Text Processing and Indexing Concepts

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Questions and Answers

What does the vector for document D2 represent?

The term frequency for apple being higher than for pear (correct)

The absence of both terms in the document

The term frequency for apple and pear being equal

The term frequency for pear being higher than for apple

What key concept does the distance between vectors relate to?

The similarity between documents (correct)

The number of terms in the vocabulary

The actual content of the documents

The size of the documents in terms of words

Which document has the lowest term frequency for both terms?

D3 (correct)

How many dimensions are used in the vector representation of the documents?

Two dimensions for terms Signup and view all the answers

Which document is best matched for a query close to the vector of D1?

D4 Signup and view all the answers

What is the first step in the process of building an index from text files?

Start with files Signup and view all the answers

Why are common words considered unhelpful for search functionality?

They occur too frequently Signup and view all the answers

What is the purpose of tokenisation in text processing?

To split text strings using white space Signup and view all the answers

Which of the following is NOT a part of the tokenisation process?

Counting unique tokens Signup and view all the answers

What is referred to as 'stop words'?

Frequently occurring common words Signup and view all the answers

How can search time be affected in the context of indexing?

By the logic used for searching Signup and view all the answers

Which step directly follows splitting text into words in the indexing process?

Creating tokens Signup and view all the answers

In the content provided, how many times did the word 'software' occur in the programming_language.txt file?

4 Signup and view all the answers

Which Boolean connector used in queries will result in a smaller set of documents?

AND Signup and view all the answers

What is a key advantage of Boolean retrieval compared to best match retrieval?

Exact queries Signup and view all the answers

In Boolean searching, what would be the outcome of a query structured as 'cat OR dog'?

Documents containing at least one of the terms Signup and view all the answers

What happens when using a combination of 'AND' and 'OR' in a Boolean query?

May lead to larger result sets Signup and view all the answers

What is one disadvantage of Boolean retrieval models?

Output can be unordered Signup and view all the answers

Why might users struggle with Boolean queries?

Users often have trouble formulating good queries Signup and view all the answers

What model represents documents as vectors in multi-dimensional space?

Vector-space model Signup and view all the answers

Which query is most likely to yield a small result set?

cat AND dog Signup and view all the answers

What is the purpose of indexing in search systems?

To build a database to return search results more quickly Signup and view all the answers

What role do web crawlers play in building search indices?

They automate the process of gathering content to create indices Signup and view all the answers

How does a search system utilize citations in documents?

To determine the relevance and importance of documents Signup and view all the answers

What technique can be used to display the content of a web page after JavaScript evaluation?

Running a headless browser Signup and view all the answers

What does full-text search entail in a search system?

Searching the entire content of documents for specific terms Signup and view all the answers

What is a common output from a background process in a search system?

Search indices created from crawled content Signup and view all the answers

What would be a likely result of utilizing a NoSQL solution like OpenSearch?

Ability to handle large amounts of unstructured data Signup and view all the answers

What effect does stopword removal have on document representation?

Reduces noise in the data Signup and view all the answers

In the context of search systems, what is the main purpose of recommendations?

To suggest documents based on user behavior and similarities Signup and view all the answers

Which of the following statements about stemming is true?

It transforms words minimally to their root forms Signup and view all the answers

What is the purpose of using inverse document frequency (idf)?

To reduce the impact of rare terms in the documents Signup and view all the answers

Which similarity measure is commonly used for comparing document vectors?

Cosine similarity Signup and view all the answers

How does adding more terms to the analysis affect the document representation?

It increases the dimensional space orthogonality Signup and view all the answers

What does the dot product indicate in vector similarity measures?

The degree of similarity between two vectors Signup and view all the answers

What is the primary benefit of using tf.idf in document representation?

It accounts for both document frequency and term frequency Signup and view all the answers

What is the primary goal of document representation techniques like stemming and stopword removal?

To enhance the computational efficiency Signup and view all the answers

Which document has a higher dot product with the query vector?

