Semanlink - Dev ; Semanlink related AND NLP

> 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

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2017-07-10 About

Salmon Run: Implementing the RAKE Algorithm with NLTK

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2017-06-26 About

RAKE: A python implementation of the Rapid Automatic Keyword Extraction

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2017-06-26 About

Intro to Automatic Keyphrase Extraction

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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

NLP keyword extraction tutorial with RAKE and Maui

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2 tools:

- simple keyword extraction with a Python library (RAKE)
- Java tool (Maui) that uses a machine-learning technique.

Focus on 2 tasks:

- Extracting the most significant words and phrases that appear in given text
- Identifying a set of topics from a predefined vocabulary that match a given text

Typical steps:

- Candidate selection (extract all possible words, phrases, terms or concepts that can potentially be keywords).
- Properties calculation (for each candidate, properties that indicate that it may be a keyword)
- Scoring and selecting keywords

RAKE: finding multi-word phrases containing frequent words. +: simplicity, ease of use -: limited accuracy, parameter configuration requirement, throws away many valid phrases, doesn’t normalize candidates (no stemming).

Maui: ("Multi-purpose automatic topic indexing"). Based on [Weka](/semanlink/tag/weka) (GPL, java, maven, github). Compared to RAKE:

- Extract keywords not just from text, but also with a reference to a controlled vocabulary
- Improve the accuracy by training Maui on manually chosen keywords
    - but requires a training model.

Maui can use a controlled vocabulary expressed in SKOS - so I could use it in semanlink!

2017-05-24 About

Swayy | Blog — An algorithm for generating automatic hashtags

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2017-05-24 About

An Efficient Way to Extract the Main Topics from a Sentence | The Tokenizer

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