Semanlink - Distributional semantics AND Vector space model

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Distributional semantics AND Vector space model

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28 Documents

Sent2Vec: An unsupervised approach towards learning sentence embeddings | RARE Technologies

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2019-03-25 A propos

Tutorial on Poincaré Embeddings (Jupyter Notebook )

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2018-05-20 A propos

Implementing Poincaré Embeddings | RARE Technologies

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2018-05-20 A propos

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 A propos

gensim/WMD_tutorial.ipynb

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2017-12-23 A propos

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

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2017-11-25 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

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

gensim : Similarity Queries using Annoy (Tutorial)

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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

Gensim tutorials

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2017-06-23 A propos

An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec

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2017-06-09 A propos

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2017-06-09 A propos

[1607.00570] Representation learning for very short texts using weighted word embedding aggregation

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2017-06-09 A propos

[1512.00765] Learning Semantic Similarity for Very Short Texts

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2017-06-09 A propos

News classification with topic models in gensim

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2017-06-07 A propos

Using Gensim for LDA (notebook)

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2017-06-02 A propos

(Relatively) quick and easy Gensim example code | William Bert

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2017-06-02 A propos

Text Summarization with Gensim

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2017-06-02 A propos

pyLDAvis

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2017-06-02 A propos

gensim: models.word2vec – Deep learning with word2vec

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2017-06-01 A propos

Word2vec in gensim Tutorial | RaRe Technologies

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2017-06-01 A propos

Latent semantic analysis and indexing - EduTech Wiki

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Latent Semantic Analysis

2017-05-26 A propos