D2 Signup and view all the answers

What does a higher cosine similarity value indicate?

A stronger match between the document and the query Signup and view all the answers

What is a disadvantage of using simple matching algorithms?

They lack a theoretical basis for matching Signup and view all the answers

What is the purpose of using cosine similarity in information retrieval?

To account for the length of the document Signup and view all the answers

Which document showed better matching performance using cosine similarity?

D1 with a value of 0.73 Signup and view all the answers

What factor is not considered in simple matching algorithms?

Length of the document Signup and view all the answers

Web search processes typically include all of the following steps except:

Generate content Signup and view all the answers

In the context provided, which statement is true regarding algorithm performance?

Simple matching is often less effective than cosine similarity Signup and view all the answers

Study Notes

Search Systems

Search systems expect users to search documents using text, citations, or links to other documents
Users may search for similar documents based on similar topics or other users' decisions
Search systems should allow for real-time or fast search capabilities

Full-text search

Full-text search involves costly processing and data input/output
Building indices is more efficient than searching files repeatedly
Indices are queried to return search results
Background processes are used to create indices, including web crawlers and operating systems
Web crawlers crawl content to build indices
Operating systems index files using background processes

Citations and Links

Search systems can look for documents containing citations and links
Counting citations builds up the importance of documents
The direction of citations, and the number of citations, indicate the importance of a page
Web pages can be referenced by many other pages
Documents are frequently crawled by background processes

Recommendations

Search systems can recommend similar documents based on other users' searches
Systems use correlation information from users' searches
Further information about recommender systems is available from specific lectures

Indexing

Search systems build databases containing indices
NoSQL solutions, like OpenSearch, are often used
NoSQL databases are accessed from web browser user interfaces

Web Page Structure

Web servers return page content to browsers
Web pages consist of HTML, CSS, and JavaScript
Browsers convert JavaScript into HTML and render HTML and CSS
Search often requires evaluating JavaScript to access text content
Parsers, like BeautifulSoup, extract text from HTML pages

Downloading HTML Files

Three example HTML files are often downloaded for study purposes
These files represent simple web pages
JavaScript is ignored in the example cases
HTML files are converted to text files

Searching Text Files

The term "software" is often included as a search query
Two separate approaches to searching through programming languages are available

Counting Words

The word "software" is counted in text files
Search systems need to identify the word "software" rather than only text fragments

Scaling Up

Searching times vary
Smaller files are processed faster than very large files
Big datasets, like billions of web pages, require indexing

Conclusions

Search systems can be built from standard tools
Search times depend on file size and the number of files to index
Often, indexing is necessary to conduct full-text searches on the majority of files

Selecting Tokens

Tokens are necessary for building the index
Input text is split into tokens using white space delimiters
Punctuation is frequently removed
Tokens are converted to lowercase for consistency
Special rules for nouns may apply
Removing nouns may be undesirable in some instances.

Splitting Text into Tokens

Common words are irrelevant for search.
These words are also referred to as stop words
Stop lists are used to remove these words

Word Frequency: Zipf Distribution

A graph showing the relationship between rank and frequency of words in a document.

Stop Word Removal

Stop word removal reduces irrelevant words from a search query
Standard stop words lists are often used
More specific stop lists may improve search performance.

Stemming

Nouns can be singular or plural
Verbs can be in different forms, requiring stemming (e.g., send, sending, sent)
Stemming reduces tokens to their stem word

Stemming Approaches

Dictionary stemming is more accurate but requires more processing time
Algorithm stemming is often preferred due to its better performance
Various stemming algorithms exist, based on rules
Algorithm stemming results may not always be correct

Porter Stemming Algorithm

Martin Porter developed the Porter stemming algorithm in 1980
This algorithm removes suffixes from words
Words are converted to their singular form using rules.

