Semanlink - NLP girls and guys ; NLP tasks / problems AND Chris Manning

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NLP girls and guys ; NLP tasks / problems AND Chris Manning

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11 Documents

[2401.18059] RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval

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2024-02-03 About

Christopher Manning sur Twitter : "As the abilities of large pre-trained language models continue to rapidly improve, as seen in this week’s ChatGPT, I find it a rather implausible position to think that these models have no understanding of the meaning of texts."

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2022-12-03 About

Christopher Manning sur Twitter : "This seems like an important contribution to the external validity of the (big) recent line of work on long-context transformer models"

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2022-07-18 About

[SAIF2020] Day2: Natural Language Processing - Christopher Manning | Samsung - YouTube

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2022-06-15 About

AK sur Twitter : "You Only Need One Model for Open-domain Question Answering..."

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2021-12-23 About

[1907.04829] BAM! Born-Again Multi-Task Networks for Natural Language Understanding

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2020-05-12 About

Baselines and Bigrams: Simple, Good Sentiment and Topic Classification. Sida Wang and Christopher D. Manning

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2019-09-03 About

Effect of Non-linear Deep Architecture in Sequence Labeling

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2018-10-22 About

Representations for Language: From Word Embeddings to Sentence Meanings (2017) - Slides

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2018-08-28 About

Representations for Language: From Word Embeddings to Sentence Meanings (2017) - YouTube

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2018-05-29 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