pwc-archive/papers-with-abstracts huggingface dataset:
IJCNLP-2017 Task 4: Customer Feedback Analysis
Authors: Chao-Hong Liu, Yasufumi Moriya, Alberto Poncelas, Declan Groves
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Machine Translation, Prediction

This document introduces the IJCNLP 2017 Shared Task on Customer Feedback Analysis. In this shared task we have prepared corpora of customer feedback in four languages, i.e. English, French, Spanish and Japanese. They were annotated in a common meanings categorization, which was improved from an ADAPT-Microsoft pivot study on customer feedback. Twenty teams participated in the shared task and twelve of them have submitted prediction results. The results show that performance of prediction meanings of customer feedback is reasonable well in four languages. Nine system description papers are archived in the shared tasks proceeding.
IJCNLP-2017 Task 5: Multi-choice Question Answering in Examinations
Authors: Shangmin Guo, Kang Liu, Shizhu He, Cao Liu, Jun Zhao, Zhuoyu Wei
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Question Answering

The IJCNLP-2017 Multi-choice Question Answering(MCQA) task aims at exploring the performance of current Question Answering(QA) techniques via the realworld complex questions collected from Chinese Senior High School Entrance Examination papers and CK12 website1. The questions are all 4-way multi-choice questions writing in Chinese and English respectively that cover a wide range of subjects, e.g. Biology, History, Life Science and etc. And, all questions are restrained within the elementary and middle school level. During the whole procedure of this task, 7 teams submitted 323 runs in total. This paper describes the collected data, the format and size of these questions, formal run statistics and results, overview and performance statistics of different methods
Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task
Authors: Yi Yang, Pengjun Xie, Jun Tao, Guangwei Xu, Linlin Li, Luo Si
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: 2D Human Pose Estimation, Position

This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging problem and design some handcraft features to solve it. Our system is mainly based on the LSTM-CRF model and 3 ensemble strategies are applied to improve the performance. At the identification level and the position level our system gets the highest F1 scores. At the position level, which is the most difficult level, we perform best on all metrics.
THU\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM
Authors: Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Sixing Wu, Zhigang Yuan
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Opinion Mining, POS, Sentiment Analysis, Task 2

Predicting valence-arousal ratings for words and phrases is very useful for constructing affective resources for dimensional sentiment analysis. Since the existing valence-arousal resources of Chinese are mainly in word-level and there is a lack of phrase-level ones, the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) task aims to predict the valence-arousal ratings for Chinese affective words and phrases automatically. In this task, we propose an approach using a densely connected LSTM network and word features to identify dimensional sentiment on valence and arousal for words and phrases jointly. We use word embedding as major feature and choose part of speech (POS) and word clusters as additional features to train the dense LSTM network. The evaluation results of our submissions (1st and 2nd in average performance) validate the effectiveness of our system to predict valence and arousal dimensions for Chinese words and phrases.
IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification
Authors: Pruthwik Mishra, D, Prathyusha a, Silpa Kanneganti, Soujanya Lanka
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Decision Making, Diversity

The Review Opinion Diversification (Revopid-2017) shared task focuses on selecting top-k reviews from a set of reviews for a particular product based on a specific criteria. In this paper, we describe our approaches and results for modeling the ranking of reviews based on their usefulness score, this being the first of the three subtasks under this shared task. Instead of posing this as a regression problem, we modeled this as a classification task where we want to identify whether a review is useful or not. We employed a bi-directional LSTM to represent each review and is used with a softmax layer to predict the usefulness score. We chose the review with highest usefulness score, then find its cosine similarity score with rest of the reviews. This is done in order to ensure diversity in the selection of top-k reviews. On the top-5 list prediction, we finished 3rd while in top-10 list one, we are placed 2nd in the shared task. We have discussed the model and the results in detail in the paper.
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification
Authors: Heba Elfardy, Manisha Srivastava, Wei Xiao, Jared Kramer, Tarun Agarwal
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Feature Engineering, General Classification, Machine Translation, Sentiment Analysis, Text Classification, Translation, Word Embeddings

