Luận văn thạc sĩ VNU UET a vietnamese text based conversational agent

A Vietnamese Text-based Conversational Agent Nguyen Quoc Dai Faculty of Information Technology University of Engineering and Technology Vietnam National University, Hanoi Supervised by Dr Pham Bao Son A thesis submitted in fulfillment of the requirements for the degree of Master of Science in Computer Science November 2011 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ORIGINALITY STATEMENT ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at University of Engineering and Technology (UET/Coltech) or any other educational institution, except where due acknowledgement is made in the thesis Any contribution made to the research by others, with whom I have worked at UET/Coltech or elsewhere, is explicitly acknowledged in the thesis I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’ Hanoi, November 23rd , 2011 Signed i LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ABSTRACT The first step that a question answering system must perform is to transform an input question into an intermediate representation Most published works so far use rule-based approaches to realize this transformation in question answering systems Nevertheless, in existing rule-based approaches, manually creating the rules is error-prone and expensive in time and effort In this thesis, we focus on introducing a rule-based approach that offers an intuitive way to create compact rules for extracting intermediate representation of input questions Experimental results are promising where our system achieves reasonable performance and demonstrate that it is straightforward to adapt to new domains and languages More importantly, this thesis introduces a Vietnamese text-based conversational agent architecture on specific knowledge domain which is integrated in a question answering system When the question answering system fails to provide answers to user input, our conversational agent can step in to interact with users to provide answers to users Experimental results are promising where our Vietnamese text-based conversational agent achieves positive feedback in a study conducted in the university academic regulation domain Publications: Dai Quoc Nguyen, Dat Quoc Nguyen and Son Bao Pham A Vietnamese Text-based Conversational Agent In Proc of The 25th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2012), Springer-Verlag LNAI, pp 699-708 Dai Quoc Nguyen, Dat Quoc Nguyen and Son Bao Pham A Semantic Approach for Question Analysis In Proc of The 25th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2012), Springer-Verlag LNAI, pp 156-165 Dat Quoc Nguyen, Dai Quoc Nguyen and Son Bao Pham Systematic Knowledge Acquisition for Question Analysis In Proc of the 8th International Conference on Recent Advances in Natural Language Processing (RANLP 2011), ACL Anthology, pp 406-412 ii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com iii Dai Quoc Nguyen, Dat Quoc Nguyen, Khoi Trong Ma and Son Bao Pham Automatic Ontology Construction from Vietnamese text In Proceedings of the 7th International Conference on Natural Language Processing and Knowledge Engineering (NLPKE’11), IEEE, pp 485-488 Dat Quoc Nguyen, Dai Quoc Nguyen, Son Bao Pham and Dang Duc Pham Ripple Down Rules for Part-Of-Speech Tagging In Proc of 12th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2011), Springer-Verlag LNCS, part I, pp 190-201 Dai Quoc Nguyen, Dat Quoc Nguyen and Son Bao Pham A Vietnamese question answering system In Proceedings of the 2009 International Conference on Knowledge and Systems Engineering (KSE 2009) , IEEE CS, pp 26–32 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com ACKNOWLEDGEMENTS First and foremost, I would like to express my deepest gratitude to my supervisor, Dr Pham Bao Son, for his patient guidance and continuous support throughout the years He always appears when I need help, and responds to queries so helpfully and promptly I would like to give my honest appreciation to my younger brother, Nguyen Quoc Dat, for his great support I would like to specially thank Prof Bui The Duy and my colleagues for their help through my time at Human Machine Interaction Laboratory, UET/Coltech I sincerely acknowledge the Vietnam National University, Hanoi, Toshiba Foundation Scholarship, and especially Dr Pham Bao Son for supporting finance to my master study Finally, this thesis would not have been possible without the support and love of my mother and my father Thank you! iv LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com To my family ♥ v LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Table of Contents Introduction 1.1 A Semantic Approach for Question Analysis 1.2 A Vietnamese Text-based Conversational Agent 1.3 Thesis Organisation 1 