Question Answering Systems
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1 Question Answering Systems An Introduction Potsdam, Germany, 14 July 2011 Saeedeh Momtazi Information Systems Group
2 Outline 2 1 Introduction
3 Outline 2 1 Introduction 2 History
4 Outline 2 1 Introduction 2 History 3 QA Architecture Factoid QA Opinion QA
5 Outline 2 1 Introduction 2 History 3 QA Architecture Factoid QA Opinion QA 4 Summary
6 Outline 3 1 Introduction 2 History 3 QA Architecture Factoid QA Opinion QA 4 Summary
7 Why QA 4
8 Why QA 4
9 Why QA 4
10 Why QA 4
11 Why QA 4
12 Why QA 4
13 Why QA 4
14 Why QA 4
15 Why QA 4
16 Why QA 4
17 Why QA 4
18 Why QA 5
19 Why QA 5
20 Why QA 5
21 Why QA 5
22 Why QA 5
23 QA vs. SE 6
24 QA vs. SE 6 longer input
25 QA vs. SE 6 longer input keywords natural language questions
26 QA vs. SE 6 longer input keywords natural language questions shorter output
27 QA vs. SE 6 longer input keywords natural language questions documents short answer strings shorter output
28 QA Types 7 Closed-domain only answer questions from a specific domain. Open-domain answer any domain independent question.
29 Outline 8 1 Introduction 2 History 3 QA Architecture Factoid QA Opinion QA 4 Summary
![locations, and the results of baseball matches LUNAR [Woods, 1977] Developed to answer natural](/docs-images/86/94402066/images/30-1.jpg "language questions about the geological analysis of rocks returned by the Apollo moon missions Able to")
30 History 9 BASEBALL [Green et al., 1963] One of the earliest question answering systems Developed to answer user questions about dates, locations, and the results of baseball matches LUNAR [Woods, 1977] Developed to answer natural language questions about the geological analysis of rocks returned by the Apollo moon missions Able to answer 90% of questions in its domain posed by people not trained on the system
31 History 10 STUDENT Built to answer high-school students questions about algebraic exercises. PHLIQA Developed to answer the user s questions about European computer systems. UC (Unix Consultant) Answered questions about the Unix operating system LILOG Was able to answer questions about tourism information in cities within Germany
32 History 11 Closed-domain systems Extracting answers from structured data (database) Converting natural language question to a database query
33 History 11 Closed-domain systems Extracting answers from structured data (database) Labor intensive to build Converting natural language question to a database query Easy to implement
34 Open-domain QA 12 Closed-domain QA Open-domain QA Using a large collection of unstructured data (e.g., the Web) instead of databases
35 Open-domain QA 12 Closed-domain QA Open-domain QA Using a large collection of unstructured data (e.g., the Web) instead of databases Covering many subjects Information constantly added and updated No manual work for building database
36 Open-domain QA 12 Closed-domain QA Open-domain QA Using a large collection of unstructured data (e.g., the Web) instead of databases Covering many subjects Information constantly added and updated No manual work for building database Some times information is not up to date Some information is wrong More irrelevant information
37 Open-domain QA 12 Closed-domain QA Open-domain QA Using a large collection of unstructured data (e.g., the Web) instead of databases Covering many subjects Information constantly added and updated No manual work for building database Some times information is not up to date Some information is wrong More irrelevant information More complex systems are required
38 Open-domain QA 13 START [Katz, 1997] Utilized a knowledge-base to answer the user s questions The knowledge-base was first created automatically from unstructured Internet data Then it was used to answer natural language questions
39 Open-domain QA 13 START [Katz, 1997] Utilized a knowledge-base to answer the user s questions The knowledge-base was first created automatically from unstructured Internet data Then it was used to answer natural language questions
40 Open-domain QA 13 START [Katz, 1997] Utilized a knowledge-base to answer the user s questions The knowledge-base was first created automatically from unstructured Internet data Then it was used to answer natural language questions
![Open-domain QA 13 START [Katz, 1997]](/docs-images/86/94402066/images/41-0.jpg "Utilized a knowledge-base to answer")
41 Open-domain QA 13 START [Katz, 1997] Utilized a knowledge-base to answer the user s questions The knowledge-base was first created automatically from unstructured Internet data Then it was used to answer natural language questions