The ability to automatically and accurately process customer feedback is a necessity in the private sector. Unfortunately, customer feedback can be one of the most difficult types of data to work with due to the sheer volume and variety of services, products, languages, and cultures that comprise the customer experience. In order to address this issue, our team built a suite of classifiers trained on a four-language, multi-label corpus released as part of the shared task on {``}Customer Feedback Analysis{''} at IJCNLP 2017. In addition to standard text preprocessing, we translated each dataset into each other language to increase the size of the training datasets. Additionally, we also used word embeddings in our feature engineering step. Ultimately, we trained classifiers using Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) Recurrent Neural Networks. Overall, we achieved a Macro-Average F-score between 48.7{\%} and 56.0{\%} for the four languages and ranked 3/12 for English, 3/7 for Spanish, 1/8 for French, and 2/7 for Japanese.
ADAPT Centre Cone Team at IJCNLP-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task
Authors: Daria Dzendzik, Alberto Poncelas, Carl Vogel, Qun Liu
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Answer Selection, Question Answering, regression

We describe the work of a team from the ADAPT Centre in Ireland in addressing automatic answer selection for the Multi-choice Question Answering in Examinations shared task. The system is based on a logistic regression over the string similarities between question, answer, and additional text. We obtain the highest grade out of six systems: 48.7{\%} accuracy on a validation set (vs. a baseline of 29.45{\%}) and 45.6{\%} on a test set.
YNU-HPCC at IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis Using a Bi-directional LSTM-CRF Model
Authors: Quanlei Liao, Jin Wang, Jinnan Yang, Xue-jie Zhang
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Position, Sentence

Building a system to detect Chinese grammatical errors is a challenge for natural-language processing researchers. As Chinese learners are increasing, developing such a system can help them study Chinese more easily. This paper introduces a bi-directional long short-term memory (BiLSTM) - conditional random field (CRF) model to produce the sequences that indicate an error type for every position of a sentence, since we regard Chinese grammatical error diagnosis (CGED) as a sequence-labeling problem.
CVTE at IJCNLP-2017 Task 1: Character Checking System for Chinese Grammatical Error Diagnosis Task
Authors: Xi-An Li, Peng Wang, Suixue Wang, Guanyu Jiang, Tianyuan You
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Position

Grammatical error diagnosis is an important task in natural language processing. This paper introduces CVTE Character Checking System in the NLP-TEA-4 shared task for CGED 2017, we use Bi-LSTM to generate the probability of every character, then take two kinds of strategies to decide whether a character is correct or not. This system is probably more suitable to deal with the error type of bad word selection, which is one of four types of errors, and the rest are words re-dundancy, words missing and words disorder. Finally the second strategy achieves better F1 score than the first one at all of detection level, identification level, position level.
LDCCNLP at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning
Authors: Peng Zhong, Jingbin Wang
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: BIG-bench Machine Learning, Sentiment Analysis, Task 2, Word Embeddings

Sentiment analysis on Chinese text has intensively studied. The basic task for related research is to construct an affective lexicon and thereby predict emotional scores of different levels. However, finite lexicon resources make it difficult to effectively and automatically distinguish between various types of sentiment information in Chinese texts. This IJCNLP2017-Task2 competition seeks to automatically calculate Valence and Arousal ratings within the hierarchies of vocabulary and phrases in Chinese. We introduce a regression methodology to automatically recognize continuous emotional values, and incorporate a word embedding technique. In our system, the MAE predictive values of Valence and Arousal were 0.811 and 0.996, respectively, for the sentiment dimension prediction of words in Chinese. In phrase prediction, the corresponding results were 0.822 and 0.489, ranking sixth among all teams.
CKIP at IJCNLP-2017 Task 2: Neural Valence-Arousal Prediction for Phrases
Authors: Peng-Hsuan Li, Wei-Yun Ma, Hsin-Yang Wang
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Sentiment Analysis, Task 2

CKIP takes part in solving the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) share task of IJCNLP 2017. This task calls for systems that can predict the valence and the arousal of Chinese phrases, which are real values between 1 and 9. To achieve this, functions mapping Chinese character sequences to real numbers are built by regression techniques. In addition, the CKIP phrase Valence-Arousal (VA) predictor depends on knowledge of modifier words and head words. This includes the types of known modifier words, VA of head words, and distributional semantics of both these words. The predictor took the second place out of 13 teams on phrase VA prediction, with 0.444 MAE and 0.935 PCC on valence, and 0.395 MAE and 0.904 PCC on arousal.
CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases
Authors: Zheng-Wen Lin, Yung-Chun Chang, Chen-Ann Wang, Yu-Lun Hsieh, Wen-Lian Hsu
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Opinion Mining, Sentiment Analysis, Task 2