Porter Algorithm (Problems)

The Porter algorithm can make mistakes (e.g., "europe" vs "european")
It does not correctly handle proper nouns, like names
It is a popular algorithm (and its basis) used in other non-English stemmers

Converting Queries into Tokens

Search queries are treated the same way as document text
Stop words are removed from queries

Other Indexing Choices

Search systems can use phrases instead of tokens
Lists of known phrases are useful
Identifying synonyms, like "shut" and "close", is useful
Term weighting can be based on importance

Term Weighting

Search systems process terms from words in documents
Early information retrieval systems use presence or absence of terms to indicate relevance
Early weighting often saved space and was suitable for short documents or titles
More advanced systems estimate term importance in the document as well as overall importance in the entire dataset

Term Frequency

Term frequency (tf) is a measure of how important a word is within a single document
Higher term frequencies often imply higher importance
There are several ways to measure term frequencies in a document. Raw frequency is the simplest calculation
Results affected because the document with the highest term frequency is shown first in a search query

Inverse Document Frequency

Inverse document frequency (idf) measures how prevalent the words are within the dataset overall
Rare terms are significantly more important than frequent terms in a corpus

TFIDF

TF-IDF combines term frequency and inverse document frequency to represent the importance of a term within a document based on its overall frequency in the entire dataset

Information Retrieval Models

Information retrieval models are methods for determining the best match between a query and a document

Boolean Retrieval

The oldest information retrieval (IR) model
It focuses solely on the presence or absence of terms to ascertain retrieval
It doesn't use terms' weights

Boolean Retrieval (Terms, Formula, and Applications)

Query terms utilize Boolean connectors (AND, OR, NOT).
Boolean retrieval is similar to SQL
Precise searches, such as in legal fields, frequently use the Boolean retrieval approach

Example Boolean Queries

Some examples of queries using Boolean operators (AND, OR, NOT).

Boolean Retrieval Usage

Boolean retrieval narrows down results
It does not weight terms

Boolean Retrieval Problems

Large result sets (especially using OR)
Small result sets, when using AND
Unintuitive language

Boolean Retrieval Advantages

Exact queries
Easy to program
Precise results

Best Match Retrieval

This is an alternative to Boolean retrieval.
Most retrieval models use the best match approach
Documents are ranked according to how well they match the query
First result is generally the most relevant result in this approach

Advantages over Boolean Retrieval

Queries are in natural language, reducing user effort
Easier to modify queries

Vector-Space Model

Terms are expressed as vectors in a multi-dimensional space.
One axis or dimension is associated with each term
A separate document vector is created for every document
Proposed in 1960 and remains a popular model

Building the Vectors

Building vectors involves assigning numerical values to represent document terms
Values are chosen to simplify illustrations
Vectors define positions in multi-dimensional space

Building the Vectors (Continued)

Document vectors imply document similarity
The closest vector to the query vector has the best match
Closest document vectors represent the best retrieval candidate

Effect of Stopword Removal

Stopword removal alters document vector positions
Relevance is often measured in multi-dimensional space without using stopwords

Effect of Stemming

Stemming affects document vector dimensionality
Documents sharing a stem word are closer in the vector space

Frequency Measures

IDF and tf.idf measurement techniques often create better separation in the vector space.

Adding More Terms

Each added term results in an orthogonal axis
TFIDF values determine position on an axis

Similarity Measures

Methods for comparing vectors exist, including coordinate-level matching and calculating the angle between query and document vectors using the cosine coefficient

Simple Matching (Dot Product)

The dot product between query and document vectors is calculated
The highest dot product value corresponds to the closest document
Simple matching returns documents containing common terms.