Literature review 2.1 Text-based conversational agents 2.1.1 Using keywords for pattern matching 2.1.2 Using the sentence similarity measure for pattern matching 2.2 FrameScript Scripting Language 2.3 Question answering systems 4 12 Our Question Answering System Architecture 3.1 Vietnamese Question Answering System 3.1.1 Natural language question analysis component 3.1.1.1 Intermediate representation of an input question 3.1.1.2 Question analysis 3.1.2 Answer retrieval component 3.2 Using FrameScript for question analysis 3.2.1 Preprocessing module 3.2.2 Syntactic analysis module 3.2.3 Semantic analysis module 15 15 16 16 17 18 19 19 20 22 Text-based Conversational Agent for Vietnamese 24 4.1 Overview of architecture 24 4.2 Determining separate contexts 25 4.3 Identifying hierarchical contexts 27 vi LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com TABLE OF CONTENTS vii Evaluation and Discussion 5.1 Experimental results for Vietnamese text-based conversational agent 5.2 Question Analysis for English 5.3 Discussion 29 Conclusion 34 A Scripting patterns for English question analysis 36 B Definitions of question-class types 38 C Definitions of question-structures 40 29 31 33 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com List of Figures 2.1 2.2 O’Shea et al.’s conversational agent framework Aqualog’s architecture 14 3.1 3.2 Architecture of our question answering system 16 Architecture of the natural language question analysis component using FrameScript 19 4.1 Architecture of our Vietnamese text-based conversational agent 25 viii LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 32 Chapter Evaluation and Discussion Table 5.5: Number of rules with conditional Question-structure type Question-structure type UnknTerm Definition ThreeTerm Normal ThreeTerm UnknTerm Combine Normal responses Number of rules 1 As the intermediate representation of our system is different to AquaLog and there is no common test set available, it is difficult to directly compare our approach with Aqualog on the English domain To demonstrate that our approach could be applied to a new open domain, we use the above 52 rules to test the data of 500 questions2 from TREC 10 Table 5.6 shows the number of correctly analyzed questions corresponding with each questionstructure type Table 5.6: Number of questions corresponding with each question-structure type Question-structure type Number of questions Definition 130 UnknTerm 66 UnknRel Normal 20 ThreeTerm 15 And Table 5.7 gives the sources of errors for 259 incorrect cases It clearly shows that most errors come from unexpected structures This could be rectified by adding more rules, especially when we construct a larger variety of question structure types from a bigger training data such as 5500 questions (Li and Roth, 2002a) Table 5.7: Error results Reason Have special characters (such as / – “ ” ’s) and abbreviations Not have compatible patterns Semantic error in elements of the intermediate representation Number of questions 64 185 10 This experiment is indicative of the ability in using our system to quickly build rules for a new domain We believe that our approach could be applied to a new http://cogcomp.cs.illinois.edu/Data/QA/QC/TREC_10.label LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 5.3 Discussion 33 language because creating the rules manually for question analysis is a language independent process 5.3 Discussion Because constructing rules depends on the identification of super-contexts and their sub-contexts in Vietnamese, so it causes difficulties in designing the hierarchy of contexts Consequently, we want to simplify this designing phase according to the process of semantic knowledge acquisition We built additional scripts by using same process shown in section 3.2 to detect noun phrases, question phrases and relation phrases or semantic constraints between them for Vietnamese Using these scripts, we constructed pattern expressions and got the suitable phrases from response expressions These phrases actually are keywords which may be used as patterns of rules in the hierarchical contexts In addition, our Vietnamese text-based conversational agent is integrated with our ontology-based Vietnamese question answering system to form a general system Our goal is to retrieve the necessary information from user’s utterance to support our Vietnamese question answering system in providing answers to users We consider the process that the Answer retrieval component similarly measures between elements of the intermediate representation of user’s question and the ontology’s elements In case of ambiguity for the similarity among ontology’s elements is still present, the system will interacts with the users by presenting different options to get the