42 IBM Watson 14 Webpage: IBM Watson Video: IBM and the Jeopardy Challenge Video: A Brief Overview of the DeepQA Project
43 Outline 15 1 Introduction 2 History 3 QA Architecture Factoid QA Opinion QA 4 Summary
44 16 Architecture Who is Warren Moon s Agent? Corpus Web Question Analysis Question Classification Query Construction Document Retrieval Sentence Retrieval Sentence Annotation Answer Extraction Answer Validation Leigh Steinberg
45 16 Architecture Who is Warren Moon s Agent? Corpus Web Question Analysis Question Classification Query Construction Document Retrieval Sentence Retrieval Sentence Annotation Answer Extraction Answer Validation Leigh Steinberg Natural Language Processing
46 16 Architecture Who is Warren Moon s Agent? Corpus Web Question Analysis Question Classification Query Construction Document Retrieval Sentence Retrieval Sentence Annotation Answer Extraction Answer Validation Leigh Steinberg Natural Language Processing Information Retrieval
47 16 Architecture Who is Warren Moon s Agent? Corpus Web Question Analysis Question Classification Query Construction Document Retrieval Sentence Retrieval Sentence Annotation Answer Extraction Answer Validation Natural Language Processing Information Retrieval Information Extraction Leigh Steinberg
48 Question Analysis 17 Target Extraction Extracting the target of the question Using question target at the query construction step
49 Question Analysis 17 Target Extraction Extracting the target of the question Using question target at the query construction step Pattern Extraction Extracting a pattern from the question Matching the pattern with a list of pre-defined question patterns Finding the corresponding answer pattern Realizing the position of the answer in the sentence at the answer extraction step
50 Question Analysis 17 Target Extraction Extracting the target of the question Using question target at the query construction step Pattern Extraction Extracting a pattern from the question Matching the pattern with a list of pre-defined question patterns Finding the corresponding answer pattern Realizing the position of the answer in the sentence at the answer extraction step examples: Question: In what country was Albert Einstein born? Question Pattern: In what country was X born? Answer Pattern: X was born in Y.
51 Question Analysis 17 Target Extraction Extracting the target of the question Using question target at the query construction step Pattern Extraction Extracting a pattern from the question Matching the pattern with a list of pre-defined question patterns Finding the corresponding answer pattern Realizing the position of the answer in the sentence at the answer extraction step
52 Question Analysis 17 Target Extraction Extracting the target of the question Using question target at the query construction step Pattern Extraction Parsing Extracting a pattern from the question Matching the pattern with a list of pre-defined question patterns Finding the corresponding answer pattern Realizing the position of the answer in the sentence at the answer extraction step Using a dependency parser to extract the syntactic relations between question terms Using the dependency relation path between question words to extract the correct answer at answer extraction step
53 Question Classification 18 Classifying the input question into a set of question types Defining a map between question types and available named entity labels Using question type to extract strings that have the same type as the input question at the answer extraction step
54 Question Classification 18 Classifying the input question into a set of question types Defining a map between question types and available named entity labels Using question type to extract strings that have the same type as the input question at the answer extraction step Example: Question: In what country was Albert Einstein born? Type: Country
55 Question Classification 19 Classification taxonomies BBN Pasca & Harabagiu Li & Roth
56 Question Classification 19 Classification taxonomies BBN Pasca & Harabagiu Li & Roth
57 19 Question Classification Classification taxonomies BBN Pasca & Harabagiu Li & Roth 6 coarse- and 50 fine-grained classes Types HUMAN ABREVIATION DESCRIPTION LOCATION NUMERIC ENTITY mountain state country city
58 Question Classification 20 Using available classifiers based on the favorite model SVM: SVM-light Maximum Entropy: Maxent, Yasmet Naive Bayes
