Challenges & Approaches in AI Text Processing & Retrieval, Study notes of Computer Science

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Artificial Intelligence

Programming Information Retrieval

Chris Brooks Department of Computer ScienceUniversity of San Francisco

Processing text Now that we know a little bit about how to consider andcompare different states, we’ll think about a problemwith a harder representation. English-language text^ Specifically, webpages We’ll look at several different approaches toautomatically summarizing and understandingdocuments.

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Difficulties What makes working with natural language hard? Nonunique parses Synonyms and multiple meanings Anaphora Slang and technical terms Analogy and metaphor Misspelling and incorrect grammar

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Information Retrieval Information retrieval deals with the storage, retrieval,organization of, and access to information items Overlaps with:^ Databases (more of a focus on content)^ AI^ Search engines

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Query Languages What are some sorts of query languages? Keyword - Google, Yahoo!, etc. Natural language - Ask.com SQL-style Similar item - Netflix, Amazon Multimedia - Flickr

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User tasks We’ll also distinguish between different types of usertasks. The most common are

searching

and^ browsing

Searching - the user has a specific information need,and wants a document that meets that need. “Find me an explanation of the re module in Python” Browsing - the user has a broadly defined set ofinterests, and wants information that satisfies his/herinterests. “Find me interesting pages about Python” These different modes have different models of success.

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Modeling a Document In order to match a query to a document, an IR systemmust have a

model

of the document.

This might be:^ A category or description (as in a library)^ A set of extracted phrases or keywords^ The full text of the document^ Full text with filtering

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“Bag of words” model The techniques we’ll look at today treat a document as a bag of words

Order is discarded; we just count how often each wordappears. No semantics involved Intuition: Frequently-appearing words give an indicationof subject matter. Advantage: No need to parse, computationally tractablefor large collections. Disadvantage: Contextual information and meaning islost.

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Data Cleaning Stemming

might also be performed. Word suffixes, such as pluralization, past tense, -ing areremoved. run, runs, running, runner all become run. Advantages: If we’re just counting words, this lets uscorrectly count different forms of a word. Disadvantages: dealing with abnormal forms(person/people, run/ran), potential misgrouping(university, universal) The stemmer can be tuned to minimize either

false

positives

(accidentally stemming a word it shouldn’t) or false negatives

(not stemming a word it should.) There’s some debate in the research community about

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“Bag of Words” Once a document has been cleaned, the simplest modeljust counts how many times each word occurs in thedocument. This is typically represented as a dictionary. You built this in assignment 1.

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Precision and Recall Precision measures how well returned documentsmatch a query.^ precision =

matchingDocs totalDocsReturned

Recall measures the fraction of relevant documentsreturned.^ recall =

relevantDocsReturned totalRelevantDocs When might we want high precision? High recall? Often, we can trade precision against recall.

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Boolean Queries Boolean queries are simple, but not very practical. User provides a set of keywords.^ Possibly also OR terms All documents containing all keywords are returned. This is the sort of query model that databases use

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Probabilistic Queries A simple extension is to allow partial matches on queries Score documents according to the fraction of queryterms matched Return documents according to score^ Example: Document contains “cat cat dog bunny fish”^ Query is “cat dog (bunny OR snake) bird”^ Score is 3/4.

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Probabilistic Queries Weaknesses:^ Still requires logical queries^ Doesn’t deal with word frequency^ Dependent on query length - short queries will havea hard time getting differentiated scores.^ The average Google query is only three words long!

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