Paper Group NANR 37
Monitoring Tweets for Depression to Detect At-risk Users. Using Teacher-Student Model For Emotional Speech Recognition[In Chinese]. A Cognition Based Attention Model for Sentiment Analysis. Extracting Drug-Drug Interactions with Attention CNNs. Zipf’s and Benford’s laws in Twitter hashtags. Event Ordering with a Generalized Model for Sieve Predicti …
Monitoring Tweets for Depression to Detect At-risk Users
| Title | Monitoring Tweets for Depression to Detect At-risk Users |
| Authors | Zunaira Jamil, Diana Inkpen, Prasadith Buddhitha, Kenton White |
| Abstract | We propose an automated system that can identify at-risk users from their public social media activity, more specifically, from Twitter. The data that we collected is from the {#}BellLetsTalk campaign, which is a wide-reaching, multi-year program designed to break the silence around mental illness and support mental health across Canada. To achieve our goal, we trained a user-level classifier that can detect at-risk users that achieves a reasonable precision and recall. We also trained a tweet-level classifier that predicts if a tweet indicates depression. This task was much more difficult due to the imbalanced data. In the dataset that we labeled, we came across 5{%} depression tweets and 95{%} non-depression tweets. To handle this class imbalance, we used undersampling methods. The resulting classifier had high recall, but low precision. Therefore, we only use this classifier to compute the estimated percentage of depressed tweets and to add this value as a feature for the user-level classifier. |
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| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-3104/ |
| https://www.aclweb.org/anthology/W17-3104 | |
| PWC | https://paperswithcode.com/paper/monitoring-tweets-for-depression-to-detect-at |
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Using Teacher-Student Model For Emotional Speech Recognition[In Chinese]
| Title | Using Teacher-Student Model For Emotional Speech Recognition[In Chinese] |
| Authors | Po-Wei Hsiao, Po-Chen Hsieh, Chia-Ping Chen |
| Abstract | |
| Tasks | Speech Recognition |
| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/O17-1024/ |
| https://www.aclweb.org/anthology/O17-1024 | |
| PWC | https://paperswithcode.com/paper/using-teacher-student-model-for-emotional |
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A Cognition Based Attention Model for Sentiment Analysis
| Title | A Cognition Based Attention Model for Sentiment Analysis |
| Authors | Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, Chu-Ren Huang |
| Abstract | Attention models are proposed in sentiment analysis because some words are more important than others. However,most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks. |
| Tasks | Eye Tracking, Feature Engineering, Product Recommendation, Sentiment Analysis |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1048/ |
| https://www.aclweb.org/anthology/D17-1048 | |
| PWC | https://paperswithcode.com/paper/a-cognition-based-attention-model-for |
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Extracting Drug-Drug Interactions with Attention CNNs
| Title | Extracting Drug-Drug Interactions with Attention CNNs |
| Authors | Masaki Asada, Makoto Miwa, Yutaka Sasaki |
| Abstract | We propose a novel attention mechanism for a Convolutional Neural Network (CNN)-based Drug-Drug Interaction (DDI) extraction model. CNNs have been shown to have a great potential on DDI extraction tasks; however, attention mechanisms, which emphasize important words in the sentence of a target-entity pair, have not been investigated with the CNNs despite the fact that attention mechanisms are shown to be effective for a general domain relation classification task. We evaluated our model on the Task 9.2 of the DDIExtraction-2013 shared task. As a result, our attention mechanism improved the performance of our base CNN-based DDI model, and the model achieved an F-score of 69.12{%}, which is competitive with the state-of-the-art models. |
| Tasks | Feature Engineering, Relation Classification, Relation Extraction |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2302/ |
| https://www.aclweb.org/anthology/W17-2302 | |
| PWC | https://paperswithcode.com/paper/extracting-drug-drug-interactions-with |
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Zipf’s and Benford’s laws in Twitter hashtags
| Title | Zipf’s and Benford’s laws in Twitter hashtags |
| Authors | Jos{'e} Alberto P{'e}rez Meli{'a}n, J. Alberto Conejero, C{`e}sar Ferri Ram{'\i}rez |
| Abstract | Social networks have transformed communication dramatically in recent years through the rise of new platforms and the development of a new language of communication. This landscape requires new forms to describe and predict the behaviour of users in networks. This paper presents an analysis of the frequency distribution of hashtag popularity in Twitter conversations. Our objective is to determine if these frequency distribution follow some well-known frequency distribution that many real-life sets of numerical data satisfy. In particular, we study the similarity of frequency distribution of hashtag popularity with respect to Zipf{'}s law, an empirical law referring to the phenomenon that many types of data in social sciences can be approximated with a Zipfian distribution. Additionally, we also analyse Benford{'}s law, is a special case of Zipf{'}s law, a common pattern about the frequency distribution of leading digits. In order to compute correctly the frequency distribution of hashtag popularity, we need to correct many spelling errors that Twitter{'}s users introduce. For this purpose we introduce a new filter to correct hashtag mistake based on string distances. The experiments obtained employing datasets of Twitter streams generated under controlled conditions show that Benford{'}s law and Zipf{'}s law can be used to model hashtag frequency distribution. |
