Semanlink - Word sense / Lexical ambiguity

> We find that the softmax layer (the output layer) causes significant inefficiency due to the large vocabulary size.
Therefore, we redesign the learning objectiv.
> Specifically, the proposed approach bypasses the softmax layer by performing language modeling with dimension reduction, and allows the models to leverage pre-trained word embeddings.
Our framework reduces the time spent on the output layer to a negligible level, eliminates almost all the trainable parameters of the softmax layer and performs language modeling without truncating the vocabulary.
When applied to ELMo, our method achieves a 4 times speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks.

**decouples learning contexts and words**

> Instead of using
a softmax layer to predict the distribution of the
missing word, we utilize and extend the SEMFIT
layer (Kumar and Tsvetkov, 2018) to **predict the
embedding of the missing word**.

2019-03-02 About

The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar

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2018-12-03 About

[1811.05370] Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents

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2018-11-20 About

iliaschalkidis/ELMo-keras: Re-implementation of ELMo on Keras

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2018-11-14 About

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

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2018-10-09 About

Linear algebraic structure of word meanings – Off the convex path

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2018-09-20 About

AI Can Recognize Images, But Text Has Been Tricky—Until Now | WIRED

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2018-09-08 About

[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 About

[1806.06259] Evaluation of sentence embeddings in downstream and linguistic probing tasks

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2018-06-19 About

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

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2018-06-08 About

Sense2vec with spaCy and Gensim · Blog · Explosion AI

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2018-04-08 About

ELMo: Deep contextualized word representations (2018)

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ELMo

2018-02-16 About

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

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2017-10-21 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