59 Query Construction 21 Goal: Formulating a query with a high chance of retrieving relevant documents Task: Assigning a higher weight to the question target Using query expansion techniques to expand the query Thesaurus-based query expansion Relevance feedback Pseudo-relevance feedback
60 Document Retrieval 22 Importance: QA components use computationally intensive algorithms Time complexity of the system strongly depend on the size of the to be processed corpus Task: Reducing the search space for the subsequent modules Retrieving relevant documents from a large corpus Selecting top n retrieved document for the next steps
61 Document Retrieval 23 Using available information retrieval models Vector Space Model Probabilistic Model Language Model
62 Document Retrieval 23 Using available information retrieval models Vector Space Model Probabilistic Model Language Model Using available information retrieval toolkits
63 Sentence Retrieval 24 Task: Finding a small segment of text that contains the answer Benefits beyond document retrieval: Documents are very large Documents span different subject areas The relevant information is expressed locally Retrieving sentences simplifies the answer extraction step
64 Sentence Retrieval 25 Language model-based sentence retrieval Query Q P(Q S 2 ) S 1 S 7 S 6 S 2 S 3 S 5 S 4
65 Sentence Retrieval 25 Language model-based sentence retrieval Query Q P(Q S 2 ) S 1 S 7 S 6 S 2 S 3 S 5 S 4 Query likelihood model: P(Q S) = M i=1 P(q i S)
66 Sentence Retrieval 26 Challenge: The term mismatch problem in sentence retrieval is more critical than document retrieval
67 Sentence Retrieval 26 Challenge: The term mismatch problem in sentence retrieval is more critical than document retrieval Approaches:
68 Sentence Retrieval 26 Challenge: The term mismatch problem in sentence retrieval is more critical than document retrieval Approaches: Query expansion, (Pseudo-)Relevance Feedback
69 Sentence Retrieval 26 Challenge: The term mismatch problem in sentence retrieval is more critical than document retrieval Approaches: Query expansion, (Pseudo-)Relevance Feedback Does not work at sentence-level retrieval
70 Sentence Retrieval 26 Challenge: The term mismatch problem in sentence retrieval is more critical than document retrieval Approaches: Query expansion, (Pseudo-)Relevance Feedback Term relationship models WordNet Term clustering model Translation model Triggering model Does not work at sentence-level retrieval
71 Sentence Annotation 27 Annotating relevant sentences using linguistic analyses: Named entity recognition Dependency parsing Noun phrase chunking Semantic role labeling
72 Sentence Annotation 27 Annotating relevant sentences using linguistic analyses: Named entity recognition Dependency parsing Noun phrase chunking Semantic role labeling Example: Question: In what country was Albert Einstein born?
73 Sentence Annotation 27 Annotating relevant sentences using linguistic analyses: Named entity recognition Dependency parsing Noun phrase chunking Semantic role labeling Example (NER): Sentence1: Albert Einstein was born on 14 March Sentence2: Albert Einstein was born in Germany. Sentence3: Albert Einstein was born in a Jewish family.
74 Sentence Annotation 27 Annotating relevant sentences using linguistic analyses: Named entity recognition Dependency parsing Noun phrase chunking Semantic role labeling Example (NER): Sentence1: Albert Einstein was born on 14 March Person Name Date Sentence2: Albert Einstein was born in Germany. Person Name Country Sentence3: Albert Einstein was born in a Jewish family. Person Name Religion
75 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation
76 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example: Question: In what country was Albert Einstein born?
77 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example: Question: In what country was Albert Einstein born? Question Pattern: In what country was X born? Answer Pattern: X was born in Y.
78 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example (Pattern): Sentence1: Albert Einstein was born on 14 March Sentence2: Albert Einstein was born in Germany. Sentence3: Albert Einstein was born in a Jewish family.
79 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example (Pattern): Sentence2: Albert Einstein was born in Germany. Sentence3: Albert Einstein was born in a Jewish family.
80 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example: Question: In what country was Albert Einstein born? Question Pattern: In what country was X born? Answer Pattern: X was born in Y.
