hsin-hsi chen1-1 information retrieval and extraction hsin-hsi chen department of computer science...

Hsin-Hsi Chen 1-1

Information Retrieval and Extraction

Hsin-Hsi ChenDepartment of Computer Science and

Information EngineeringNational Taiwan University

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Chapter 1 Introduction

Hsin-Hsi Chen （陳信希）國立台灣大學資訊程學系

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Motivation• Information retrieval

– To retrieve information which might be useful or relevant to the user

– Representation, Storage, Organization, Access

• Information need (vs query)– Find all the pages containing information on college tennis t

eams which • (1) are maintained by an university in the USA and • (2) participate in the NCAA tennis tournament.

– To be relevant, the page must include information on the national ranking of the team in the last three years and the email or phone number of the team coach.

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資訊需求Information Need

• 找尋金馬獎 / 諾貝爾獎相關報導

獎金馬獎 / 諾貝爾獎金馬獎 / 諾貝爾獎得主今年金馬獎 / 諾貝爾獎得主今年金馬獎最佳影片得主

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檢索：今年金馬獎得主 (2000)

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檢索：今年金馬獎得主 (2008)

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檢索：今年諾貝爾獎得主 (2000)

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檢索：今年諾貝爾獎得主 (2008)

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尋找“門聯”資料 (2000)

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尋找“門聯”資料 (2008)

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Information versus Data Retrieval

• Data retrieval– Determine which documents of a collection

contain the keywords in the user query– Retrieve all objects which satisfy clearly defined

conditions in regular expression or relational algebra expression

– Data has a well defined structure and semantics– Solution to the user of a database system

• Information retrieval

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Database Management

• A specified set of attributes is used to characterize each item.EMPLOYEE(NAME, SSN, BDATE, ADDR, SEX, SALARY, DNO)

• Exact match between the attributes used inquery formulations and those attached to the record.

SELECT BDATE, ADDRFROM EMPLOYEEWHERE NAME = ‘John Smith’

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Basic Concepts• Content identifiers (keywords, index terms,

descriptors) characterize the stored texts.• degrees of coincidence between the sets of

identifiers attached to queries and documents

content analysisquery formulation

User task Logical viewof the documents

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The User Task

• Convey the semantics of information need

• Retrieval and browsing

Retrieval

Browsing

Database

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Logical View of Documents

• Full text representation

• A set of index terms– Elimination of stop-words– The use of stemming– The identification of noun groups– …

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From full text to a set of index terms

document

structurerecognition

text+structure

accents,spacing,

etc.stopwords

noungroups

stemming

automaticor manualindexing

structure

text

full textindexterms

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Indexing

• indexing: assign identifiers to text items.• assign: manual vs. automatic indexing• identifiers:

– objective vs. nonobjective text identifiers cataloging rules define, e.g., author names, publisher names, dates of publications, …

– controlled vs. uncontrolled vocabulariesinstruction manuals, terminological schedules, …

– single-term vs. term phrase

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The retrieval process

User Interface

Text Operations

QueryOperations

Searching

Ranking

Indexing

Index

DB ManagerModule

TextDatabase

Text

Text

logical viewlogical view

user need

userfeedback query

retrieved documents

rankeddocuments

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Information Retrieval• generic information retrieval 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 routingthe dissemination of incoming documents to appropriate users on the basis of user interest profiles

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Detection Need• Definition

a set of criteria specified by the user which describes the kind of information desired.– queries in document search task– profiles in routing task

• forms– keywords– keywords with Boolean operators– free text– example documents– ...

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Example

<head> Tipster Topic Description<num> Number: 033<dom> Domain: Science and Technology<title> Topic: Companies Capable of Producing Document

Management<des> Description:Document must identify a company who has the capability toproduce document management system by obtaining a turnkey-system or by obtaining and integrating the basic components.<narr> Narrative:To be relevant, the document must identify a turnkey documentmanagement system or components which could be integratedto form a document management system and the name of eitherthe company developing the system or the company using thesystem. These components are: a computer, image scanner oroptical character recognition system, and an information retrievalor text management system.

