Search Words Mastery

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About this activity

Quiz on search terms and information retrieval.

Created by

RA DRM

Peru

Download the paper version to play

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Answer if these songs won the Grammy for Best Song or Record of the Year (the general ones, category Grammys do not count)
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Test your knowledge of the bones, joints, and muscles of the foot and ankle with this interactive quiz. Answer with ✅ if the statement is correct and ❌ if it is incorrect.
Is it true?

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In this game, you will have to determine if the adjective provided accurately describes the noun. Test your knowledge of adjectives and see if you can spot the correct matches!


Search Words MasteryOnline version Quiz on search terms and information retrieval. by RA DRM 1 Levenshtein distance is used to measure semantic similarity. Sí No 2 BM25 ignores document length normalization. Sí No 3 PageRank is used to determine term weight in document scoring. Sí No 4 Neural IR cannot benefit from pretraining. Sí No 5 A precision-recall curve is not used in evaluation of search systems. Sí No 6 TF-IDF combines term frequency with inverse document frequency to score terms. Sí No 7 Language models for retrieval assume a uniform distribution over terms in documents. Sí No 8 Idf is computed as log(N/df) where N is total terms in corpus. Sí No 9 Query expansion can improve recall by adding semantically related terms. Sí No 10 Word embeddings are used only for small vocabularies in search. Sí No 11 Query likelihood model assumes queries are generated by a single fixed document. Sí No 12 Inverted index stores documents for each term. Sí No 13 Stemming reduces words to a common stem to improve matching. Sí No 14 In information retrieval, stop words are often removed to reduce dimensionality. Sí No 15 Stop words enhance precision by boosting common terms. Sí No 16 Inverse document frequency increases with how common a term is across documents. Sí No

Search Words MasteryOnline version

Quiz on search terms and information retrieval.

by RA DRM

Levenshtein distance is used to measure semantic similarity.

Sí

BM25 ignores document length normalization.

Sí

PageRank is used to determine term weight in document scoring.

Sí

Neural IR cannot benefit from pretraining.

Sí

A precision-recall curve is not used in evaluation of search systems.

Sí

TF-IDF combines term frequency with inverse document frequency to score terms.

Sí

Language models for retrieval assume a uniform distribution over terms in documents.

Sí

Idf is computed as log(N/df) where N is total terms in corpus.

Sí

Query expansion can improve recall by adding semantically related terms.

Sí

Word embeddings are used only for small vocabularies in search.

Sí

Query likelihood model assumes queries are generated by a single fixed document.

Sí

Inverted index stores documents for each term.

Sí

Stemming reduces words to a common stem to improve matching.

Sí

In information retrieval, stop words are often removed to reduce dimensionality.

Sí

Stop words enhance precision by boosting common terms.

Sí

Inverse document frequency increases with how common a term is across documents.

Sí

You can integrate the game into an LMS compatible with LTI 1.1 or LTI 1.3 such as Canvas, Moodle, or Blackboard. This way, the scores will be automatically saved into the platform’s gradebook.

Search Words Mastery

Yes or No

About this activity

Created by

Download the paper version to play

Top Games

Yes or No

Grammys for best song/record

Yes or No

Is it created with Artificial Intelligence? - Complete game

Yes or No

Foot and Ankle Anatomy Quiz

Yes or No

Is it true?

Yes or No

Adjective Checker

Search Words MasteryOnline version

by RA DRM

Levenshtein distance is used to measure semantic similarity.

BM25 ignores document length normalization.

PageRank is used to determine term weight in document scoring.

Neural IR cannot benefit from pretraining.

A precision-recall curve is not used in evaluation of search systems.

TF-IDF combines term frequency with inverse document frequency to score terms.

Language models for retrieval assume a uniform distribution over terms in documents.

Idf is computed as log(N/df) where N is total terms in corpus.

Query expansion can improve recall by adding semantically related terms.

Word embeddings are used only for small vocabularies in search.

Query likelihood model assumes queries are generated by a single fixed document.

Inverted index stores documents for each term.

Stemming reduces words to a common stem to improve matching.

In information retrieval, stop words are often removed to reduce dimensionality.

Stop words enhance precision by boosting common terms.

Inverse document frequency increases with how common a term is across documents.

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