1. Reading Report on Hybrid Question Answering System
系统阅读
孙亚伟

2. Articles
ISOFT at QALD-5: Hybrid question answering system over linked data and text data
Seonyeong Park, Pohang University of Science and Technology
In CLEF 2015 Working Notes Papers

3. Introduction System Description Experiment Conclusion Outline
System Architecture
Basic Analysis
Query Generation
Semantic Answer Type
Multi-information Tagged Text Database
SPARQL query template generator
Experiment
Conclusion

4. Introduction Complex Questions
Where was the "Father of Singapore" born?
Which Secretary of State was significantly involved in the United States‘ dominance of the Caribbean？
Who is the architect of the tallest building in Japan?
Which German mathematicians were members of the von Braun rocket group?
How old was Steve Jobs' sister when she first met him?
Answering these questions need information from both structured data and unstructured data

5. QA over linked data and text data Method: combine KBQA and IRQA
Introduction
QA over linked data and text data
Method: combine KBQA and IRQA
Extract clues using IRQA
If fail to find clues, generate SPARQL query

6. Multi-information Tagged Text Database SPARQL query template generator
System Description
System Architecture
Basic Analysis
Query Generation
Semantic Answer Type
Multi-information Tagged Text Database
SPARQL query template generator

7. System Architecture

8. Example

9. Ordinary NLP techniques
Basic Analysis
Method
Statistical and Rule
Ordinary NLP techniques
Tokenization, POS tagging, Dependency Parsing
Keyword , Term , NE
QA system oriented techniques
Question to statement(Q2S)
Lexical answer type (LAT)
Phrase extraction
Predicate phrase
Prepositional phrase

10. Example

11. Question  Sequential Phrases Queries
Query Generation
Question  Sequential Phrases Queries
First query: concatenating the two rightmost phrases
Next query: concatenation of the answer of the first query and the next rightmost phrase
Repeat this process to find the answer
Note: If fail to find clues, generate SPARQL query

12. Semantic Answer Type
SAT Classification
Type Set from DBpedia
Features
Keywords, LAT, type of each NE, Interrogative wh-word, predicate and its arguments
Accuracy
71.42% (3-level type ontology)
84.62% (2-level type ontology)

13. Multi-information Tagged Text Database
Each Sentence in Wikipedia
Plain text
Tagged text with co-reference resolution and disambiguation information
Title from which Wikipedia page the sentence is
POS tagging, dependency parsing, and SRL result

14. SPARQL query template generator
Detect words from each question to extract the SPARQL template
Questions including arithmetic information
Comparative word such as ‘deeper’ , Superlative word such as ‘deepest’
Yes/No questions
If not ‘wh-question’ nor ‘list-question’, then ‘yes/no question’
Simple questions
Using lexical matching and semantic similarity, Map predicate to properties in KB

15. Experiment
Test: QALD-5 hybrid question test dataset
Data: Wikipedia, DBpedia 3.10

16. Success Question QGeneration Process
[53].Who is the architect of the tallest building in Japan?[Building]
in Japan/tallest building/architect
[1]. Find tallest building in Japan is
“Tokyo_Skytree” [using IR]
[2]. Map “architect” to “architect” 
Nikken_Sekkei [using sparql]
[55].In which city where Charilie Chaplin’s half brothers born[City]
Charlie Chaplin half brother /born
[1].Find the half brother of Charlie
Chaplin [using IR]
[2].Select ?uri {Sydney_Chaplin birthplace?uri} [using sparql]
“England”, “United Kingdom” and “London”
[3]. Using Semantic answer type, filter “London”. [Filter]
[58].Are there man-made lakes in Australia that are deeper than 100 meters?[Place]
man-made lake Australia / deeper 100
[1]. Extract answer clues of “man -made lake Australia”. [using IR]
[2]. Compare the length of each named entities and check the more than one river is deeper than 100 meters. [using sparql]

17. Error No find answer clue Question Question generation
[51].Where was the “Father of Singapore” born? [Place]
“Father of Singapore” born/Where
[52].Which Secretary of State was significantly involved in the United States‘ dominance of the Caribbean [ADMINISTRATIVE REGION]
Unites’ dominance of the Carbbean/involve/
Secretary
[56].Which German mathematicians were members of the von Braun rocket group?[Person]
member von Braun rocket group
/German mathematician
[57].Which writers converted to Islam[Person]
writer converted Islam
[59].Which movie by the Coen brothers stars John Turturro in the role of a New York City playwright?[Place]
role of a New York City playwright/Coen
brother stars
[60].Which of the volcanoes that erupted in 1550 is still active?[Place]
volcanoes/erupted in 1550/active

18. Error Question Question generation Error
[54].What is the name of the Viennese newspaper founded by the creator of the croissant[Person]
creator of croissant/ Viennese newspaper
founded
Cannot map the “founded” to predicate in DBpedia
[60].Which of the volcanoes that erupted in 1550 is still active?[Place]
volcanoes/erupted in 1550/active
Cannot generate appropriate query to extract answer clue

19. Conclusion Hybrid QA System Future
Both KBQA approach and IRQA approach
First search text data
If the results are not appropriate or related to arithmetic, generate SPARQL query
Future
Extend query to find relevant answer clue
Predicate Mapping
Semantic Parsing
Information Extraction

20. Tools Stanford co-reference tool: co-reference resolution
DBPedia Spotlight: map NEs in the question to entities in DBpedia
Predicate Mapping
lexical matching
semantic similarity on explicit semantic analysis(ESA)
ClearNLP
Tokenization, POS tagging and dependency parsing
WordNet: term extraction
Lucene: Wikipedia index
libSVM: SAT classifier

21. 致谢
欢迎老师和同学提问!

22. 讨论 ISOFT ISOFT优点是什么？ ISOFT缺点是什么？ 问题分解成短语 从右开始，文本检索
一旦文本检索不相关或与算术计算有关，进入SPARQL模块
最后利用答案类型过滤
ISOFT优点是什么？
ISOFT缺点是什么？

23. 讨论 Hybrid QA System 的关键是什么？
问句分析？Predicate-argument Structure? 问句拆分子句？子句如何求解？原子操作是什么？
短语检索？底层数据文本分析？
Entity Linking？Predicate Mapping？Semantic Matching?
SPARQL Construction?
Candidate Answers Ranking? Filters?
Textual Entailment? Paraphrase?