81 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example: Question: In what country was Albert Einstein born? Question Pattern: In what country was X born? Answer Pattern: X was born in Y. Question Type: LOCATION - COUNTRY
82 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example (Pattern): Sentence2: Albert Einstein was born in Germany. Sentence3: Albert Einstein was born in a Jewish family.
83 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example (NER): Sentence2: Albert Einstein was born in Germany. Person Name Country Sentence3: Albert Einstein was born in a Jewish family. Person Name Religion
84 Answer Extraction 28 Extracting candidate answers based on various informations: The extracted patterns from question analysis The dependency pars of question from the question analysis The question type from question classification All annotated data from sentence annotation Example (NER): Sentence2: Albert Einstein was born in Germany. Person Name Country Sentence3: Albert Einstein was born in a Jewish family. Person Name Religion
85 Answer Validation 29 Using the Web as a knowledge resource Sending question keywords and answer candidates to a search engine Finding the frequency of the answer candidate within the Web data Selecting the most likely answers based on the frequencies
86 Answer Validation 30 Query model: Bag-of-Word Noun-Phrase-Chunks Declarative-Form
87 Answer Validation 30 Query model: Bag-of-Word Noun-Phrase-Chunks Declarative-Form Example: Question: In what country was Albert Einstein born?
88 Answer Validation 30 Query model: Bag-of-Word Noun-Phrase-Chunks Declarative-Form Example: Question: In what country was Albert Einstein born? Answer Candidate: Germany
89 Answer Validation 30 Query model: Bag-of-Word Noun-Phrase-Chunks Declarative-Form Bag-of-Word: Albert Einstein born Germany
90 Answer Validation 30 Query model: Bag-of-Word Noun-Phrase-Chunks Declarative-Form Bag-of-Word: Albert Einstein born Germany Noun-Phrase-Chunks: Albert Einstein born Germany
91 Answer Validation 30 Query model: Bag-of-Word Noun-Phrase-Chunks Declarative-Form Bag-of-Word: Albert Einstein born Germany Noun-Phrase-Chunks: Albert Einstein born Germany Declarative-Form: Albert Einstein born Germany
92 31 Architecture Who is Warren Moon s Agent? Corpus Web Question Analysis Question Classification Query Construction Document Retrieval Sentence Retrieval Sentence Annotation Answer Extraction Answer Validation Natural Language Processing Information Retrieval Information Extraction Leigh Steinberg
93 Fact vs. Opinion 32 Factual questions Who is Warren Moon s Agent? When was Mozart born? In what country was Albert Einstein born? Who is the director of the Hermitage Museum?
94 Fact vs. Opinion 33 Opinionated questions What do people like about Wikipedia? What organizations are against universal health care? What are the public opinions on human cloning? How do students feel about Microsoft products?
95 Outline 34 1 Introduction 2 History 3 QA Architecture Factoid QA Opinion QA 4 Summary
96 35 Architecture What do people like about Wikipedia? Corpus Web Question Analysis Question Classification Query Construction Document Retrieval Sentence Retrieval Sentence Annotation Answer Extraction Answer Validation
97 35 Architecture What do people like about Wikipedia? Corpus Question Analysis Question Classification Query Construction Document Retrieval Web Sentence Annotation Answer Extraction Answer Validation
98 35 Architecture What do people like about Wikipedia? Corpus Question Analysis Question Classification Query Construction Document Retrieval Sentence Retrieval Web Sentence Annotation Answer Extraction Answer Validation
99 35 Architecture What do people like about Wikipedia? Corpus Web Question Analysis Question Classification Query Construction Document Retrieval Sentence Annotation Answer Extraction Answer Validation Sentence Retrieval S Opinion Classification
100 35 Architecture What do people like about Wikipedia? Corpus Web Question Analysis Question Classification Query Construction Document Retrieval Sentence Annotation Answer Extraction Answer Validation Sentence Retrieval S Opinion Classification S Polarity Classification
101 35 Architecture What do people like about Wikipedia? Question Analysis Corpus Web Query Construction Document Retrieval Sentence Annotation Answer Extraction Answer Validation Sentence Retrieval S Opinion Classification S Polarity Classification
