Generic IR Pipeline
27.1.5 Generic IR Pipeline
As we mentioned earlier, documents are made up of unstructured natural language text composed of character strings from English and other languages. Common examples of documents include newswire services (such as AP or Reuters), corpo- rate manuals and reports, government notices, Web page articles, blogs, tweets, books, and journal papers. There are two main approaches to IR: statistical and semantic.
In a statistical approach, documents are analyzed and broken down into chunks of text (words, phrases, or n-grams, which are all subsequences of length n characters in a text or document) and each word or phrase is counted, weighted, and measured for relevance or importance. These words and their properties are then compared with the query terms for potential degree of match to produce a ranked list of resulting documents that contain the words. Statistical approaches are further clas- sified based on the method employed. The three main statistical approaches are Boolean, vector space, and probabilistic (see Section 27.2).
Semantic approaches to IR use knowledge-based techniques of retrieval that broadly rely on the syntactic, lexical, sentential, discourse-based, and pragmatic lev- els of knowledge understanding. In practice, semantic approaches also apply some form of statistical analysis to improve the retrieval process.
Figure 27.1 shows the various stages involved in an IR processing system. The steps shown on the left in Figure 27.1 are typically offline processes, which prepare a set of documents for efficient retrieval; these are document preprocessing, document modeling, and indexing. The steps involved in query formation, query processing, searching mechanism, document retrieval, and relevance feedback are shown on the right in Figure 27.1. In each box, we highlight the important concepts and issues. The rest of this chapter describes some of the concepts involved in the various tasks within the IR process shown in Figure 27.1.
Figure 27.2 shows a simplified IR processing pipeline. In order to perform retrieval on documents, the documents are first represented in a form suitable for retrieval. The significant terms and their properties are extracted from the documents and are represented in a document index where the words/terms and their properties are stored in a matrix that contains these terms and the references to the documents that contain them. This index is then converted into an inverted index (see Figure
27.4) of a word/term vs. document matrix. Given the query words, the documents
27.2 Retrieval Models 1001
Document 3 Document 2
Document Corpus
SEARCH INTENT
Document 1 Information Need/Search
Stopword removal
Query Formation Stemming
Preprocessing
Keywords, Boolean, phrase,
proximity, wildcard queries, etc.
Thesaurus Digits, hyphens,
Query Processing Information extraction
punctuation marks, cases
Conversion from humanly
understandable to internal format Situation assessment
Modeling
Query expansion heuristics
Retrieval models
(users’s profile, related metadata,
Type of queries
etc.)
Choice of search strategy
Searching
Inverted index construction Mechanism
Indexing
(approximate vs. exact matches,
exhaustive vs. top K)
Index vocabulary
Type of similarity measure
Document statistics Index maintenance
Ranking results
Document
Storing user’s Relevance
Showing useful
Retrieval
feedback Feedback
metadata
Personalization
Pattern analysis
External data
Metadata
of relevant
ontologies
Integration
results Legend
Dashed line indicates
Figure 27.1
next iteration
Generic IR framework.
containing these words—and the document properties, such as date of creation, author, and type of document—are fetched from the inverted index and compared with the query. This comparison results in a ranked list shown to the user. The user can then provide feedback on the results that triggers implicit or explicit query expansion to fetch results that are more relevant for the user. Most IR systems allow for an interactive search where the query and the results are successively refined.
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» Introduction to Information Retrieval
» Types of Queries in IR Systems
» Evaluation Measures of Search Relevance
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