Semanlink - Vector space model

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33 Documents (Short List)

[1910.03524] Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

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2019-10-09 About

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

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2019-03-25 About

Tutorial on Poincaré Embeddings (Jupyter Notebook )

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2018-05-20 About

Implementing Poincaré Embeddings | RARE Technologies

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2018-05-20 About

[1710.04099] Wembedder: Wikidata entity embedding web service

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2018-02-13 About

gensim/WMD_tutorial.ipynb

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2017-12-23 About

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

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2017-11-25 About

[1706.00957] Semantic Vector Encoding and Similarity Search Using Fulltext Search Engines

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2017-11-11 About

Semantics with Dense Vectors

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2017-11-11 About

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

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2017-10-23 About

Concept Search on Wikipedia · Chris McCormick

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2017-09-10 About

Gensim tutorial: Similarity Queries

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2017-07-19 About

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 About

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

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2017-07-18 About

gensim : Similarity Queries using Annoy (Tutorial)

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2017-07-10 About

gensim: models.phrases – Phrase (collocation) detection

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2017-07-10 About

From Frequency to Meaning: Vector Space Models of Semantics (2010)

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2017-07-10 About

gensim/FastText_Tutorial.ipynb

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2017-06-28 About

How to reduce dimension for TfIdf / BOW vector? : MachineLearning

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2017-06-26 About

Gensim tutorials

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2017-06-23 About

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

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2017-06-09 About

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2017-06-09 About

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

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2017-06-09 About

[1512.00765] Learning Semantic Similarity for Very Short Texts

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2017-06-09 About

News classification with topic models in gensim

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2017-06-07 About

Using Gensim for LDA (notebook)

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2017-06-02 About

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

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2017-06-02 About

Text Summarization with Gensim

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2017-06-02 About

pyLDAvis

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2017-06-02 About

gensim: models.word2vec – Deep learning with word2vec

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2017-06-01 About

Word2vec in gensim Tutorial | RaRe Technologies

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2017-06-01 About

Latent semantic analysis and indexing - EduTech Wiki

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Latent Semantic Analysis

2017-05-26 About

Summarize Documents using Tf-Idf – Alexander Crosson – Medium

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2017-05-24 About

Properties

sl:creationDate : 2013-05-31
sl:creationTime : 2013-05-31T14:46:32Z
sl:describedBy :
- https://en.wikipedia.org/wiki/Vectorial_semantics
- https://en.wikipedia.org/wiki/Vector_space_model
rdf:type : sl:Tag
skos:altLabel : Vectorial semantics@en
skos:prefLabel : Vector space model@en