Cosine Matching

Cosine similarity considers document length and shared terms
Closer angle implies higher similarity

Simple and Cosine Compared

Dot product measures (simple matching) and angles in multi-dimensional space (cosine) are alternatives
Matching algorithms return slightly different results (e.g., different orderings that may yield different matches)

Advantages

Vector space models are generally easy to understand and interpret, including their geometric view
They work well with various similarity measures
They often lead to better performance than Boolean models

Disadvantages

Vector models might not have a strong theoretical foundation in some instances
They do not inherently provide predictive power, requiring empirical evaluations
They may lack control over document selection

Web Searches

Similar principles apply to standard information retrieval
Data acquisition, indexing, query matching, and presentation are steps
The Web differs in that data is decentralized and changes dynamically

Data Collection

Data collection on the web has to be frequently collected and updated for a search engine
Data is decentralized and distributed
Large scale Web data often is collected dynamically and is semi-structured
Web data handling frequently includes various file types (PDF, HTML, word), encodings, and languages

Data Collection (Deployment)

Web crawlers and robots collect data from web pages
These programs (often referred to as 'spiders') crawl through web pages recursively
They collect pages and follow links (crawling)

Adversarial Information Retrieval

Web search engines face manipulation attempts from individuals or companies
Content suppliers frequently try to manipulate search results
Manipulating search results include promotion of a company's own pages without paying for advertising
Manipulators might also intentionally hide the real content of a website, or redirect searchers to different pages

Static Data

Static datasets are typically well-defined and reliable

Web Data

Data on web pages can be old or have false or manipulated information

Web Search Engine Requirements

Web search engines have policies, as well as many considerations, regarding how sites should be indexed
Policies handle encoding schemes
Managing dynamic pages and keeping cached pages up-to-date are extra considerations

Query and Information Provided

Users might submit poor quality queries which might be one or two terms.
Search engines must often clean up or modify the query itself to match a format a typical database can use

Matching Web Pages

Web pages vary in quality
Search engines prioritize trustworthy pages
Web page readability must be considered
Pop-ups and other annoyances should be avoided
Security concerns, including HTTPS verification, need addressing

Web Page Ranking

Scoring and ranking web pages depends on search queries
Search engines combine multiple search scores (e.g., matching the query, popularity, age, etc.)
Popularity is a key factor affecting web retrieval
PageRank measurement of web page popularity

PageRank of Web Pages

PageRank algorithm assesses the importance of web pages

Graph of Web Pages

The web can be modeled as a graph

PageRank - Random Walk

Web page importance assesses random user page browsing
Probability of visiting a page is calculated
Important pages have higher probability scores

PageRank - Damping Factor

Adding a damping factor accounts for isolated graphs to improve PageRank accuracy
Random jumps account for user behavior

PageRank - Damping Factor (Continued)

Random jumps add stochastic behavior to the PageRank calculation, addressing isolated or dead-end pages
Random jumps can occur with a given probability (e.g., 15%).
The user might "jump" to another page with a given probability
Normal browsing would reflect a typical use probability for following web links (e.g., 85%).

Dead Ends

In PageRank algorithm, dead-end web pages cause issues
Random jumps give dead end pages a chance to be revisited

PageRank Formula

A mathematical formula is often used to calculate PageRank

PageRank - Page Quality

An important quality factor affecting PageRank is the PageRank values of other pages that link into a page.

Aggregated Search

Web search incorporates several sources
Multiple sources might be combined in a search (e.g. images, news, video, blog content)

Search Tools

Search tools used for information retrieval (often based on open source tools)

Lucene

A frequently used (free and open-source) library for information retrieval

Solr

Enterprise-grade search engine, based on Lucene, often used for large-scale data

Elasticsearch

Enterprise-grade search engine, based on Lucene
JSON documents are stored
Solr relies on JSON format for storing and handling documents

Other Tool Kits

Other web search tool kits are also frequently used for search functionality
Many websites and search engines sometimes use Python
Some search engines have APIs (Application Programming Interfaces) that allow for programmatic access

Case Study

One example case study shows a Python-based system implementing facetted and keyword searches on a dataset
The case study demonstrates a Python system using the case study's company names to retrieve other company data.

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Description

This quiz explores key concepts in text processing, including vector representation, tokenization, and the role of stop words in indexing. Test your understanding of how documents are analyzed for effective search functionality. Ideal for students studying information retrieval or data management.