Sentiment lexicon is very helpful in dimensional sentiment applications. Because of countless Chinese words, developing a method to predict unseen Chinese words is required. The proposed method can handle both words and phrases by using an ADVWeight List for word prediction, which in turn improves our performance at phrase level. The evaluation results demonstrate that our system is effective in dimensional sentiment analysis for Chinese phrases. The Mean Absolute Error (MAE) and Pearson{'}s Correlation Coefficient (PCC) for Valence are 0.723 and 0.835, respectively, and those for Arousal are 0.914 and 0.756, respectively.
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
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Clustering, Feature Engineering, Part-Of-Speech Tagging, Prediction, Sentiment Analysis, Task 2

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.
NLPSA at IJCNLP-2017 Task 2: Imagine Scenario: Leveraging Supportive Images for Dimensional Sentiment Analysis
Authors: Szu-Min Chen, Zi-Yuan Chen, Lun-Wei Ku
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: General Classification, Sentiment Analysis, Sentiment Classification, Task 2

Categorical sentiment classification has drawn much attention in the field of NLP, while less work has been conducted for dimensional sentiment analysis (DSA). Recent works for DSA utilize either word embedding, knowledge base features, or bilingual language resources. In this paper, we propose our model for IJCNLP 2017 Dimensional Sentiment Analysis for Chinese Phrases shared task. Our model incorporates word embedding as well as image features, attempting to simulate human{'}s imaging behavior toward sentiment analysis. Though the performance is not comparable to others in the end, we conduct several experiments with possible reasons discussed, and analyze the drawbacks of our model.
NCYU at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases using Vector Representations
Authors: Jui-Feng Yeh, Jian-Cheng Tsai, Bo-Wei Wu, Tai-You Kuang
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Clustering, Sentiment Analysis, Task 2

This paper presents two vector representations proposed by National Chiayi University (NCYU) about phrased-based sentiment detection which was used to compete in dimensional sentiment analysis for Chinese phrases (DSACP) at IJCNLP 2017. The vector-based sentiment phraselike unit analysis models are proposed in this article. E-HowNet-based clustering is used to obtain the values of valence and arousal for sentiment words first. An out-of-vocabulary function is also defined in this article to measure the dimensional emotion values for unknown words. For predicting the corresponding values of sentiment phrase-like unit, a vectorbased approach is proposed here. According to the experimental results, we can find the proposed approach is efficacious.
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction
Authors: Yassine Benajiba, Jin Sun, Yong Zhang, Zhiliang Weng, Or Biran
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Prediction, Sentiment Analysis, Task 2, Word Embeddings

This paper introduces Mainiway AI Labs submitted system for the IJCNLP 2017 shared task on Dimensional Sentiment Analysis of Chinese Phrases (DSAP), and related experiments. Our approach consists of deep neural networks with various architectures, and our best system is a voted ensemble of networks. We achieve a Mean Absolute Error of 0.64 in valence prediction and 0.68 in arousal prediction on the test set, both placing us as the 5th ranked team in the competition.
NCTU-NTUT at IJCNLP-2017 Task 2: Deep Phrase Embedding using bi-LSTMs for Valence-Arousal Ratings Prediction of Chinese Phrases
Authors: Yen-Hsuan Lee, Han-Yun Yeh, Yih-Ru Wang, Yuan-Fu Liao
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Chinese Word Segmentation, regression, Sentiment Analysis, Task 2

In this paper, a deep phrase embedding approach using bi-directional long short-term memory (Bi-LSTM) is proposed to predict the valence-arousal ratings of Chinese words and phrases. It adopts a Chinese word segmentation frontend, a local order-aware word, a global phrase embedding representations and a deep regression neural network (DRNN) model. The performance of the proposed method was benchmarked by the IJCNLP 2017 shared task 2. According the official evaluation results, our best system achieved mean rank 6.5 among all 24 submissions.
NTOUA at IJCNLP-2017 Task 2: Predicting Sentiment Scores of Chinese Words and Phrases
Authors: Chuan-Jie Lin, Hao-Tsung Chang
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: BIG-bench Machine Learning, Sentiment Analysis, Task 2

This paper describes the approaches of sentimental score prediction in the NTOU DSA system participating in DSAP this year. The modules to predict scores for words are adapted from our system last year. The approach to predict scores for phrases is keyword-based machine learning method. The performance of our system is good in predicting scores of phrases.
CYUT at IJCNLP-2017 Task 3: System Report for Review Opinion Diversification
Authors: Shih-Hung Wu, Su-Yu Chang, Liang-Pu Chen
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: regression

