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Emotion classification of chinese microblog text via fusion of BoW and eVector feature representations 3

Emotion classification of chinese microblog text via fusion ...

引用

3rd ccf conference on natural language processing and chinese computing, nlpcc 2014

作者： Li, Chengxin Wu, Huimin Jin, Qin School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)9783662459232

Sentiment Analysis has been a hot research topic in recent years. Emotion classification is more detailed sentiment analysis which cares about more than the polarity of sentiment. In this paper, we present our system of emotion analysis for the Sina Weibo texts on both the document and sentence level, which detects whether a text is sentimental and further decides which emotion classes it conveys. The emotions of focus are seven basic emotion classes: anger, disgust, fear, happiness, like, sadness and surprise. Our baseline system uses supervised machine learning classifier (support vector machine, SVM) based on bag-of-words (BoW) features. In a contrast system, we propose a novel approach to construct an emotion lexicon and to generate a new feature representation of text which is named emotion vector eVector. Our experimental results show that both systems can classify emotion significantly better than random guess. Fusion of both systems obtains additional gain which indicates that they capture certain complementary information. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Sentiment analysis

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A hybrid method for chinese entity relation extraction 3

A hybrid method for Chinese entity relation extraction

引用

3rd ccf conference on natural language processing and chinese computing, nlpcc 2014

作者： Wang, Hao Qi, Zhenyu Hao, Hongwei Xu, B. Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783662459232

Entity relation extraction is an important task for information extraction, which refers to extracting the relation between two entities from input text. Previous researches usually converted this problem to a sequence labeling problem and used statistical models such as conditional random field model to solve it. This kind of method needs a large, high-quality training dataset. So it has two main drawbacks: 1) for some target relations, it is not difficult to get training instances, but the quality is poor;2) for some other relations, it is hardly to get enough training data automatically. In this paper, we propose a hybrid method to overcome the shortcomings. To solve the first drawback, we design an improved candidate sentences selecting method which can find out high-quality training instances, and then use them to train our extracting model. To solve the second drawback, we produce heuristic rules to extract entity relations. In the experiment, the candidate sentences selecting method improves the average F1 value by 78.53% and some detailed suggestions are given. And we submitted 364944 triples with the precision rate of 46.3% for the competition of Sougou chinese entity relation extraction and rank the 4th place in the platform. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Knowledge based systems

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natural language processing and chinese computing 1

引用

丛书名： Communications in Computer and Information Science

1000年

作者： Chengqing Zong Jian-Yun Nie Dongyan Zhao Yansong Feng

ISBN: (数字)9783662459249

ISBN: (纸本)9783662459232

This book constitutes the refereed proceedings of the Third ccf conference, nlpcc 2014, held in Shenzhen, China, in December 2014. The 35 revised full papers presented together with 8 short papers were carefully reviewed and selected from 110 English submissions. The papers are organized in topical sections on fundamentals on language computing; applications on language computing; machine translation and multi-lingual information access; machine learning for NLP; NLP for social media; NLP for search technology and ads; question answering and user interaction; web mining and information extraction.

关键词： natural language processing (NLP) User Interfaces and Human Computer Interaction Multimedia Information Systems natural language processing (NLP) Computer Applications

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A Simple, Fast Strategy for Weighted Alignment Hypergraph

A Simple, Fast Strategy for Weighted Alignment Hypergraph

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2nd Annual conference of China-Computer-Federation Technical-Committee-of-chinese-Information on natural language processing and chinese computing (ccf TCCI nlpcc)

作者： Tu, Zhaopeng Xie, Jun Lv, Yajuan Liu, Qun Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Dublin City Univ Ctr Next Generat Locolisat Dublin Ireland

ISBN: (纸本)9783642416439;9783642416446

Weighted alignment hypergraph [4] is potentially useful for statistical machine translation, because it is the first study to simultaneously exploit the compact representation and fertility model of word alignment. Since estimating the probabilities of rules extracted from hypergraphs is an NP-complete problem, they propose a divide-and-conquer strategy by decomposing a hypergraph into a set of independent subhypergraphs. However, they employ a Bull's algorithm to enumerate all consistent alignments for each rule in each subhypergraph, which is very time-consuming especially for the rules that contain non-terminals. This limits the applicability of this method to the syntax translation models, the rules of which contain many non-terminals (e. g. SCFG rules). In response to this problem, we propose an inside-outside algorithm to efficiently enumerate the consistent alignments. Experimental results show that our method is twice as fast as the Bull's algorithm. In addition, the efficient dynamic programming algorithm makes our approach applicable to syntax-based translation models.

