Semanlink - Favoris ; Unsupervised machine learning AND Clustering

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Favoris ; Unsupervised machine learning AND Clustering

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40 Documents

LlamaIndex 🦙 sur X : "An emerging technique to better represent your data for RAG/LLM applications is to only chunk the data, but also hierarchically cluster and index it..."

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2024-03-30 About

huggingface/text-clustering: Easily embed, cluster and semantically label text datasets

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2024-03-07 About

On the Surprising Behavior of Distance Metrics in High Dimensional Space (Aggarwal 2001)

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2024-03-03 About

[2210.16637] Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering Representations

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2022-11-25 About

Nils Reimers sur Twitter : "Creating intent classes for chatbots is challenging This tutorial shows how to use sentence-transformers to find potentially overlapping intent classes and how to improve your data annotation work." / Twitter

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2022-02-19 About

Nils Reimers sur Twitter : "how to use the fast clustering algorithm from sentence-transformers..."

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2022-02-19 About

Semantic Keyword Clustering For 10,000+ Keywords [With Script]

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2022-02-18 About

sentence-transformers/fast_clustering.py at master · UKPLab/sentence-transformers

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2022-02-18 About

Application of Self-Organizing Maps in Text Clustering: A Review | IntechOpen (2012)

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2021-10-17 About

Improving Quality of Search Results Clustering with Approximate Matrix Factorisations (2006)

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Lingo

2021-06-02 About

Carrot2 search results clustering engine (online)

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Carrot2

2021-05-27 About

Clustering of semantically enriched short texts (2018)

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2021-05-26 About

Integrating Document Clustering and Multidocument Summarization

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2021-05-25 About

[2001.11631] Enhancement of Short Text Clustering by Iterative Classification

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2021-05-20 About

[2103.12953] Supporting Clustering with Contrastive Learning

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2021-05-20 About

A Self-Training Approach for Short Text Clustering - (Hadifar 2019)

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2021-05-20 About

Simultaneous Categorization of Text Documents And Identification of Cluster-dependent Keywords (2003)

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Clustering of text documents

2021-04-20 About

A Survey of Text Clustering Algorithms - C. C. Aggarwal (2012)

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2021-04-20 About

[2010.00402] From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering

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2020-10-03 About

[1707.00306] Variable Selection Methods for Model-based Clustering

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2019-12-11 About

Clustering of semantically enriched short texts (2019)

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2019-11-15 About

[Jaeyoung2018] Patent Document Clustering with Deep Embeddings

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2019-02-23 About

[1511.06335] Unsupervised Deep Embedding for Clustering Analysis

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2019-02-19 About

Modifications for the Cluster Content Discovery and the Cluster Label Induction Phases of the Lingo Algorithm (2014)

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Lingo

2017-11-11 About

[1602.06797] Semi-supervised Clustering for Short Text via Deep Representation Learning

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2017-11-07 About

Semantic Enriched Short Text Clustering | SpringerLink

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Short Text Clustering

2017-11-07 About

[1701.00185] Self-Taught Convolutional Neural Networks for Short Text Clustering

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2017-11-04 About

Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition (slides)

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2017-07-11 About

Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition (2004) (paper)

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2017-07-11 About

Hierarchical clustering in Python and beyond

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2017-07-11 About

Document Clustering with Python

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2017-06-28 About

Document Representation and Dimension Reduction for Text Clustering

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2017-06-24 About

Lingo3G or Carrot2? | Carrot Search

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Carrot2

2017-06-04 About

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Carrot2

2017-05-23 About

Carrot2: Text Clustering Algorithms and Applications

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2017-05-23 About

Result Clustering - Apache Solr Reference Guide - Apache Software Foundation

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2017-05-23 About

Lingo3G: real-time text clustering engine | Carrot Search

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2017-05-22 About

What are the best open source tools for unsupervised clustering of text documents? - Quora

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Clustering of text documents

2017-05-22 About

Quick review on Text Clustering and Text Similarity Approaches

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Author: Maali Mnasri (PhD @ CEA)

First transform text units to vectors? not always (eg. sentence similarity task using lexical word alignment). But vectors are efficient to process, and benefit from existing clustering algorithms such as k-means.

Sentence level or document level? Sentence clustering to summarise large documents.

Thematic clustering vs Semantic clustering: depends on the similarity measure.

Text similarity measures:

- Cosine similarity of tf-idf (suitable to produce thematic clusters)
- Knowledge-based Measures (wordNet) (quantify semantic relatedness of words),
- Word embedings

Examples, sample code:

- using wordnet with NLTK, and the formula to compute sentence similarities from word similarities.
- computing similarities between docs using gensim/word2vec

Which clustering algorithm?

- when we have an approximation of the clusters number, and when the similarity measure is not expensive in terms of computation time, clustering algo are suitable and fast. Sample code of k-means clustering using tf-idf vectors with scikit-learn
- Hierarchical clustering algorithms
    - don't need to give the number of clusters
    - but time consuming (calculate a similarity matrix for the sentences) 
- for voluminous data, use an incremental clustering algorithm: sentences are processed one at a time ; each new sentence is compared to each of the already formed clusters.

2017-05-18 About

Sapping Attention: When you have a MALLET, everything looks like a nail

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2014-04-25 About