correct ontology’s elements In this process, we can construct the supplemental scripts to solve ambiguities from ontology knowledge base Using these scripts, we can retrieve the suitable elements from ontology through the conversational contexts structured based on the given ontology LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Chapter Conclusion In this thesis, we firstly presented, in chapter 3, our first Ontology-based Vietnamese question answering system with two components The first component takes the user’s question and transforms it into an intermediate representation in a compact form The intermediate representation used in our system comprises of a questionstructure and one or more query-tuples in the format of (question-structure, questionclass, Term1 , Relation, Term2 , Term3 ), in which Term1 represents a concept (object class), Term2 , and Term3 , if exist, represent entities (objects), Relation (property) is a semantic constraint between terms in the question The second component maps terms and relations in the query-tuple to concepts, instances and relations in the ontology to generate a semantic answer Obviously, we spent a large amount of time for writing grammar rules to analyze input questions and did realize difficulties in controlling interactions between these rules Consequently, also in chapter 3, we introduced a rule-based approach for converting a natural language question into an intermediate representation in a question answering system Our system utilizes FrameScript to help users to create intuitive and compact rules for extracting elements of the intermediate representation We constructed rules including patterns and associated responses, in which pattern is used to match user’ questions and its corresponding response as output is sent to return the intermediate representation of question Experimental results of our system on a wide range of questions are promising with reasonable performance (in chapter 5) We believe our approach can be applied to question answering in open domain against text corpora that requires an analysis to turn an input question to an explicit representation of some sort Our 34 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 35 method could be combined nicely with the processing of annotating corpus and it is straightforward to apply for a new domain and a new language We will extend our system in the future to assist the rule creation process on a wide range of questions in open domain and to improve the generalization capability of existing rules Importantly, in this thesis, we proposed a Vietnamese text-based conversational agent architecture as backup component integrated with our Vietnamese question answering system to form a general system (in chapter 4) We focused on presenting an approach to construct the hierarchical contexts consisting of organized rules over a specific knowledge domain There are two steps to construct the conversational contexts: the first step to identify the transformations from a context to other contexts, and the second step to organize these contexts into a hierarchy to handle unexpected inputs Our contribution is to provide the suitable information related to users’ statements and to retrieve the necessary knowledge to support our question answering system in providing answers The experimental results (as described in chapter 5) are promising, with positive evaluation from users for our Vietnamese text-based conversational agent To the best of our knowledge, this is the first Vietnamese text-based conversational agent to enable users to interact with the system via a natural language interface In the future, our text-based conversational agent will be extended not only to communicate with users but also to get the necessary information related to ontology knowledge base from input utterances We will build scripts to resolve the ambiguity between elements of ontology such as the similarity of string names among classes or instances in the ontology The constructed scripts would be utilized to generate options in order to obtain terms from conversational interactions with users LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Appendix A Scripting patterns for English question analysis ==> [ ∧ (∧ == is or ∧ == are) –> Definition, (Definition, What, ?, ?, ∧ 3, ?) | UnknTerm, (UnknTerm, What, ?, ∧ 2, ∧ 3, ?) ] ==> [ ∧ (∧ == is or ∧ == are) –> Definition, (Definition, Who, ?, ?, ∧ 3, ?) | UnknTerm, (UnknTerm, Who, ?, ∧ 2, ∧ 3, ?) ] ==> [ UnknTerm, (UnknTerm, Which, ?, ∧ 2, ∧ 3, ?) ] ==> [ UnknRel, (UnknRel, Entity, ∧ 1, ?, ∧ 3, ?) ] ==> [ UnknRel, (UnknRel, List, ∧ 1, ?, ∧ 3, ?) ] ==> [ Normal, (Normal, Entity, ∧ 1, ∧ 2, ∧ 3, ?) ] ==> [ Normal, (Normal, List, ∧ 1, ∧ 2, ∧ 3, ?) ] ==> 36 