information retrieval and extraction 資訊檢索與擷取

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Information Retrieval and Extraction資訊檢索與擷取

Chia-Hui Chang, Assistant Professor

Dept. of Computer Science & Information EngineeringNational Central University, Taiwan

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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

Definitiona 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 to produce 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 document management system or components which could be integrated to form a document management system and the name of either the company developing the system or the company using thesystem. These components are: a computer, image scanner or optical character recognition system, and an information retrieval or text management system.

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Example (Continued)

<con> Concepts:1. document management, document processing, office automation electronic imaging2. image scanner, optical character recognition (OCR)3. text management, text retrieval, text database4. optical disk

<fac> Factors:<def> Definitions

Document Management-The creation, storage and retrieval of documents containing, text, images, and graphics. Image Scanner-A device that converts a printed image into a video image, 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 of their 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

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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?» How can a representation be generated from a description

of the document?» What are retrievable units and how are they organized?

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Research Issues (Continued)

Issue 2How 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?

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

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

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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

Coreference 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