Semanlink - Personal-information management AND NLP

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Arxiv Doc - Chatbots - Classification: label correlation - Demo - Frequently cited paper - GitHub project - Good - k-nearest neighbors - Multi-label classification - NLP 4 Semanlink - Personal Knowledge Base - Personal Knowledge Graph - Personal Knowledge Management - Raphaël Sourty - Semanlink related - Tweet -

Personal-information management AND NLP

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7 Documents

raphaelsrty sur X : "My personal Knowledge Base made to the front page of HackerNews today..."

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2024-03-23 About

raphaelsty.github.io/knowledge demo

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2023-03-15 About

Linus sur Twitter : "I built a personal chatbot from my personal corpus a couple weeks ago on fully open-source LMs... it made a huge difference in how it feels to interact. Much more natural...

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2023-02-25 About

FP Servant sur Twitter : "constructing a personal knowledge graph as a support for learning (and a metaphor of the learning experience)..."

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2020-12-03 About

raphaelsty/ckb: Contextual knowledge bases

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2020-11-09 About

[1905.10070] Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

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2019-06-22 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About