| Tasks | |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-4009/ |
| https://www.aclweb.org/anthology/E17-4009 | |
| PWC | https://paperswithcode.com/paper/zipfs-and-benfords-laws-in-twitter-hashtags |
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Event Ordering with a Generalized Model for Sieve Prediction Ranking
| Title | Event Ordering with a Generalized Model for Sieve Prediction Ranking |
| Authors | Bill McDowell, Nathanael Chambers, Alex Ororbia II, er, David Reitter |
| Abstract | This paper improves on several aspects of a sieve-based event ordering architecture, CAEVO (Chambers et al., 2014), which creates globally consistent temporal relations between events and time expressions. First, we examine the usage of word embeddings and semantic role features. With the incorporation of these new features, we demonstrate a 5{%} relative F1 gain over our replicated version of CAEVO. Second, we reformulate the architecture{'}s sieve-based inference algorithm as a prediction reranking method that approximately optimizes a scoring function computed using classifier precisions. Within this prediction reranking framework, we propose an alternative scoring function, showing an 8.8{%} relative gain over the original CAEVO. We further include an in-depth analysis of one of the main datasets that is used to evaluate temporal classifiers, and we show how despite using the densest corpus, there is still a danger of overfitting. While this paper focuses on temporal ordering, its results are applicable to other areas that use sieve-based architectures. |
| Tasks | Word Embeddings |
| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/I17-1085/ |
| https://www.aclweb.org/anthology/I17-1085 | |
| PWC | https://paperswithcode.com/paper/event-ordering-with-a-generalized-model-for |
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A Geometric Method for Detecting Semantic Coercion
| Title | A Geometric Method for Detecting Semantic Coercion |
| Authors | Stephen McGregor, Elisabetta Jezek, Matthew Purver, Geraint Wiggins |
| Abstract | |
| Tasks | |
| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-6813/ |
| https://www.aclweb.org/anthology/W17-6813 | |
| PWC | https://paperswithcode.com/paper/a-geometric-method-for-detecting-semantic |
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RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks
| Title | RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks |
| Authors | Sudipta Kar, Suraj Maharjan, Thamar Solorio |
| Abstract | In this paper, we present our systems for the {``}SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News{''}. In our system, we combined hand-engineered lexical, sentiment and metadata features, the representations learned from Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU) with Attention model applied on top. With this architecture we obtained weighted cosine similarity scores of 0.72 and 0.74 for subtask-1 and subtask-2, respectively. Using the official scoring system, our system ranked the second place for subtask-2 and eighth place for the subtask-1. It ranked first for both of the subtasks by the scores achieved by an alternate scoring system. | |
| Tasks | Sentiment Analysis |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2150/ |
| https://www.aclweb.org/anthology/S17-2150 | |
| PWC | https://paperswithcode.com/paper/ritual-uh-at-semeval-2017-task-5-sentiment |
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IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks
| Title | IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks |
| Authors | Anders Bj{"o}rkelund, Agnieszka Falenska, Xiang Yu, Jonas Kuhn |
| Abstract | This paper presents the IMS contribution to the CoNLL 2017 Shared Task. In the preprocessing step we employed a CRF POS/morphological tagger and a neural tagger predicting supertags. On some languages, we also applied word segmentation with the CRF tagger and sentence segmentation with a perceptron-based parser. For parsing we took an ensemble approach by blending multiple instances of three parsers with very different architectures. Our system achieved the third place overall and the second place for the surprise languages. |
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| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/K17-3004/ |
| https://www.aclweb.org/anthology/K17-3004 | |
| PWC | https://paperswithcode.com/paper/ims-at-the-conll-2017-ud-shared-task-crfs-and |
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Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation
| Title | Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation |
| Authors | Ivan Vuli{'c}, Douwe Kiela, Anna Korhonen |
| Abstract | Recent work on evaluating representation learning architectures in NLP has established a need for evaluation protocols based on subconscious cognitive measures rather than manually tailored intrinsic similarity and relatedness tasks. In this work, we propose a novel evaluation framework that enables large-scale evaluation of such architectures in the free word association (WA) task, which is firmly grounded in cognitive theories of human semantic representation. This evaluation is facilitated by the existence of large manually constructed repositories of word association data. In this paper, we (1) present a detailed analysis of the new quantitative WA evaluation protocol, (2) suggest new evaluation metrics for the WA task inspired by its direct analogy with information retrieval problems, (3) evaluate various state-of-the-art representation models on this task, and (4) discuss the relationship between WA and prior evaluations of semantic representation with well-known similarity and relatedness evaluation sets. We have made the WA evaluation toolkit publicly available. |