Review Opinion Diversification (RevOpiD) 2017 is a shared task which is held in International Joint Conference on Natural Language Processing (IJCNLP). The shared task aims at selecting top-k reviews, as a summary, from a set of re-views. There are three subtasks in RevOpiD: helpfulness ranking, rep-resentativeness ranking, and ex-haustive coverage ranking. This year, our team submitted runs by three models. We focus on ranking reviews based on the helpfulness of the reviews. In the first two models, we use linear regression with two different loss functions. First one is least squares, and second one is cross entropy. The third run is a random baseline. For both k=5 and k=10, our second model gets the best scores in the official evaluation metrics.
JUNLP at IJCNLP-2017 Task 3: A Rank Prediction Model for Review Opinion Diversification
Authors: Monalisa Dey, Anupam Mondal, Dipankar Das
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12

IJCNLP-17 Review Opinion Diversification (RevOpiD-2017) task has been designed for ranking the top-k reviews of a product from a set of reviews, which assists in identifying a summarized output to express the opinion of the entire review set. The task is divided into three independent subtasks as subtask-A,subtask-B, and subtask-C. Each of these three subtasks selects the top-k reviews based on helpfulness, representativeness, and exhaustiveness of the opinions expressed in the review set individually. In order to develop the modules and predict the rank of reviews for all three subtasks, we have employed two well-known supervised classifiers namely, Na{\"\i}ve Bayes and Logistic Regression on the top of several extracted features such as the number of nouns, number of verbs, and number of sentiment words etc from the provided datasets. Finally, the organizers have helped to validate the predicted outputs for all three subtasks by using their evaluation metrics. The metrics provide the scores of list size 5 as (0.80 (mth)) for subtask-A, (0.86 (cos), 0.87 (cos d), 0.71 (cpr), 4.98 (a-dcg), and 556.94 (wt)) for subtask B, and (10.94 (unwt) and 0.67 (recall)) for subtask C individually.
All-In-1 at IJCNLP-2017 Task 4: Short Text Classification with One Model for All Languages
Authors: Barbara Plank
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: All, General Classification, Multilingual text classification, Multilingual Word Embeddings, text-classification, Text Classification, Word Embeddings

We present All-In-1, a simple model for multilingual text classification that does not require any parallel data. It is based on a traditional Support Vector Machine classifier exploiting multilingual word embeddings and character n-grams. Our model is simple, easily extendable yet very effective, overall ranking 1st (out of 12 teams) in the IJCNLP 2017 shared task on customer feedback analysis in four languages: English, French, Japanese and Spanish.
SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model
Authors: Shuying Lin, Huosheng Xie, Liang-Chih Yu, K. Robert Lai
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: General Classification, Multi-Label Classification, Sarcasm Detection, Sentence, Sentence Classification, Sentiment Analysis, Word Embeddings

The analysis of customer feedback is useful to provide good customer service. There are a lot of online customer feedback are produced. Manual classification is impractical because the high volume of data. Therefore, the automatic classification of the customer feedback is of importance for the analysis system to identify meanings or intentions that the customer express. The aim of shared Task 4 of IJCNLP 2017 is to classify the customer feedback into six tags categorization. In this paper, we present a system that uses word embeddings to express the feature of the sentence in the corpus and the neural network as the classifier to complete the shared task. And then the ensemble method is used to get final predictive result. The proposed method get ranked first among twelve teams in terms of micro-averaged F1 and second for accura-cy metric.
IIIT-H at IJCNLP-2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches
Authors: D, Prathyusha a, Pruthwik Mishra, Silpa Kanneganti, Soujanya Lanka
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12

The IJCNLP 2017 shared task on Customer Feedback Analysis focuses on classifying customer feedback into one of a predefined set of categories or classes. In this paper, we describe our approach to this problem and the results on four languages, i.e. English, French, Japanese and Spanish. Our system implemented a bidirectional LSTM (Graves and Schmidhuber, 2005) using pre-trained glove (Pennington et al., 2014) and fastText (Joulin et al., 2016) embeddings, and SVM (Cortes and Vapnik, 1995) with TF-IDF vectors for classifying the feedback data which is described in the later sections. We also tried different machine learning techniques and compared the results in this paper. Out of the 12 participating teams, our systems obtained 0.65, 0.86, 0.70 and 0.56 exact accuracy score in English, Spanish, French and Japanese respectively. We observed that our systems perform better than the baseline systems in three languages while we match the baseline accuracy for Japanese on our submitted systems. We noticed significant improvements in Japanese in later experiments, matching the highest performing system that was submitted in the shared task, which we will discuss in this paper.