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Example (Continued)

<con> Concepts:1. document management, document processing, office automationelectronic imaging2. image scanner, optical character recognition (OCR)3. text management, text retrieval, text database4. optical disk<fac> Factors:<def> DefinitionsDocument Management-The creation, storage and retrieval of documents containing, text, images, and graphics.Image Scanner-A device that converts a printed image into a videoimage, without recognizing the actual content of the text or pictures.Optical Disk-A disk that is written and read by light, and are sometimes associated with the storage of digital images because oftheir high storage capacity.

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search vs. routing

• The search process matches a single Detection Need against the stored corpus to return a subset of documents.

• Routing matches a single document against a group of Profiles to determine which users are interested in the document.

• Profiles stand long-term expressions of user needs.• Search queries are ad hoc in nature.• A generic detection architecture can be used for

both the search and routing.

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Search• retrieval of desired documents from an existing corpus

• Retrospective search is frequently interactive.

• Methods

– indexing the corpus by keyword, stem and/or phrase

– apply statistical and/or learning techniques to better understand the content of the corpus

– analyze free text Detection Needs to compare with the indexed corpus or a single document

– ...

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Document Detection: Search

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Document Detection: Search(Continued)

• Document Corpus– the content of the corpus may have significant

the performance in some applications

• Preprocessing of Document Corpus– stemming– a list of stop words– phrases, multi-term items– ...

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• 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

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• 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

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• Compare Query with Index

• Resultant Rank Ordered List of Documents– Return the top ‘N’ documents – Rank the list of relevant documents from the

most relevant to the query to the least relevant

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Routing

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Routing (Continued)

• 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.

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Routing (Continued)

• Convert Detection Need to System Specific Query

• Building Index from Queries– 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

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Routing (Continued)

• Routing Profile Index– The index will be system specific and will make use of

all the preprocessing techniques employed by a particular detection system.

• 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.

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Routing (Continued)

• 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

• Compare Document with Index– Identify which Profiles are relevant to the document

– Given a document, which of the indexed profiles match it?

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Routing (Continued)

• Resultant List of Profiles– The list of Profiles identify which user should

receive the document

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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.

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Documents* Queries

DocumentRepresentation

QueryRepresentation

Comparison

Basic Architecture of an Information Retrieval System

* 此模型可以延伸到其他媒體所呈現的資訊

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Research Issues

• Given a set of description for objects in the collection and a description of an information need, we must consider

• Issue 1– What makes a good document representation?– What are retrievable units and how are they

organized?– How can a representation be generated from a

description of the document?

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Research Issues (Continued)

• Issue 2How can we represent the information need and how can we acquire this representation either from a description of the information need or through interaction with the user?

• Issue 3How can we compare representations to judge likelihood that a document matches an information need?

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Research Issues (Continued)

• Issue 4How can we evaluate the effectiveness of the retrieval process?

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Text Data Mining Tasks

• Information extraction -- facts, fill database

• Summarization

• Categorization

• Clustering

• Associations

• Temporal analysis of document collection

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Information Extraction:Beyond Document Retrieval

• Question and Answering– Q: Who is the author of the book, "The Iron

Lady: A Biography of Margaret Thatcher"?A: Hugo Young

– Q: What was the monetary value of the Nobel Peace Prize in 1989?A: $469,000

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Information Extraction

• Generic Information Extraction SystemAn 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.

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Information Extraction (Continued)

• What are the transducers or modules?

• What are their input and output?

• What structure is added?

• What information is lost?

• What is the form of the rules?

• How are the rules applied?

• How are the rules acquired?

