experiments show improvements over KFAC in optimization speed for several deep network architectures. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/8164-fast-approximate-natural-gradient-descent-in-a-kronecker-factored-eigenbasis |
| http://papers.nips.cc/paper/8164-fast-approximate-natural-gradient-descent-in-a-kronecker-factored-eigenbasis.pdf | |
| PWC | https://paperswithcode.com/paper/fast-approximate-natural-gradient-descent-in |
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Improving String Processing for Temporal Relations
| Title | Improving String Processing for Temporal Relations |
| Authors | David Woods, Fern, Tim o |
| Abstract | |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4709/ |
| https://www.aclweb.org/anthology/W18-4709 | |
| PWC | https://paperswithcode.com/paper/improving-string-processing-for-temporal |
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Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification
| Title | Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification |
| Authors | Raksha Sharma, Pushpak Bhattacharyya, D, S apat, ipan, Himanshu Sharad Bhatt |
| Abstract | Getting manually labeled data in each domain is always an expensive and a time consuming task. Cross-domain sentiment analysis has emerged as a demanding concept where a labeled source domain facilitates a sentiment classifier for an unlabeled target domain. However, polarity orientation (positive or negative) and the significance of a word to express an opinion often differ from one domain to another domain. Owing to these differences, cross-domain sentiment classification is still a challenging task. In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification. We present a novel approach based on χ2 test and cosine-similarity between context vector of words to identify polarity preserving significant words across domains. Furthermore, we show that a weighted ensemble of the classifiers enhances the cross-domain classification performance. |
| Tasks | Domain Adaptation, Sentiment Analysis, Transfer Learning |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1089/ |
| https://www.aclweb.org/anthology/P18-1089 | |
| PWC | https://paperswithcode.com/paper/identifying-transferable-information-across |
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Quantifying Context Overlap for Training Word Embeddings
| Title | Quantifying Context Overlap for Training Word Embeddings |
| Authors | Yimeng Zhuang, Jinghui Xie, Yinhe Zheng, Xuan Zhu |
| Abstract | Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations. In this paper, a metric is designed to estimate second order co-occurrence relations based on context overlap. The estimated values are further used as the augmented data to enhance the learning of word embeddings by joint training with existing neural word embedding models. Experimental results show that better word vectors can be obtained for word similarity tasks and some downstream NLP tasks by the enhanced approach. |
| Tasks | Dimensionality Reduction, Language Modelling, Learning Word Embeddings, Representation Learning, Semantic Composition, Word Embeddings |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1057/ |
| https://www.aclweb.org/anthology/D18-1057 | |
| PWC | https://paperswithcode.com/paper/quantifying-context-overlap-for-training-word |
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Transc&Anno: A Graphical Tool for the Transcription and On-the-Fly Annotation of Handwritten Documents
| Title | Transc&Anno: A Graphical Tool for the Transcription and On-the-Fly Annotation of Handwritten Documents |
| Authors | Nadezda Okinina, Lionel Nicolas, Verena Lyding |
| Abstract | |
| Tasks | Language Acquisition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1112/ |
| https://www.aclweb.org/anthology/L18-1112 | |
| PWC | https://paperswithcode.com/paper/transcanno-a-graphical-tool-for-the |
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Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning
| Title | Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning |
| Authors | Fengyu Guo, Ruifang He, Di Jin, Jianwu Dang, Longbiao Wang, Xiangang Li |
| Abstract | Implicit discourse relation recognition aims to understand and annotate the latent relations between two discourse arguments, such as temporal, comparison, etc. Most previous methods encode two discourse arguments separately, the ones considering pair specific clues ignore the bidirectional interactions between two arguments and the sparsity of pair patterns. In this paper, we propose a novel neural Tensor network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition. (1) We mine the most correlated word pairs from two discourse arguments to model pair specific clues, and integrate them as interactive attention into argument representations produced by the bidirectional long short-term memory network. Meanwhile, (2) the neural tensor network with sparse constraint is proposed to explore the deeper and the more important pair patterns so as to fully recognize discourse relations. The experimental results on PDTB show that our proposed TIASL framework is effective. |
| Tasks | Sparse Learning, Text Summarization |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1046/ |
| https://www.aclweb.org/anthology/C18-1046 | |
