[PDF] Top 20 IJCNLP 2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases
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IJCNLP 2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases
... This task seeks to evaluate the capability of sys- tems for predicting dimensional sentiments of Chinese words and phrases. For a given word or phrase, participants were asked to provide a ... See full document
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NLPSA at IJCNLP 2017 Task 2: Imagine Scenario: Leveraging Supportive Images for Dimensional Sentiment Analysis
... Categorical sentiment classification has drawn much attention in the field of NLP, while less work has been conducted for di- mensional sentiment analysis ...for IJCNLP 2017 ... See full document
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THU NGN at IJCNLP 2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM
... and phrases jointly. We segment al- l words and phrases and pad them to the same length for joint ...this task show the effectiveness of our ... See full document
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LDCCNLP at IJCNLP 2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning
... Words that always appear together are semanti- cally similar, and their meaning may be reflected by co-occurrence context (Chen and You, 2002). Through matrix co-occurrence, we can also train low-dimensional word ... See full document
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NCYU at IJCNLP 2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases using Vector Representations
... (2) where and denote the original value of va- lence and arousal and and represent the value after calculating. C is center value of the va- lence and arousal and 、 is a real-number show the degree and negative ... See full document
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NCTU NTUT at IJCNLP 2017 Task 2: Deep Phrase Embedding using bi LSTMs for Valence Arousal Ratings Prediction of Chinese Phrases
... In this paper, we had proposed and evaluated various order-aware embedding representations in both word- and phrase-levels. It is found that the order-aware Word2Vec and LSTM-based Phrase2Vec all could improve the ... See full document
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Alibaba at IJCNLP 2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases
... In the final submission for word level task, we use all features above. Run2 is an average boosted neural network applied with word embedding fea- tures on cw2vec and CWE while Run1 is gener- ated on cw2vec alone. ... See full document
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Proceedings of the IJCNLP 2017, Shared Tasks
... • Task 2: Dimensional Sentiment Analysis for Chinese Phrases. Given a word or a phrase, participants were asked to generate a real-valued score between 1 and 9, indicating ... See full document
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CKIP at IJCNLP 2017 Task 2: Neural Valence Arousal Prediction for Phrases
... shared task calls for systems that automatically predict VA for Chinese phrases to overcome the scarcity of labeled Chinese phrases and ...VA-annotated phrases with their mod- ... See full document
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MainiwayAI at IJCNLP 2017 Task 2: Ensembles of Deep Architectures for Valence Arousal Prediction
... This paper introduces Mainiway AI Labs submitted system for the IJCNLP 2017 shared task on Dimensional Sentiment Analysis of Chinese Phrases (DSAP), and related ... See full document
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CIAL at IJCNLP 2017 Task 2: An Ensemble Valence Arousal Analysis System for Chinese Words and Phrases
... The system we developed for DSAW integrates E-HowNet and word embeddings with K-Nearest Neighbors in valence dimension. Support vector regression and linear regression in arousal dimen- sions. The evaluation results show ... See full document
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YNU HPCC at IJCNLP 2017 Task 4: Attention based Bi directional GRU Model for Customer Feedback Analysis Task of English
... fundamental task in provid- ing good customer service. The goal of task 4 of the custom feedback analysis of IJCNLP-2017 is to train classifiers for the detection of meaning in customer ... See full document
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ADAPT at IJCNLP 2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task
... fine-grained sentiment classifica- tions of user ...Their task is performed in three steps: (i) min- ing product features that have been commented on by customers, (ii) identifying opinion sentences in each ... See full document
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IIIT H at IJCNLP 2017 Task 3: A Bidirectional LSTM Approach for Review Opinion Diversification
... We can remove the blank reviews and train our system for further analysis. We intend to use char- acter embedding along with the word embeddings to get better representation of a sequence, in this case a review. ... See full document
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IIIT H at IJCNLP 2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches
... Sentiment analysis on customer feedback data by (Gamon, 2004) using linear SVM for classifi- cation had yielded satisfying ...and 2 grouped together versus 3 and 4 grouped together, both using top 2k ... See full document
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IJCNLP 2017 Task 4: Customer Feedback Analysis
... feedback analysis. First, differ- ent kinds of sentiment categorizations that were used in sentiment analysis in Microsoft Office and many other institutions (Salameh et ...feedback ... See full document
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IJCNLP 2017 Task 1: Chinese Grammatical Error Diagnosis
... rule-based analysis (Lee et ...linguistic analysis, the ICCE-2014 workshop on Natural Language Processing Techniques for Educational Applications (NLP-TEA) organized a shared task on diagnosing ... See full document
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SentiNLP at IJCNLP 2017 Task 4: Customer Feedback Analysis Using a Bi LSTM CNN Model
... 2014), sentiment analysis (Ir- soy and Cardie, 2014; Liu et ...al., 2017) can provide word vector representation that captures semantic and syntactic information of ... See full document
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IITP at IJCNLP 2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network
... provides services and softwares to help business firms to manage and retain their customers. Identifying a category for customer feedback re- quires deep semantic analysis of the lexicons to identify the emotions ... See full document
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CoSACT: A Collaborative Tool for Fine Grained Sentiment Annotation and Consolidation of Text
... 1. Sentiment granularity - Currently, the majority of sen- timent datasets is annotated in a categorical fashion with polarity (positive, negative, neutral) [Saif et ...the sentiment polarity (positive, ... See full document
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