102 35 Architecture What do people like about Wikipedia? Question Analysis Q Semantic Classification Corpus Web Query Construction Document Retrieval Sentence Annotation Answer Extraction Answer Validation Q Polarity Classification Sentence Retrieval S Opinion Classification S Polarity Classification
103 35 Architecture What do people like about Wikipedia? Question Analysis Q Semantic Classification Corpus Web Query Construction Document Retrieval Sentence Annotation Answer Extraction Answer Validation Q Polarity Classification Sentence Retrieval S Opinion Classification S Polarity Classification Natural Language Processing Information Retrieval Information Extraction
104 35 Architecture What do people like about Wikipedia? Question Analysis Q Semantic Classification Corpus Web Query Construction Document Retrieval Sentence Annotation Answer Extraction Answer Validation Q Polarity Classification Sentence Retrieval S Opinion Classification S Polarity Classification Natural Language Processing Information Retrieval Information Extraction Opinion Mining
105 Q Polarity Classification 36 Running in parallel with question classification Task: Decide whether the input question has a positive or negative polarity
106 Q Polarity Classification 36 Running in parallel with question classification Task: Decide whether the input question has a positive or negative polarity examples: What do people like about Wikipedia? Why people hate reading Wikipedia articles?
107 Q Polarity Classification 36 Running in parallel with question classification Task: Decide whether the input question has a positive or negative polarity Approaches: Using a rule-based model based on subjectivity lexicon Running a classifier trained on an annotated corpus Training on overall vocabulary of the dataset Training on all polarity expressions from the subjectivity lexicon
108 S Opinion Classification 37 Importance: Sentence retrieval output is mixed (factual & opinionated) Opinion question answering systems are looking for opinionated sentences
109 S Opinion Classification 37 Importance: Sentence retrieval output is mixed (factual & opinionated) Opinion question answering systems are looking for opinionated sentences Goal: Classifying retrieved sentences as opinionated or factual.
110 S Opinion Classification 37 Importance: Sentence retrieval output is mixed (factual & opinionated) Opinion question answering systems are looking for opinionated sentences examples: Question: What do people like about Wikipedia?
111 S Opinion Classification 37 Importance: Sentence retrieval output is mixed (factual & opinionated) Opinion question answering systems are looking for opinionated sentences examples: S1: I agree Wikipedia is very much in handy when your online; however, I can not use it when I am not online. S2: Wikipedia began as a complementary project for Nupedia, a free online English-language encyclopedia project. S3: Jimmy Wales and Larry Sanger co-founded Wikipedia in January S4: Wikipedia is a great way to access lots of information.
112 S Opinion Classification 37 Importance: Sentence retrieval output is mixed (factual & opinionated) Opinion question answering systems are looking for opinionated sentences Opinion S1: I agree Wikipedia is very much in handy when your online; however, I can not use it when I am not online. S4: Wikipedia is a great way to access lots of information. Fact S2: Wikipedia began as a complementary project for Nupedia, a free online English-language encyclopedia project. S3: Jimmy Wales and Larry Sanger co-founded Wikipedia in January 2001.
113 S Opinion Classification 37 Importance: Sentence retrieval output is mixed (factual & opinionated) Opinion question answering systems are looking for opinionated sentences Goal: Classifying retrieved sentences as opinionated or factual. Approaches: Running a classifier trained on an annotated corpus SVM Maximum Entropy Naive Bayes
114 S Polarity Classification 38 Task: Distinguishing between positive and negative sentences Returning sentences which have the same polarity as the input question Approach: Using a classifier to classify opinionated sentences as positive or negative Using a small set of lightweight linguistic polarity features Considering the distance between polarity features and the topic in the sentence Using a dependency parser to consider syntactic features
115 Outline 39 1 Introduction 2 History 3 QA Architecture Factoid QA Opinion QA 4 Summary
116 Summary 40
117 Next Semester 41 Master Seminar on Question Answering Systems
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