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Appendix A 37 [ Affirm, (Affirm, YesNo, ∧ 2, ∧ 3, ∧ 4, ?) ] ==> [ Affirm, (Affirm, YesNo, ∧ 2, ∧ 1, ∧ 3, ?) ] { | } ==> [ ThreeTerm, (ThreeTerm, List, ∧ 1, ∧ 2, ∧ 3, ∧ 6) ] ==> [ ThreeTerm, (ThreeTerm, Entity, ∧ 1, ∧ 4, ∧ 3, ∧ 5) ] ==> [ And, (UnknTerm, Who, ?, ∧ 2, ∧ 3, ?), (UnknTerm, Who, ?, ∧ 5, ∧ 6, ?) ] ==> [ And, (Normal, Entity, ∧ 1, ∧ 2, ∧ 3, ?), (Normal, Entity, ∧ 1, ∧ 5, ∧ 6, ?) ] ==> [ Or, (UnknTerm, Who, ?, ∧ 2, ∧ 3, ?), (UnknTerm, Who, ?, ∧ 2, ∧ 6, ?) ] ==> [Clause, (UnknTerm, What, ?, ∧ 2, ?, ?), (Normal, Which, ∧ 3, ∧ 5, ∧ 6, ?) ] LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Appendix B Definitions of question-class types In our approaches, question is classified into one of the following classes of HowWhy, YesNo, What, When, Where, Who, Many, ManyClass, List, and Entity To identify question categories, we specify a number of JAPE grammars using NounPhrase annotations and the question-word information identified by the preprocessing module Obviously using this method in question-phrases detection phase will result in ambiguity when a question belongs to multiple categories We allow for this and resolve the ambiguity in the semantic analysis module • A question referring causes or methods by containing the word re-annotated by single TokenVn annotation such as “tại saowhy ”, or “vì saowhy ”, or “thế nàohow ”, or “là nàohow ”, , will be classified to HowWhy (in English, it appertains types of Why-questions and How is/are questions) • A question requiring true or false answer by holding the word re-covered by single TokenVn annotations such as “có làis that ”, or “có phải làis this ”, or “phải khôngare there ”, or “đúng khôngare those ”, , will be categorized to YesNo class • A question which refers to something in consisting of the word “cái gìwhat ”, or “là gìwhat ”, or “là gìwhat ”, , are classified to What class In English, this question type is What is/are-question-like • A question containing the word relabelled by single TokenVn annotation such as “khi nàowhen ”, or “vào thời gian nàowhich time ”, or “lúc nàowhen ”, or “ngày nàowhich date ”, , will belong to When class 38 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Appendix B 39 • A question which requires answers about location in consisting of word “ở nơi nàowhere ”, or “là nơi đâuwhere ”, or “ở chỗ nàowhere ”, , appertains to Where category (in English, this question type is kind of Where-questions) • A question will be categorized to Who class, if it contains the word indicating answer referring to a person such as “là aiwho ”, or “là người nàowho ”, or “những aiwho ”, • A question expecting the answer about number will belong to Many class (in English, these questions are How much/many is/are-questions) This question type holds the word like “bao nhiêuhow much|many ”, or “là bao nhiêuhow much|many ”, or “số lượnghow many ”, • A question will appear in ManyClass class, if it contains the word like “bao nhiêuHowmany ”, or “số lượngN umberof ” followed by a noun phrase (in English, this type is the same kind of How many NounPhrase-question) • A question will appertain in Entity category if it holds a noun phrase followed by the word “nàowhich ” or “gìwhat ” (in English, this kind of question belongs to set ofwhich/what Noun Phrase questions such as: which students, what class, ) • A question will categorized to List class if it contains the word referring commands such as: “cho biếtgive ”, “chỉ rashow ”, “kể ratell ”, “tìmf ind ”, “liệt kêlist ” followed by a noun phrase LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Appendix C Definitions of question-structures We define question structures: Normal, UnknTerm, UnknRel, Definition, Compare, ThreeTerm, Clause, Combine, And, Or, Affirm, Affirm_3Term, Affirm_MoreTuples as following: • A question will have question-structure of Normal if it has only one querytuple, and the query-tuple’s Term3 is missing • A question will have question-structure of UnknTerm if it has only one querytuple, and the query-tuple lacks Term1 and Term3 • A question will have UnknRel question-structure if it has only one query-tuple in the lack of Relation and Term3 • A question will have Definition question-structure if it has only one querytuple lacking all of Term1 , Relation and Term3 For example, the question “Nguyễn Quốc Đại ai?” (“who is Nguyen Quoc Dai?”) has query-tuple ( Definition , Who , ? , ? , Nguyễn Quốc ĐạiN guyenQuocDai , ? ) • If a question belongs to one of three question-structure types Normal, UnknRel and UnknTerm and appears in YesNo category, the question will have question-structure of Affirm For example, the question “Tồn sinh viên Trần Bình Giang phải khơng?” (“Is there a student who Tran Binh Giang is?”) has