| Tasks | Information Retrieval, Representation Learning, Semantic Textual Similarity |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-1016/ |
| https://www.aclweb.org/anthology/E17-1016 | |
| PWC | https://paperswithcode.com/paper/evaluation-by-association-a-systematic-study |
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基於聽覺感知模型之類神經網路及其在語者識別上之應用 (Two-stage Attentional Auditory Model Inspired Neural Network and Its Application to Speaker Identification) [In Chinese]
| Title | 基於聽覺感知模型之類神經網路及其在語者識別上之應用 (Two-stage Attentional Auditory Model Inspired Neural Network and Its Application to Speaker Identification) [In Chinese] |
| Authors | Yu-Wen Lo, Yuan-Fu Liao, Tai-Shih Chi |
| Abstract | |
| Tasks | Speaker Identification |
| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/O17-1031/ |
| https://www.aclweb.org/anthology/O17-1031 | |
| PWC | https://paperswithcode.com/paper/ao14e12eoc-aa1ii-cc2iaaa-eaei14aa-a1c-two |
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Coded Distributed Computing for Inverse Problems
| Title | Coded Distributed Computing for Inverse Problems |
| Authors | Yaoqing Yang, Pulkit Grover, Soummya Kar |
| Abstract | Computationally intensive distributed and parallel computing is often bottlenecked by a small set of slow workers known as stragglers. In this paper, we utilize the emerging idea of coded computation'' to design a novel error-correcting-code inspired technique for solving linear inverse problems under specific iterative methods in a parallelized implementation affected by stragglers. Example machine-learning applications include inverse problems such as personalized PageRank and sampling on graphs. We provably show that our coded-computation technique can reduce the mean-squared error under a computational deadline constraint. In fact, the ratio of mean-squared error of replication-based and coded techniques diverges to infinity as the deadline increases. Our experiments for personalized PageRank performed on real systems and real social networks show that this ratio can be as large as $10^4$. Further, unlike coded-computation techniques proposed thus far, our strategy combines outputs of all workers, including the stragglers, to produce more accurate estimates at the computational deadline. This also ensures that the accuracy degrades gracefully’’ in the event that the number of stragglers is large. |
| Tasks | |
| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6673-coded-distributed-computing-for-inverse-problems |
| http://papers.nips.cc/paper/6673-coded-distributed-computing-for-inverse-problems.pdf | |
| PWC | https://paperswithcode.com/paper/coded-distributed-computing-for-inverse |
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Incorporating visual features into word embeddings: A bimodal autoencoder-based approach
| Title | Incorporating visual features into word embeddings: A bimodal autoencoder-based approach |
| Authors | Mika Hasegawa, Tetsunori Kobayashi, Yoshihiko Hayashi |
| Abstract | |
| Tasks | Representation Learning, Word Embeddings |
| Published | 2017-01-01 |
| URL | https://www.aclweb.org/anthology/W17-6912/ |
| https://www.aclweb.org/anthology/W17-6912 | |
| PWC | https://paperswithcode.com/paper/incorporating-visual-features-into-word |
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Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases
| Title | Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases |
| Authors | Xin Zhou, Jian Wang, Xu Xie, Changlong Sun, Luo Si |
| Abstract | This paper introduces Team Alibaba{'}s systems participating IJCNLP 2017 shared task No. 2 Dimensional Sentiment Analysis for Chinese Phrases (DSAP). The systems mainly utilize a multi-layer neural networks, with multiple features input such as word embedding, part-of-speech-tagging (POST), word clustering, prefix type, character embedding, cross sentiment input, and AdaBoost method for model training. For word level task our best run achieved MAE 0.545 (ranked 2nd), PCC 0.892 (ranked 2nd) in valence prediction and MAE 0.857 (ranked 1st), PCC 0.678 (ranked 2nd) in arousal prediction. For average performance of word and phrase task we achieved MAE 0.5355 (ranked 3rd), PCC 0.8965 (ranked 3rd) in valence prediction and MAE 0.661 (ranked 3rd), PCC 0.766 (ranked 2nd) in arousal prediction. In the final our submitted system achieved 2nd in mean rank. |
| Tasks | Feature Engineering, Part-Of-Speech Tagging, Sentiment Analysis |
| Published | 2017-12-01 |
| URL | https://www.aclweb.org/anthology/I17-4016/ |
| https://www.aclweb.org/anthology/I17-4016 | |
| PWC | https://paperswithcode.com/paper/alibaba-at-ijcnlp-2017-task-2-a-boosted-deep |
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Understanding Idiomatic Variation
| Title | Understanding Idiomatic Variation |
| Authors | Kristina Geeraert, R. Harald Baayen, John Newman |
| Abstract | This study investigates the processing of idiomatic variants through an eye-tracking experiment. Four types of idiom variants were included, in addition to the canonical form and the literal meaning. Results suggest that modifications to idioms, modulo obvious effects of length differences, are not more difficult to process than the canonical forms themselves. This fits with recent corpus findings. |
| Tasks | Eye Tracking, Semantic Textual Similarity |
| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/W17-1710/ |
| https://www.aclweb.org/anthology/W17-1710 | |
| PWC | https://paperswithcode.com/paper/understanding-idiomatic-variation |
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