Semanlink - Favoris ; Technical girls and guys AND Information retrieval: techniques

- the first one retrieves documents from a large source of knowledge (the retriever)
- the second one processes the support documents to solve the task (the reader).

> First the retriever selects support passages in a large knowledge
source. Then these passages are processed by the reader, along with the question, to generate an
answer

Inspired by knowledge distillation: the reader model is the teacher and the retriever is the student.

> More precisely, we use a sequence-to-sequence model as the reader, and use
the attention activations over the input documents as synthetic labels to train the retriever. 
> (**train the retriever by learning to approximate the attention score of the reader**)

Refers to:

- [REALM: Retrieval-Augmented Language Model Pre-Training](doc:2020/12/2002_08909_realm_retrieval_a)
- [Dehghani: Neural Ranking Models with Weak Supervision](doc:?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1704.08803)

2020-12-11 A propos

SLEEC: Sparse Local Embeddings for Extreme Multi-label Classification (2015)

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2020-08-11 A propos

Thomas Wolf sur Twitter : "I liked the LSH attention in the reformer..."

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2020-01-05 A propos

[1911.00172] Generalization through Memorization: Nearest Neighbor Language Models

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2019-12-20 A propos

[1602.01137] A Dual Embedding Space Model for Document Ranking

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2019-07-17 A propos

[1704.08803] Neural Ranking Models with Weak Supervision

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2019-01-27 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

A Theoretical Approach to Semantic Coding and Hashing | Simons Institute for the Theory of Computing (2016)

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2018-05-26 A propos

Modèle Neuronal de Recherche d'Information Augmenté par une Ressource Sémantique (2017)

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2018-02-12 A propos

Semantics with Dense Vectors

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2017-11-11 A propos

Semantic Hashing [9 mins] - Université de Toronto | Coursera

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2017-11-07 A propos

Semantic hashing (2008) - Ruslan Salakhutdinov, Geoffrey Hinton

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> We show how to learn a deep graphical model of the word-count vectors obtained from a
large set of documents. The values of the latent variables in the deepest layer are easy to
infer and give a much better representation of each document than Latent Semantic Analysis.
When the deepest layer is forced to use a small number of binary variables (e.g. 32),
the graphical model performs ‘‘semantic hashing”: Documents are mapped to memory
addresses in such a way that semantically similar documents are located at nearby
addresses. Documents similar to a query document can then be found by simply accessing
all the addresses that differ by only a few bits from the address of the query document. This
way of extending the efficiency of hash-coding to approximate matching is much faster
than locality sensitive hashing, which is the fastest current method. By using semantic
hashing to filter the documents given to TF-IDF, we achieve higher accuracy than applying
TF-IDF to the entire document set.

Indexing is implemented in the following manner: a document is mapped to a word-count vector and then this vector is passed through a [#Restricted Boltzmann Machine](/tag/restricted_boltzmann_machine) autoencoder and encoded to 32-bit address.

2017-11-07 A propos

Distributed Word Representations for Information Retrieval

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2017-10-01 A propos

Vectorland: Brief Notes from Using Text Embeddings for Search

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2017-09-18 A propos

Using Text Embeddings for Information Retrieval

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2017-09-18 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

TextRank: Bringing Order into Texts (2004)

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2017-06-14 A propos

word2vec-api

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2017-06-09 A propos

Introduction to Information Retrieval, Cambridge University Press (2008) Manning, Raghavan, and Schütze

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2012-04-01 A propos