1. Information Retrieval and Extraction -- Course Introduction Chia-Hui Chang National Central University chia@csie.ncu.edu.tw

2. Information Retrieval (IR) Problem Definition for Generic IR system select and return to the user desired documents from a large set of documents in accordance with criteria specified by the user Functions  Document search the selection of documents from an existing collection of documents  Document routing the dissemination of incoming documents to appropriate users on the basis of user interest profiles

3. Document Detection: Search

4. Search: Text Operation Document Corpus  the content of the corpus may have significant performance in some applications Preprocessing of Document Corpus  stemming  stop words removing  phrases, multi-term items ...

5. Search: Indexing Building Index from Stems  key place for optimizing run-time performance  cost to build the index for a large corpus Document Index  a list of terms, stems, phrases, etc.  frequency of terms in the document and corpus  frequency of the co-occurrence of terms within the corpus  index may be as large as the original document corpus

6. Search: Query Operation Detection Need  the user ’ s criteria for a relevant document Convert detection need to system specific query  first transformed into a detection query, and then a retrieval query.  detection query: specific to the retrieval engine, but independent of the corpus  retrieval query: specific to the retrieval engine, and to the corpus

7. Search: Query Model Compare query with index Rank the list of relevant documents  Return the top ‘ N ’ documents

8. Routing

9. Routing: Detection Needs Profile of Multiple Detection Needs  A Profile is a group of individual Detection Needs that describes a user ’ s areas of interest.  All Profiles will be compared to each incoming document (via the Profile index).  If a document matches a Profile the user is notified about the existence of a relevant document.

10. Routing: Query Index Convert detection need to system specific query Building Index from Queries  The index will be system specific and will make use of all the preprocessing techniques employed by a particular detection system Routing Profile Index  similar to build the corpus index for searching  the quantify of source data (Profiles) is usually much less than a document corpus  Profiles may have more specific, structured data in the form of SGML tagged fields

11. Routing: Document Preprocessing Document to be routed  A stream of incoming documents is handled one at a time to determine where each should be directed  Routing implementation may handle multiple document streams and multiple Profiles Preprocessing of Document  A document is preprocessed in the same manner that a query would be set-up in a search  The document and query roles are reversed compared with the search process

12. Routing: Ranking Compare Document with Index  Identify which Profiles are relevant to the document  Given a document, which of the indexed profiles match it? Resultant List of Profiles  The list of Profiles identify which user should receive the document

13. Summary Generate a representation of the meaning or content of each object based on its description. Generate a representation of the meaning of the information need. Compare these two representations to select those objects that are most likely to match the information need.

14. DocumentsQueries Document Representation Query Representation Comparison Basic Architecture of an Information Retrieval System

15. Research Issues Issue 1  What makes a good document representation?  How can a representation be generated from a description of the document?  What are retrievable units and how are they organized? Issue 2 How can we represent the information need and how can we acquire this representation?  from a description of the information need or  through interaction with the user?

16. Research Issues (Continued) Issue 3 How can we compare representations to judge likelihood that a document matches an information need? Issue 4 How can we evaluate the effectiveness of the retrieval process?

17. Information Extraction Problem Definition An information extraction system is a cascade of transducers or modules that at each step add structure and often lose information, hopefully irrelevant, by applying rules that are acquired manually and/or automatically.

18. Example: Parser Transducer: parser Input: the sequence of words or lexical items Output: a parse tree Information added: predicate-argument and modification relations Information lost: no Rule form: unification grammars Application method: chart parser Acquisition method: manually

19. Modules Text Zoner turn a text into a set of text segments Preprocessor turn a text or text segment into a sequence of sentences, each of which is a sequence of lexical items, where a lexical item is a word together with its lexical attributes Filter turn a set of sentences into a smaller set of sentences by filtering out the irrelevant ones

20. Modules (Continued) Lexical Disambiguation turn a semantic structure with general or ambiguous predicates into a semantic structure with specific, unambiguous predicates Coreference Resolution, or Discourse Processing turn a tree-like structure into a network-like structure by identifying different descriptions of the same entity in different parts of the text Template Generator derive the templates from the semantic structures

21. Input Document types Plain Text IE: ( 一句一句，平鋪直述 )  利用 lexical 、 semantic analysis 。  AutoSlog(Riloff 93), LIEP(Huffman 95), CRYSTAL(Soderland 95), HASTEN(Krupka 95) 。 Semi-structured Web IE ： ( 半結構性文件 )  利用 html 語法特性 -tag 。  觀察所得之 heuristics: Layout 。

22. Text IE vs. IR IR simply finds texts and presents them to the user IE analyses texts and presents only the specific information from them that the user is interested in IE systems are more difficult and knowledge- intensive to build IE systems are tied to particular domains and scenarios

23. IE Tasks Plain Text  Named Entity recognition (NE)  Coreference Resolution (CO)  Template Element construction (TE)  Scenario Template production (ST) Semi-structured Documents  The template is a k-tuple relation

24. Web IE Applications Meta Search Engines Information Agents  以特定目的為導向，例 : 新聞代理人 (News spider) 網羅新聞 購物比價 找工作  ShopBot (Doorenbos 97)  Software LEGO(Hsu 99)

25. Information Integration Systems Unprocessed, Unintegrated Details Translation and Wrapping Semantic Integration Mediation Abstracted Information Text, Images/Video, Spreadsheets Hierarchical & Network Databases Relational Databases Object & Knowledge Bases SQLORBWrapper Mediator Human & Computer Users Heterogeneous Data Sources Information Integration Service Mediator User Services: Query Monitor Update Agent/Module Coordination

26. References Information Retrieval: Data Structures and Algorithms, Chapter 1. by W.B. Frakes and R. Baeza-Yates (Eds.) Englewood Cliffs, NJ: Prentice Hall, 1992. Modern Information Retrieval, Chapter 1 Modern Information Retrieval by R. Baeza-Yates and B. Ribeiro-Neto, Addison-Wesley, 1999. H. Cunningham, Information Extraction – a User Guide, http://www.dcs.shef.ac.uk http://www.dcs.shef.ac.uk I. Muslea, Extraction Patterns for Information Extraction Tasks: A Survey, The AAAI-99 Workshop on Machine Learning for Information Extraction.Extraction Patterns for Information Extraction Tasks: A Survey