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5 quiz questions in IR, Information retrieval quiz, information
retrieval mcqs with answers, information retrieval, stemming,
query expansion, recall, search result relevance, thesaurus based query expansion, wordnet
Information Retrieval MCQs - SET 04
1. The heuristic process of cutting
off one or few letters from the end of words to extract the root words from
inflected words is called
a) Lemmatization
b) Stemming
c) Inverted index
d) Case folding
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Ans : (b)
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Answer: (b)
Stemming
Usually, a word
has multiple meanings based on its usage in text, similarly, different forms
of words convey related meaning, like “video” and “videos”, indicate
identical meaning. A word is modified to express different grammatical
categories such as tense, case, voice, aspect, person, number, gender, mood,
animacy, and definiteness. In linguistics, this process is called as
inflection. Inflection is the process of adding affixes to a word.
Stemming is the
process of removing a part of a word, or reducing a word to its stem or root. Stemming is a rule-based
approach. It simply slices the inflected words from prefix or suffix as per
the need using a set of commonly underused prefix and suffix, like “-ing”,
“-ed”, “-es”, “-pre”, etc. It results in a stem (root word) and that stem
need not be identical to the morphological root of the word.
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2. In thesaurus-based query expansion
technique, for every term t in a query, we expand the query using the synonyms
and related words of t taken from the thesaurus. This can increase ______
a) precision
b) recall
c) performance
d) mean average precision
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Ans : (b)
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Answer: (a)
recall
Thesaurus based
query expansion technique usually increases recall. This technique may
significantly reduce the precision.
Recall is the
fraction of relevant documents that are retrieved. For example, if there are
100 relevant documents for a given query, and the IR system retrieves 50 from
those relevant documents, then the recall is 50% (50/100).
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3. Which of the following is a
non-decreasing function of the number of documents retrieved?
a) precision
b) recall
c) accuracy
d) mean average precision
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Ans : (b)
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Answer: (b)
recall
Recall is a
non-decreasing function of the number of documents retrieved.
Recall = (Total
number of documents retrieved that are relevant)/(Total number of relevant
documents in the database)
As recall is
calculated based on the number of relevant documents retrieved from the set
of total relevant documents, it cannot decrease.
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4. Which of the following is not an
element to measure the relevance of search results in ad hoc information
retrieval?
a) A benchmark document collection
b) A benchmark suit of queries
c) An assessment of either relevant or
non-relevant for each query and each document
d) A set of trained users to check the
result
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Ans : (d)
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Answer: (d)
A set of trained users to check the result
Trained users are not necessarily to be used in this
process.
To measure ad hoc information retrieval effectiveness
in the standard way, we need a test collection consisting of three things:
1. A document collection
2. A test suite of information needs, expressible as
queries
3. A set of relevance judgments, that says either relevant
or non-relevant for each query-document pair.
Source: https://nlp.stanford.edu/IR-book/pdf/08eval.pdf
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5. In WordNet, the lexemes that share
same form but have unrelated meanings is
a) Homonym
b) Hypernym
c) Hyponym
d) Meronym
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Ans : (a)
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Answer: (a)
Homonym
In linguistics, homonym
refers to a relationship between words that have different meanings but are
pronounced the same or spelled the same or both. The word homonym can be used
as a synonym for homophone (same sound), homograph (same spelling) or both.
Example:
Homograph: bark
(tree bark) and bark (dog barks)
Homophone: write
and right
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Related links
Keywords
What are the elements required to measure the relevance of search result in an information retrieval system?
Define homonym and how they are used in Wordnet.
Why recall is referred as non-decreasing function of the number of documents retrieved?
Difference between stemming and lemmatization