| PWC | https://paperswithcode.com/paper/implicit-discourse-relation-recognition-using |
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ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets
| Title | ARB-SEN at SemEval-2018 Task1: A New Set of Features for Enhancing the Sentiment Intensity Prediction in Arabic Tweets |
| Authors | El Moatez Billah Nagoudi |
| Abstract | This article describes our proposed Arabic Sentiment Analysis system named ARB-SEN. This system is designed for the International Workshop on Semantic Evaluation 2018 (SemEval-2018), Task1: Affect in Tweets. ARB-SEN proposes two supervised models to estimate the sentiment intensity in Arabic tweets. Both models use a set of features including sentiment lexicon, negation, word embedding and emotion symbols features. Our system combines these features to assist the sentiment analysis task. ARB-SEN system achieves a correlation score of 0.720, ranking 6th among all participants in the valence intensity regression (V-reg) for the Arabic sub-task organized within the SemEval 2018 evaluation campaign. |
| Tasks | Arabic Sentiment Analysis, Sentiment Analysis |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1055/ |
| https://www.aclweb.org/anthology/S18-1055 | |
| PWC | https://paperswithcode.com/paper/arb-sen-at-semeval-2018-task1-a-new-set-of |
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Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese
| Title | Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese |
| Authors | Chi-Yen Chen, Wei-Yun Ma |
| Abstract | |
| Tasks | Knowledge Graphs, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1132/ |
| https://www.aclweb.org/anthology/L18-1132 | |
| PWC | https://paperswithcode.com/paper/word-embedding-evaluation-datasets-and |
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Webly Supervised Joint Embedding for Cross-Modal lmage-Text Retrieval
| Title | Webly Supervised Joint Embedding for Cross-Modal lmage-Text Retrieval |
| Authors | Niluthpol Chowdhury Mithun, Rameswar Panda, Vagelis Papalexakis, Amit K.Roy-Chowdhury |
| Abstract | Cross-modal retrieval between visual data and natural language description remains a long-standing challenge in multimedia. While recent image-text retrieval methods offer great promise by learning deep representations aligned across modalities, most of these meth-ods are plagued by the issue of training with small-scale datasets covering a limited number of images with ground-truth sentences. Moreover, it is extremely expensive to create a larger dataset by annotating millions of training images with ground-truth sentences and may lead to a biased model. Inspired by the recent success of web-supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn robust image-text joint representation. Specifically, our main idea is to leverage web images and corresponding tags, along with fully annotated datasets, in training for learning the visual-semantic joint embedding. We propose a two-stage approach for the task that can augment a typical supervised pair-wise ranking loss based formulation with weakly-annotated web images to learn a more robust visual-semantic embedding. Extensive experiments on two standard benchmark datasets demonstrate that our method achieves a significant performance gain in image-text retrieval compared to state-of-the-art approaches. |
| Tasks | Cross-Modal Retrieval |
| Published | 2018-10-01 |
| URL | https://dl.acm.org/doi/abs/10.1145/3240508.3240712 |
| https://par.nsf.gov/servlets/purl/10064325 | |
| PWC | https://paperswithcode.com/paper/webly-supervised-joint-embedding-for-cross-1 |
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Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition
| Title | Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition |
| Authors | Yansong Tang, Yi Tian, Jiwen Lu, Peiyang Li, Jie Zhou |
| Abstract | In this paper, we propose a deep progressive reinforcement learning (DPRL) method for action recognition in skeleton-based videos, which aims to distil the most informative frames and discard ambiguous frames in sequences for recognizing actions. Since the choices of selecting representative frames are multitudinous for each video, we model the frame selection as a progressive process through deep reinforcement learning, during which we progressively adjust the chosen frames by taking two important factors into account: (1) the quality of the selected frames and (2) the relationship between the selected frames to the whole video. Moreover, considering the topology of human body inherently lies in a graph-based structure, where the vertices and edges represent the hinged joints and rigid bones respectively, we employ the graph-based convolutional neural network to capture the dependency between the joints for action recognition. Our approach achieves very competitive performance on three widely used benchmarks. |
| Tasks | Skeleton Based Action Recognition, Temporal Action Localization |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Tang_Deep_Progressive_Reinforcement_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Tang_Deep_Progressive_Reinforcement_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deep-progressive-reinforcement-learning-for |
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