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Data Science - Information Retrieval

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Information retrieval (IR) in computing and information science is the process of obtaining information system resources that are relevant to an information need from a collection of those resources. Searches can be based on full-text or other content-based indexing.

Data Science - Information Retrieval

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25 Questions

1. What is the formula for accuracy?

# right --------- # totalThe proportion of non-relevant documents that are retrieved, out of all non-relevant documents available.both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

2. explicit sementic analysis (ESA)

The proportion of non-relevant documents that are retrieved, out of all non-relevant documents available.both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval# right --------- # totalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

3. What is the standard Boolean model of IR?

both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval# right --------- # totalThe proportion of non-relevant documents that are retrieved, out of all non-relevant documents available.provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

4. In plain engish, what is fall-out?

5. What is a way to garuentee search results for queries?

both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts# right --------- # totalCreate and use collations Collations are combinations of suggestions

6. Rank SIFT

uses ranking techniques to improve the performance of the SIFT algorithm. used in key point localization or descriptor generation# right --------- # totalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptsboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval

7. Regular expression matches do not always line up nicely on word boundaries, so the inverted index cannot be based on words Then _________ old information retrieval trick and build an index of n-grams, substrings of length n. This sounds more general than it is. In practice, there are too few distinct 2-grams and too many distinct 4-grams, so 3-grams (trigrams) it is.

# right --------- # totalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptsbuild an index of n-grams, substrings of length n. Use trigrams In practice, there are too few distinct 2-grams and too many distinct 4-grams eg: (1) Google Code Search (2) Google Code Project Hosting (3) Google Web Search _Co: {1, 2} Sea: {1, 3} e_W: {3} ogl: {1, 2, 3} _Ho: {2} Web: {3} ear: {1, 3} oje: {2} _Pr: {2} arc: {1, 3} eb_: {3} oog: {1, 2, 3} _Se: {1, 3} b_S: {3} ect: {2} ost: {2} _We: {3} ct_: {2} gle: {1, 2, 3} rch: {1, 3} Cod: {1, 2} de_: {1, 2} ing: {2} roj: {2} Goo: {1, 2, 3} e_C: {1, 2} jec: {2} sti: {2} Hos: {2} e_P: {2} le_: {1, 2, 3} t_H: {2} Pro: {2} e_S: {1} ode: {1, 1} tin: {2}both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval

8. What is recall in terms of likelihood?

provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptslikelihod that a relevant result was returned# right --------- # totalboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval

9. What is recall in terms of only / all results?

both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalAll relevant results are returned# right --------- # totalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

10. LSI overcomes two of the most problematic constraints of Boolean keyword queries: 1 = ____________ 2 = ____________

# right --------- # totalboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval1. multiple words that have similar meanings (synonymy) 2. words that have more than one meaning (polysemy)provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

11. What is awful way to do autocomplete?

both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptssave a bunch of strings to SQL SELECT exercise_name FROM exercise_table WHERE exercise_name LIKE 'bar%';# right --------- # total

12. Okapi BM25

provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptsboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievala ranking function used by search engines to rank matching documents according to their relevance to a given search query a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document, regardless of the inter-relationship between the query terms within a document (e.g., their relative proximity). BM stands for Best Matching# right --------- # total

13. In plain english, what is precision in document retrievel?

# right --------- # totalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptsthe fraction of the documents retrieved that are relevant to the user's information need.both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval

14. List 6 limitations of VSMs:

provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts# right --------- # total'1. Long documents are poorly represented because they have poor similarity values (a small scalar product and a large dimensionality) 2. Search keywords must precisely match document terms; word substrings might result in a ''false positive match'' 3. Semantic sensitivity; documents with similar context but different term vocabulary won't be associated, resulting in a ''false negative match''. 4. The order in which the terms appear in the document is lost in the vector space representation. 5. Theoretically assumes terms are statistically independent. 6. Weighting is intuitive but not very formal.'both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval

15. What is the best way to model multi-keyword concepts?

map ngram to hash (that reflects unique meaning)# right --------- # totalboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

16. What is a probablistic interpretation of precision?

Precision is the probability that a (randomly selected) retrieved document is relevant.provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptsboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval# right --------- # total

17. unary operator

the part of the query immediately following is required to exist in any documents matched eg: +houseboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval# right --------- # totalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

18. polysemy

19. In practice queries may be_______ and there may be different shades of _______.

both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptsill-posed relevancy# right --------- # total

20. What is support?

both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalNumber of occurances in each classprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts# right --------- # total

21. In plain english, what is precision in document retrievel?

# right --------- # totalboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalthe fraction of the documents retrieved that are relevant to the user's information need.provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

22. Precision measures the ability to ____________.

provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts# right --------- # totalboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrievalcorrectly classify labeled examples

23. Fowlkes–Mallows index a much accurate representation for unrelated data than ___.

both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval# right --------- # totalRand indexprovide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts

24. It is better to focus on _______ extraction than general keyphrase extraction.

provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible conceptsn-gramboth the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval# right --------- # total

25. A perfect search engine should have a MRR of __

provide fine gramed semnatic representations of natural language texts in high dimensiona spac eof coimpreshensible concepts# right --------- # total1both the documents to be searched and the user's query are conceived as sets of terms Retrieval is based on whether or not the documents contain the query terms. Pros: + Clean formalism + Easy to implement + Intuitive concept Cons: - Exact matching may retrieve too few or too many documents - Hard to translate a query into a Boolean expression - All terms are equally weighted - More like data retrieval than information retrieval

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25 Questions

❤ If you liked Fatskills, consider supporting us by checking out The Life Manuals You Never Got.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know? Privacy | Terms |

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All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | OSHA Basics Quiz | What Should We Know?
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Without work one finishes nothing. - Ralph Waldo Emerson
© 2026 Fatskills.com