Semanlink - Information retrieval ; Dev AND Semanlink related

librAIry

Tags:

2021-06-22 About

Tutorial on Poincaré Embeddings (Jupyter Notebook )

Tags:

2018-05-20 About

Implementing Poincaré Embeddings | RARE Technologies

Tags:

2018-05-20 About

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

Tags:

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

gensim: models.phrases – Phrase (collocation) detection

Tags:

2017-07-10 About

Automated Keyword Extraction – TF-IDF, RAKE, and TextRank (Less Than Dot - Blog)

Tags:

2017-06-13 About

Text Summarization with Gensim

Tags:

2017-06-02 About

Thesaurus-augmented Search with Jena Text | ZBW Labs

Tags:

2013-08-12 About

hackdiary: Using Wikipedia and the Yahoo API to give structure to flat lists

Tags:

2005-09-05 About