关键词： statistical machine translation weighted alignment hypergraph optimization

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chinese Weibo Sentiment Analysis Based on Character Embedding with Dual-Channel Convolutional Neural Network 3

Chinese Weibo Sentiment Analysis Based on Character Embeddin...

引用

3rd IEEE International conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Chen, Sisi Ding, Youdong Xie, Zhifeng Liu, Shenkai Ding, Huiming Shanghai Univ Shanghai Film Acad Shanghai Peoples R China Shanghai Univ Shanghai Engn Res Ctr Mot Picture Special Effects Shanghai Peoples R China

ISBN: (纸本)9781538643013

As one of the most compelling NLP(natural language processing) tasks, sentiment analysis becomes more and more popular. In this paper, we have proposed a new method of sentiment analysis by using pre-trained character embedding with a dual-channel convolutional neural network (char-DCCNN) to comprehend the sentiment of Sina Weibo's chinese short comments. First of all, we divide chinese corpus into single chinese characters which are then trained as character vectors. Characters that appear less frequently are randomly initialized. Then, the vector matrix representing the text is input into a two-channel convolutional neural network. The vector of one channel remains static (as a kind of global feature) and another is fine-tuned (as a kind of local feature) according to the input data. Finally we record the train performance, validation performance and the final average validation performance respectively to reflect the sentiment classification results. The dataset used in this paper is nlpcc2012 micro-blog sentiment analysis datasets and the reviews of sports, film, social and other fields crawled from Sina Weibo. The 10-fold cross-validation will be employed. Three experiments are done to show the good performance of our method through three aspects-embedding, activation function, channel. In all, the char-DCCNN that we have put forward improves the sentiment classification results of Weibo chinese short comments and possesses practical significance.

关键词： sentiment analysis weibo chinese character embedding CNN dual-channel

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Will Repeated Reading Benefit natural language Understanding? 6th

Will Repeated Reading Benefit Natural Language Understanding...

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6th ccf International conference on natural language processing and chinese computing (nlpcc)

作者： Sha, Lei Qian, Feng Sui, Zhifang Peking Univ Sch Elect Engn & Comp Sci Key Lab Computat Linguist Minist Educ Beijing Peoples R China Peking Univ Inst Network Comp & Informat Syst Beijing Peoples R China

ISBN: (纸本)9783319736181;9783319736174

Repeated Reading (re-read), which means to read a sentence twice to get a better understanding, has been applied to machine reading tasks. But there have not been rigorous evaluations showing its exact contribution to natural language processing. In this paper, we design four tasks, each representing a different class of NLP tasks: (1) part-of-speech tagging, (2) sentiment analysis, (3) semantic relation classification, (4) event extraction. We take a bidirectional LSTM-RNN architecture as standard model for these tasks. Based on the standard model, we add repeated reading mechanism to make the model better "understand" the current sentence by reading itself twice. We compare three different repeated reading architectures: (1) Multi-level attention (2) Deep BiLSTM (3) Multi-pass BiLSTM, enforcing apples-to-apples comparison as much as possible. Our goal is to understand better in what situation repeated reading mechanism can help NLP task, and which of the three repeated reading architectures is more appropriate to repeated reading. We find that repeated reading mechanism do improve performance on some tasks (sentiment analysis, semantic relation classification, event extraction) but not on others (POS tagging). We discuss how these differences may be caused in each of the tasks. Then we give some suggestions for researchers to follow when choosing whether to use repeated model and which repeated model to use when faced with a new task. Our results thus shed light on the usage of repeated reading in NLP tasks.

关键词： Sentiment analysis

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Personal Attributes Extraction in chinese Text Bakeoff in CLP 2014: Overview 3

Personal Attributes Extraction in Chinese Text Bakeoff in CL...

