Semanlink - Information retrieval: techniques AND Supervised machine learning

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Information retrieval: techniques AND Supervised machine learning

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24 Documents

k-Nearest Neighbor lookups on embeddings: kNN vs SVM

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2023-04-15 About

Building a sentence embedding index with fastText and BM25 | by David Mezzetti | Towards Data Science

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2021-09-30 About

[1901.04085] Passage Re-ranking with BERT

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2021-03-26 About

Rodrigo Nogueira sur Twitter : "Slides of our WSDM 2021 tutorial "Pretrained Transformers for Text Ranking: BERT and Beyond"

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2021-03-09 About

SLEEC: Sparse Local Embeddings for Extreme Multi-label Classification (2015)

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2020-08-11 About

A novel multi-label classification algorithm based on K-nearest neighbor and random walk - Zhen-Wu Wang, Si-Kai Wang, Ben-Ting Wan, William Wei Song, 2020

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2020-08-11 About

[1908.10084] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

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2019-08-28 About

A Short Introduction to Learning to Rank (2010)

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2019-04-18 About

Ranking Measures and Loss Functions in Learning to Rank (2009)

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2019-04-18 About

[1704.08803] Neural Ranking Models with Weak Supervision

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2019-01-27 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

Learning Deep Structured Semantic Models for Web Search using Clickthrough Data - Microsoft Research (2013)

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2017-12-30 About

DSSM ("Deep Semantic Similarity Model") - Microsoft Research

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DSSM (Deep Semantic Similarity Model)

2017-12-30 About

(fastText) Euclidean distance instead of cosine-similarity?

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2017-12-07 About

How to use pre-trained word vectors from Facebook’s fastText

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2017-11-25 About

Efficient unsupervised keywords extraction using graphs

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2017-10-04 About

What is a simple but detailed explanation of Textrank? - Quora

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TextRank

2017-07-12 About

How does Textrank work? (slides)

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TextRank

2017-07-12 About

gensim/FastText_Tutorial.ipynb

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2017-06-28 About

A Ranking Approach to Keyphrase Extraction - Microsoft Research (2009)

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2017-06-27 About

DivRank: the Interplay of Prestige and Diversity in Information Networks

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Learning to rank

2017-06-27 About

TextRank: Bringing Order into Texts (2004)

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2017-06-14 About

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

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2017-06-13 About

Text Summarization with Gensim

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2017-06-02 About