[PDF] Top 20 IJCNLP 2017 Task 4: Customer Feedback Analysis
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IJCNLP 2017 Task 4: Customer Feedback Analysis
... for customer feedback ...sentiment analysis in Microsoft Office and many other institutions (Salameh et ...2015) Customer feedback analysis is now an industry in its own right ... See full document
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SentiNLP at IJCNLP 2017 Task 4: Customer Feedback Analysis Using a Bi LSTM CNN Model
... shared task on customer feedback analysis, several sentences of annotated and unan- notated customer feedback in English were pre- pared, with a total of 3065 training texts, 500 ... See full document
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IIIT H at IJCNLP 2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches
... (Bentley and Batra, 2016) dealt with Microsoft Of- fice users feedback, on which they applied var- ious machine learning techniques. They imple- mented classification techniques on labeled data and applied ... See full document
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ADAPT at IJCNLP 2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task
... of customer feedback are extracted from Microsoft Office customers in four languages, ...international customer feedback analysis. The task is to develop a system in order to ... 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
... JCNLP 2017 shared task 4, for predict- ing the tags of unseen customer feedback sentences, such as comments, complaints, bugs, requests, and meaningless and un- determined ... See full document
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All In 1 at IJCNLP 2017 Task 4: Short Text Classification with One Model for All Languages
... the IJCNLP 2017 shared task on customer feedback analysis, in which data from four languages was available (English, French, Japanese and ... See full document
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IITP at IJCNLP 2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network
... Analyzing customer feedback is the best way to channelize the data into new mar- keting strategies that benefit entrepreneurs as well as ...the customer behavior is in great ...analyzing ... See full document
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NCYU at IJCNLP 2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases using Vector Representations
... For evaluating for the proposed approach, a sys- tem was developed. The data preparation, metric and results are illustrated as following sections. Based on Chinese valence-arousal words 2.0 (CVAW 2.0) developed by (Yu ... See full document
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CKIP at IJCNLP 2017 Task 2: Neural Valence Arousal Prediction for Phrases
... Sentiment Analysis for Chinese Phrases (DSAP) share task of IJCNLP ...This task calls for systems that can predict the valence and the arousal of Chinese phrases, which are real val- ues ... See full document
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IJCNLP 2017 Task 3: Review Opinion Diversification (RevOpiD 2017)
... Despite the limitations in previous opinion min- ing evaluations, a recurring and fundamental fea- ture in most of these methodologies is the iden- tification of nuggets (in summarization jargon) or subtopics (in ... See full document
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LDCCNLP at IJCNLP 2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning
... Sentiment analysis on Chinese text has in- tensively studied. The basic task for related research is to construct an affective lexicon and thereby predict emotional scores of different ... See full document
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Proceedings of the IJCNLP 2017, Shared Tasks
... • Task 4: Customer Feedback ...in customer feedback in English, French, Spanish, and Japanese: comment, request, bug, complaint, meaningless, and ... See full document
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Bingo at IJCNLP 2017 Task 4: Augmenting Data using Machine Translation for Cross linguistic Customer Feedback Classification
... 1/8 and 2/7 on English, Spanish, French and Japanese datasets respectively. The inclusion of word embeddings and machine translation offered the largest boosts to system performance. Future directions for work in this ... See full document
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MainiwayAI at IJCNLP 2017 Task 2: Ensembles of Deep Architectures for Valence Arousal Prediction
... The final competition results are shown in Table 3, with our system highlighted. The ensemble ap- proach of Run 2 achieved higher results than the rectangular network of Run 1, which is consistent with our experimental ... See full document
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JUNLP at IJCNLP 2017 Task 3: A Rank Prediction Model for Review Opinion Diversification
... In order to achieve this objective, we have cho- sen to observe the sentiments expressed in each opinion. We have also perceived that both posi- tive and negative opinions have to be included in order to increase the ... See full document
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Alibaba at IJCNLP 2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task
... CGED Task gives Chinese NLP researchers an opportunity to build and develop the Chinese Grammatical Error Diag- nosis System, compare their results and exchange their learning ... See full document
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IIIT H at IJCNLP 2017 Task 3: A Bidirectional LSTM Approach for Review Opinion Diversification
... The Review Opinion Diversification (Revopid-2017) shared task (Singh et al., 2017b) focuses on selecting top-k reviews from a set of reviews for a particular product based on a specific criteria. In this ... See full document
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CVTE at IJCNLP 2017 Task 1: Character Checking System for Chinese Grammatical Error Diagnosis Task
... this task, Run2 plays better than Run1, and it has relatively better perfor- mance on Accuracy, Precision and F1-score ...level task, Run1 achieves the highest precision rate com- paring with other teams, ... See full document
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Impact of marketing mix on customer satisfactions – a case study on officina (bd) ltd
... total customer experience will build lasting customer ...total customer experience they will automatically generate high and lasting customer loyalty” Mascarenhas et ...total customer ... See full document
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YNUDLG at IJCNLP 2017 Task 5: A CNN LSTM Model with Attention for Multi choice Question Answering in Examinations
... “Multi-choice Question Answering in Exams” is a typical question answering task, which aims to test how accurately the participants could answer the questions in exams. Most of the existing QA systems typically ... See full document
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