query-tuple ( Affirm , YesNo , sinh viênstudent , ? , Trần Bình GiangT ran Binh Giang , ? ) 40 LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Appendix C 41 • A question will have ThreeTerm question-structure if it has only one querytuple, and one or more of the query-tuple’s Term1 and Relation is missing For instance, the question “những giảng viên khoa công nghệ thông tin trường đại học Công Nghệ?” (“which lecturers are from faculty of Information Technology of College of Technology?”) has query-tuple ( ThreeTerm , Who , ? , giảng viênis lecturer , khoa công nghệ thông tinf aculty of Inf ormation T echnology , trường đại học Cơng NghệCollege of T echnology ) • If a question has question-structure of ThreeTerm and appears in YesNo category, it will have Affirm_3Term question-structure Given the question “số lượng sinh viên học lớp khoa học máy tính 45 phải không?” (“is that the number of students studying in computer science class is 45?”) has query-tuple ( Affirm_3Term, ManyClass, sinh viênstudent , họcstudy , lớp khoa học máy tínhcomputerscienceclass , 45 ) • A question will have question-structure of Compare if it belongs to one of three question-structure types Normal, UnknRel and UnknTerm, and contains a comparing-phrase which is detected by preprocessing module; the querytuple’s Term3 is used to hold this comparison information For example, the question “sinh viên có điểm trung bình cao khoa công nghệ thông tin?” (“which student has highest grade point average in faculty of Information Technology?” ) has query-structure of Compare and query-tuple (UnknTerm , Who , ? , điểm trung bìnhgrade point average , khoa cơng nghệ thơng tinf aculty of Inf ormation T echnology , cao nhấthighest ) • If a question contains either token “vàand ” or “màand ”/“hoặcor ”, it will have two or more query-tuples corresponding with And/Or question-structure However, with some question like “Phạm Đức Đăng học trường hướng dẫn ai?” ( “Which university does Pham Duc Dang study and who advises him?”), it contains “vàand ”, but it has question-structure of Or and two following querytuples: ( Normal , Entity , trườngschool , họcstudy , Phạm Đức ĐăngP ham Duc Dang ,? ) and ( UnknTerm , Who , ? , hướng dẫnguide , Phạm Đức ĐăngP ham Duc Dang , ? ) LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 42 Appendix C • If a question appearing in YesNo class belongs to And or Or question-structure types, it will have Affirm_MoreTuples question-structure • If a question corresponds two query-tuples and returned results of this querytuple is considered as miss-element in remaining query-tuple, it will have question-structure of Clause For example, the question “những sinh viên có quê Hà Nội học lớp nào?” (“which classes students coming Hanoi study?”) has question-structure of type Clause and two query-tuples: ( Normal , Entity , lớpclass , họcstudy , ? , ? ) and ( Normal , ? , sinh viênstudent , có quêhas including question-class, hometown , Hà NộiHanoi , ? ), without We can see that returned results of sub-question represented by second querytuple indicate missing element of Term2 in the first query-tuple • If a composite question is constructed from two or more independent clauses, it will have question-structure of Combine For example, the question “ai có quê Hà Tây sinh viên học khoa công nghệ thông tin?” (“who from Hatay and which student study in faculty of Information Technology?”) has two query-tuples: ( UnknTerm , Who ,? , có quêhas hometown , Hà TâyHatay , ? ) and ( Normal , Entity , sinh viênstudent , họcstudy , khoa công nghệ thông tinf aculty of Inf ormation T echnology , ? ) In each query-tuple, in general, Term1 represents a concept, without cases of Affirm, Affirm_3Term and Affirm_MoreTuples question-structures; Term2 and Term3 , if exist, represent entities (maybe representing concepts in case of Definition question-structure) LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com Bibliography Ion Androutsopoulos, Graeme Ritchie, and Peter Thanisch Natural language interfaces to databases — an introduction Natural Language Engineering, 1:29–81, 1995 William W Cohen, Pradeep Ravikumar, and Stephen E Fienberg A 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Vietnamese Text- based Conversational Agent A text- based conversational agent is a program allowing the conversational interactions between human and... luanvanchat@agmail.com Chapter Text- based Conversational Agent for Vietnamese In this chapter, we will describe a Vietnamese text- based conversational agent (CA) architecture and focus on creating... ordered transformation among those contexts LUAN VAN CHAT LUONG download : add luanvanchat@agmail.com 28 Chapter Text- based Conversational Agent for Vietnamese Table 4.3: Order of transformation