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作者：鲁霞浙江大学

学位级别：硕士

The purpose of this thesis is to explore the relevant issues in automatic dialogue summarization (ADS) within the context of the speech summarization project While the majority of techniques in computational linguistics for summarization are "computationally" oriented, our approach to the task is largely "linguistically" *** start with, we build a corpus of dialogues from the business domain and classify them into 16 sub-domains, each of which is concerned with a specific activity in business transaction, such as inquiring, offering, price negotiation, etc. The dialogues are then segmented and tagged with part-of-speech *** summarization as sentence extraction we try to identify the distinguished linguistic features of dialogues. Through the linguistic analysis of dialogues we define two information structures: the information inquiry dialogue with the parallel information allocation and the negotiation dialogue with the sequential information allocation. Based on this observation we developed different summarization methods for the two types of dialogues in our corpus. We use the Edmundsonian paradigm to assign a score for each utterance in the dialogue, rank the utterances and select the top n (adjustable) utterances as the summary. We also propose a new evaluation method which evaluates both the informativeness and redundancy of the summary. The evaluation results indicate the better performance of our methods compared with the MMR *** summarization as language understanding, we adopt the concept of speech act to determine the function of an utterance. Summarization using this method will take three stages: 1) tagging each utterance with proper dialogue act;2) dialogue act recognition (DAR);3) information identification using natural language processing techniques. Our work in this thesis is in the first stage, the design of a dialogue act tag-set. We define the tags on the three dimensions: dialogue, task and emotion. We then annota

关键词： summarization automatic dialogue summarization dialogue analysis information structure dialogue act sentence extraction

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Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

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Computers, Materials & Continua 2022年第8期72卷 2323-2339页

作者： Sami Ullah Muhammad Ramzan Talib Toqir A.Rana Muhammad Kashif Hanif Muhammad Awais Department of Computer Science Government College University Faisalabad38000Pakistan Department of Computer Science and IT The University of Lahore54590Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)11800PenangMalaysia Department of Software Engineering Government College University Faisalabad38000Pakistan

In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar *** of such material where more than one person is involved has a spate challenge as compared to other text analysis *** are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been *** Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu ***,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the *** accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue *** that,we have preprocessed the data and selected dialogues with common *** the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of *** have tuned the algorithms according to the Urdu language *** experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and *** believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

关键词： dialogue analysis conversational opinion mining sentiment analysis sentiment analysis in Urdu language deep learning machine learning

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