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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

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Modules

• Text Zonerturn a text into a set of text segments

• Preprocessorturn 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

• Filterturn a set of sentences into a smaller set of sentences by filtering out the irrelevant ones

• Preparsertake a sequence of lexical items and try to identify various reliably determinable, small-scale structures

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Modules (Continued)

• Parserinput a sequence of lexical items and perhaps small-scale structures (phrases) and output a set of parse tree fragments, possibly complete

• Fragment Combinerturn a set of parse tree or logical form fragments into a parse tree or logical form for the whole sentence

• Semantic Interpretergenerate a semantic structure or logical form from a parse tree or from parse tree fragments

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Modules (Continued)

• Lexical Disambiguationturn a semantic structure with general or ambiguous predicates into a semantic structure with specific, unambiguous predicates

• Co-reference Resolution, or Discourse Processingturn a tree-like structure into a network-like structure by identifying different descriptions of the same entity in different parts of the text

• Template Generatorderive the templates from the semantic structures

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<DOC><DOCID> NTU-AIR_LAUNCH- 中國時報 -19970612-002 </DOCID><DATASET> Air Vehicle Launch </DATASET><DD> 1997/06/12 </DD><DOCTYPE> 報紙報導 </DOCTYPE><DOCSRC> 中國時報 </DOCSRC><TEXT>【本報綜合紐約、華盛頓十一日外電報導】在華盛頓宣布首度出售「刺針」肩射防空飛彈給南韓的第二天，美國與北韓今天在紐約恢復延擱已久的會談，這項預定三天的會談將以北韓的飛彈發展為重點，包括北韓準備部署射程可涵蓋幾乎日本全境的「蘆洞」一號長程飛彈的報導。

　美國國務院發言人柏恩斯說：「在有關北韓飛彈擴散問題上，美方的確有多項關切之處。」美國官員也長期懷疑北韓正對伊朗和敘利亞輸出飛彈，並希望平壤加入禁止擴散此種武器的

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國際公約。美國官員已知會北韓說，倘若北韓希望與美國建立正常的外交關係，就必須減少飛彈輸出。

　這項有關北韓飛彈計劃的會談是雙方於一九九六年四月在德國柏林舉行的首度會談的後續談判。美國在該次會談中要求北韓停止生產、測試及出售飛彈給他國，尤其是敘利亞和伊朗兩國。

　美國副助理國務卿艾恩宏和北韓外交部對外事務局局長李衡哲分別為雙方的談判代表，會談預定在十三日結束。

　柏恩斯說：「美方非常關心所有北韓本身，或是北韓與中共、伊朗或其他國家的飛彈問題。我們認為就此與他們舉行會談是甚為重要。」

　而為提昇南韓陸軍的自衛能力，美國於昨天宣布準備出售價值三億零七百萬美元的一千零六十五枚刺針飛彈與其他武器給南韓，它說，這項交易不會使朝鮮半島的緊張局勢惡化。

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五角大廈說：「這項設備與支援的銷售不會影響該區基本軍事均勢。」

　國務院也表示全力支持此項包含兩百一十三座發射台、支援設備、零件與訓練的交易。

　柏恩斯說：「這項交易獲得政府內每一個人的全力支持，它符合我們在朝鮮半島的政策。」他強調：「我們的第一優先是防衛南韓。」

　如果國會同意，這將是華府對南韓出售防空飛彈的第一筆交易。

</TEXT></DOC>

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<ID="3"> 十一日 <ID="4" REF="3" > 今天 <ID="5“ REF="3"> 出售「刺針」肩射防空飛彈給南韓的第二天

<ID="63" > 延擱已久的會談 <ID=“66” REF=“63”> 一九九六年四月在德國柏林舉行的首度會談的後續談判 <ID="65" REF="63"> 這項有關北韓飛彈計劃的會談 <ID="70" REF="65"> 會談 <ID="69" REF="65"> 會談 <ID="64" REF="63"> 這項預定三天的會談

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The Advanced Research and Development Activity (ARDA)

• a joint activity of the Intelligence Community (IC) and the Department of Defense (DOD) in late November 1998

• intelligence community's (IC) center for conducting advanced research and development related extracting intelligence from and providing security for information stored, transmitted, or manipulated by electronic means