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3rd CIPS-SIGHAN Joint conference on chinese language processing, CLP 2014

作者： Xu, Ruifeng Wang, Shuai Shi, Feng Xu, Jian Key Laboratory of Network Oriented Intelligent Computation Harbin Institute of Technology Shenzhen Graduate School China Department of Computing Hong Kong Polytechnic University Hong Kong

This paper presents the overview of Personal Attributes Extraction in chinese Text Bakeoff in CLP 2014. Personal attribute extraction plays an important role in information extraction, event tracking, entity disambiguation and other related research areas. This task is designed to evaluate the techniques for extracting person specific attributes from unstructured chinese texts, which is similar to slot filling, but focuses on person attributes. This task brings some challenges issues because chinese language contains some common words and lacks of capital clues as in English. The task organizer manually constructs the query names and corresponding documents. The value/presence of the texts corresponding 25 pre-defined attributes are annotated to construct the training and testing dataset. The bakeoff results achieved by the participators show the good progress in this field. © 2014 CLP 2014 - 3rd CIPS-SIGHAN Joint conference on chinese language processing. All rights reserved.

关键词： Statistical tests

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Question Answering for Technical Customer Support 7th

Question Answering for Technical Customer Support

引用

7th ccf International conference on natural language processing and chinese computing, nlpcc

作者： Li, Yang Miao, Qingliang Geng, Ji Alt, Christoph Schwarzenberg, Robert Hennig, Leonhard Hu, Changjian Xu, Feiyu Lenovo Bldg H6 West Shangdi Rd Beijing Peoples R China DFKI Alt Moabit 91c D-10559 Berlin Germany

ISBN: (纸本)9783319994956;9783319994949

Human agents in technical customer support provide users with instructional answers to solve a task. Developing a technical support question answering (QA) system is challenging due to the broad variety of user intents. Moreover, user questions are noisy (for example, spelling mistakes), redundant and have various natural language expresses, which are challenges for QA system to match user queries to corresponding standard QA pair. In this work, we combine question intent categories classification and semantic matching model to filter and select correct answers from a back-end knowledge base. Using a real world user chat-log dataset with 60 intent categories, we observe that while supervised models, perform well on the individual classification tasks. For semantic matching, we add muti-info (answer and product information) into standard question and emphasize context information of user query (captured by GRU) into our model. Experiment results indicate that neural multi-perspective sentence similarity networks outperform baseline models. The precision of semantic matching model is 85%.

关键词： Question and Answer Answer selection Semantic matching

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chinese text sentiment analysis based on transformer model 3

Chinese text sentiment analysis based on transformer model

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3rd International conference on Electronic Communication and Artificial Intelligence, IWECAI 2022

作者： Jing, Huang Yang, Cai School of Information Science And Technology Zhejiang Sci-Tech University Hangzhou China

ISBN: (纸本)9781665479974

As an important research area of natural language processing, text sentiment analysis has been widely used in opinion processing and product review analysis. Recurrent neural networks have the characteristics of temporal order, cannot directly extract the contextual semantic features of sentences, and cannot be computed in parallel;convolutional neural networks can be computed in parallel, but have the disadvantages of huge computation and long training time. To address the above problems, this paper proposes a Light-Transformer model based on word vector representation and positional embedding, and modifies the structure of the Transformer to keep only the Encoder module, and finally the extracted sentence features are input to two fully connected layers for classification. Experimental results on the nlpcc2014 Task2 dataset show that the method improves the classification accuracy by 0.3%-1.0% compared to traditional methods such as LSTM, CNN, etc., and the number of model parameters is greatly reduced compared to other Transformer-based models with close accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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Learning Word Sense Embeddings from Word Sense Definitions

Learning Word Sense Embeddings from Word Sense Definitions

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5th International conference on natural language processing and chinese computing (nlpcc) / 24th International conference on Computer processing of Oriental languages (ICCPOL)

作者： Li, Qi Li, Tianshi Chang, Baobao Peking Univ Minist Educ Sch Elect Engn & Comp Sci Key Lab Computat Linguist 5 Yiheyuan Rd Beijing 100871 Peoples R China Collaborat Innovat Ctr Language Abil Xuzhou 221009 Peoples R China

ISBN: (纸本)9783319504964;9783319504957

Word embeddings play a significant role in many modern NLP systems. Since learning one representation per word is problematic for polysemous words and homonymous words, researchers propose to use one embedding per word sense. Their approaches mainly train word sense embeddings on a corpus. In this paper, we propose to use word sense definitions to learn one embedding per word sense. Experimental results on word similarity tasks and a word sense disambiguation task show that word sense embeddings produced by our approach are of high quality.

关键词： Word sense embedding RNN WordNet

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