Semanlink - Word sense / Lexical ambiguity

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Word sense / Lexical ambiguity

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(((ل()(ل() 'yoav))))👾 sur Twitter : "our attempt at producing large-scale, sense-annotated corpora, with automatically derived word senses ..."

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2022-03-28 A propos

Clustering of semantically enriched short texts (2019)

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2019-11-15 A propos

[1710.06632] Towards a Seamless Integration of Word Senses into Downstream NLP Applications

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2018-10-09 A propos

Linear algebraic structure of word meanings – Off the convex path

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2018-09-20 A propos

[1601.03764] Linear Algebraic Structure of Word Senses, with Applications to Polysemy

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> Here it is shown that multiple word senses reside
in linear superposition within the word
embedding and simple sparse coding can recover
vectors that approximately capture the
senses

> Each extracted word sense is accompanied by one of about  2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense.

> The success of the approach is mathematically explained using a variant of
the random walk on discourses model

("random walk": a generative model for language). Under the assumptions of this model,  there
exists a linear relationship between the vector of a
word w and the vectors of the words in its contexts (It is not the average of the words in w's context, but in a given corpus the matrix of the linear relationship does not depend on w. It can be estimated, and so we can compute the embedding of a word from the contexts it belongs to)

[Related blog post](/doc/?uri=https%3A%2F%2Fwww.offconvex.org%2F2016%2F07%2F10%2Fembeddingspolysemy%2F)

2018-08-28 A propos

A Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)

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2018-06-08 A propos

Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017)

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2017-10-21 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

Propriétés

sl:creationDate : 2017-10-21
sl:creationTime : 2017-10-21T17:08:58Z
rdf:type : sl:Tag
skos:altLabel : Polysemy
skos:prefLabel : Word sense / Lexical ambiguity@en