情報

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ARDA R&D Programs

• Information Exploitation– Pulling Information – Pushing Information– Visualizing and Navigating Information

• Quantum Information Science & Photonics

• Digital Network Intelligence

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Pulling Information

• Providing answers to complex, multifaceted questions that analysts pose

• The analyst seeks to "pull" the answer out of multiple, very large, heterogeneous data sources that may physically reside in diverse locations

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Pulling Information (Continued)

• Accepting complex questions in a form natural to the analyst.• Questions may include judgment terms and an acceptable answer

may need to be based upon conclusions and decisions reached by the system and may require the summarization, fusion, and synthesis of information drawn from multiple sources.

• Translating analytic questions into multiple queries appropriate to the various data sets to be searched.

• Finding relevant information in distributed, multimedia, multilingual, multi-agency data sets.

• Analyzing, fusing and summarizing information into a coherent answer.

• Providing the answer to the analyst in the form that he/she want

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Pushing Information

• Providing information from multiple, very large, heterogeneous data sources that analysts do not ask

• The system discovers information in some profiling, clustering, pattern recognition, data mining, or other fashion and "pushes" this information to analysts that the system determines might have an interest.

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Pushing Information (Continued)

• Profiling and blind clustering of new data.

• Detecting anomalies, patterns and changes in large volumes of data.

• Analyzing the nature and description of the anomalies, patterns, and changes.

• Alerting the appropriate analyst(s) of the newly discovered information.

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Topics

• Introduction to Information Retrieval and Extraction

• Modeling• Retrieval Evaluation• Query Languages• Query Operations• Text and Multimedia Languages and Properties• Text Operations• Indexing and Searching

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Topics (Continued)

• User Interfaces and Visualization• Multimedia IR: Models and Languages• Multimedia IR: Indexing and Searching• Searching the Web• Digital Libraries• Information Extraction (IJCAI 1999)• Text Data Mining (ACL 1999)• Text Web Mining (AIRS 2004)

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TextIR

Retrieval Modelsand Evaluation

ImprovementsOn Retrieval

EfficientProcessing

Interfaces &Visualization

MultimediaModeling

& Searching

Human-ComputerInteraction for IR

MultimediaIR

Applicationsfor IR

Bibliographic

Systems

TheWeb

DigitalLibraries

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Information Sources• http://www-csli.stanford.edu/~schuetze/information-ret

rieval.html• Books

– Ricardo Baeza-Yates and Berthier Riberiro-Neto (1999) Modern Information Retrieval, Addison-Wesley.台灣進口商為 “華通書坊” 電話 : (03)5720317

– Salton, G. (1989) Automatic Text Processing. The Transformation, Analysis and Retrieval of Information by Computer. Reading, MA: Addison-Wesley.

– Frakes, W.B. and Baeza-Yates, R. (Eds.) (1992) Information Retrieval: Data Structures and Algorithms. Englewood Cliffs, NJ: Prentice Hall.

– Cheong, F. (1996) Internet Agents: Spiders, Wanderers, Brokers, and Bots. Indianapolis, IN: New Riders, 1996.

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Information Sources

• Karen Sparck Jones and Peter Willett (1997) Readings in Information Retrieval, CA: Morgan Kaufmann Publishers.

• Christopher D. Manning, et al. Introduction to Information Retrieval, Cambridge University Press. 2007. http://www-csli.stanford.edu/~schuetze/information-retrieval-book.html.

• Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau, Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer, 2005.

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Information Sources

• Conference Proceedings– ACM SIGIR Annual International Conference on

Research and Development in Information Retrieval (1978-)

– ACM International Conference on Digital Libraries

– ACM Conference on Information and Knowledge Management

– Text Retrieval Conference

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Information Sources(Continued)

• Journals– ACM Transactions on Information Systems

– Information Processing and Management

– Journal of the American Society for Information Science and Technology

– Journal of Documentation

– Information Systems

– Information Retrieval

– Knowledge and Information Systems