Semanlink - Favoris ; N-grams AND Semantic technology

Searching with Shingles | Elastic Blog

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2020-09-03 About

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (2018)

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A La Carte Embedding

2018-09-18 About

Simple and efficient semantic embeddings for rare words, n-grams, and language features – Off the convex path

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2018-09-18 About

Finding Similar Items

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**Jaccard similarity**: similarity of sets, based on the relative size of their intersection -> **finding textually similar documents in a large corpus, near duplicates**. [Collaborative Filtering](/tag/collaborative_filtering) as a Similar-Sets Problem (cf. online purchases, movie ratings)

**Shingling** turns the problem of textual similarity of documents into a pb of similarity of sets

k-shingle: substring of length k found within a document. k: 5 for emails. Hashing shingles. Shingles built from words (stop word + 2 following words)

Similarity-Preserving Summaries of Sets: shingles sets are large -> compress large sets into small representations (“signatures”) that preserve similarity: **[Minhashing](/tag/minhash)** - related to Jaccard similarity (good explanation in [wikipedia](https://en.wikipedia.org/wiki/MinHash))

It still may be impossible to find the pairs of docs with greatest similarity efficiently -> **[Locality-Sensitive Hashing](/tag/locality_sensitive_hashing)** for Documents

Distance measures

Theory of Locality-Sensitive Functions

LSH famiies for other distance measures

Applications of Locality-Sensitive Hashing:

- entity resolution
- matching fingerprints
- matching newpapers articles

Methods for High Degrees of Similarity: LSH-based methods most effective when the degree of similarity we
accept is relatively low. When we want to find sets that are almost identical, other methods can be faster.

2017-07-26 About

An empirical study of smoothing techniques for language modeling

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2012-03-25 About

Google Ngram Viewer

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2012-03-24 About

All Our N-gram are Belong to You

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2012-03-24 About