Semanlink - Distributional semantics AND Information retrieval

> Transferring the success of word embeddings to Information Retrieval (IR) task is currently an active research topic. While embedding-based retrieval models could tackle the vocabulary mismatch problem by making use of the embedding’s inherent similarity between distinct words, most of them struggle to compete with the prevalent strong baselines such as TF-IDF and BM25.

Considering a practical ad-hoc IR task composed of two steps, matching and scoring, compares the performance of several techniques that leverage word embeddings in the retrieval models to compute the similarity between the query and the documents (namely word centroid similarity, paragraph vectors, Word Mover’s distance, as well as a novel inverse document frequency (IDF) re-weighted word centroid similarity).

> We confirm that word embeddings can be successfully employed in a practical information retrieval setting. The proposed cosine similarity of IDF re-weighted, aggregated word vectors is competitive to the TF-IDF baseline.

2018-01-28 A propos

gensim/WMD_tutorial.ipynb

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2017-12-23 A propos

(fastText) Euclidean distance instead of cosine-similarity?

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2017-12-07 A propos

How to use pre-trained word vectors from Facebook’s fastText

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2017-11-25 A propos

Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings (2015)

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2017-11-12 A propos

Semantics with Dense Vectors

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2017-11-11 A propos

Using Gensim Word2Vec Embeddings in Keras | Ben Bolte's Blog

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2017-10-23 A propos

Distributed Word Representations for Information Retrieval

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2017-10-01 A propos

Concept Search on Wikipedia · Chris McCormick

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2017-09-10 A propos

Gensim tutorial: Similarity Queries

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2017-07-19 A propos

Latent semantic indexing ("Introduction to Information Retrieval" Manning 2008)

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VSM : problem with synonymy and polysemy (eg. synonyms are accorded separate dimensions)

Could we use the co-occurrences of terms to capture the latent semantic associations of terms and alleviate these problems?

Concluding remarks:

- computational cost of the SVD is significant
    - biggest obstacle to the widespread adoption to LSI.
    - One approach to this obstacle: build the LSI representation on a randomly sampled subset of the documents, following which the remaining documents are ``folded in'' (cf Gensim tutorial "[Random Projection (used as an option to speed up LSI)](https://radimrehurek.com/gensim/models/rpmodel.html)")
- As we reduce k, recall tends to increase, as expected.
- **Most surprisingly**, a value of k in the low hundreds can actually increase precision. **This appears to suggest that for a suitable value of *k*, LSI addresses some of the challenges of synonymy**.
- LSI works best in applications where there is little overlap between queries and documents. (--??)

The experiments also documented some modes where LSI failed to match the effectiveness of more traditional indexes and score computations.

LSI shares two basic drawbacks of vector space retrieval:
    
- no good way of expressing negations
- no way of enforcing Boolean conditions.

LSI can be viewed as soft clustering by interpreting each dimension of the reduced space as a cluster and the value that a document has on that dimension as its fractional membership in that cluster.

2017-07-19 A propos

Indexing by Latent Semantic Analysis - Deerwester et al. (1990)

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2017-07-18 A propos

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2017-07-10 A propos

gensim: models.phrases – Phrase (collocation) detection

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2017-07-10 A propos

gensim/FastText_Tutorial.ipynb

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2017-06-28 A propos