Semanlink - Tagging AND Favoris

> 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

Extreme Multi-label Learning with Label Features for Warm-start Tagging, Ranking & Recommendation (2018)

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2018-03-04 About

Efficient unsupervised keywords extraction using graphs

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2017-10-04 About

Enriching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems - Microsoft Research (2015)

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> new simple, yet effective approaches to
learn domain specific word embeddings.

## Intro

> Adapting word embeddings, such as jointly capturing
syntactic and semantic information, can further enrich semantic
word representations for several tasks, e.g., sentiment
analysis (Tang et al. 2014), named entity recognition
(Lebret, Legrand, and Collobert 2013), entity-relation extraction
(Weston et al. 2013), etc. (Yu and Dredze 2014)
has introduced a lightly supervised word embedding learning
extending word2vec. They incorporate prior information to the objective
function as a regularization term considering synonymy relations
between words from Wordnet (Fellbaum 1999).

> In this work, we go one step further and investigate if
enriching the word2vec word embeddings trained on unstructured/
unlabeled text with domain specific semantic relations
obtained from knowledge sources (e.g., knowledge
graphs, search query logs, etc.) can help to discover relation
aware word embeddings. Unlike earlier work, **we encode the
information about the relations between phrases, thereby,
entities and relation mentions are all embedded into a low dimensional
vector space**.

## Related work (Learning Word Embeddings with Priors)

- word2vec
- Relational Constrained Model (RTM) (Yu and Dredze 2014)
While CBOW learns lexical word embeddings from provided text, the RTM learns embeddings of words based on their similarity to other words provided by a knowledge resource (eg. wordnet)
- Joint model  (Yu and Dredze 2014)
combines CBOW and RTM through linear combination

2017-10-02 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: models.phrases – Phrase (collocation) detection

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2017-07-10 About

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

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2017-06-27 About

Salmon Run: Implementing the RAKE Algorithm with NLTK

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2017-06-26 About

Kea (Keyphrase Extraction Algorithm)

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Keyword/keyphrase extraction

2017-06-26 About

RAKE: A python implementation of the Rapid Automatic Keyword Extraction

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2017-06-26 About

Automatic Keyphrase Extraction (Poster): A Survey of the State of the Art (2014)

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Keyword/keyphrase extraction

2017-06-20 About

Automatic Keyphrase Extraction: A Survey of the State of the Art (2014)

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Keyword/keyphrase extraction

2017-06-20 About

TextRank: Bringing Order into Texts (2004)

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2017-06-14 About

Conundrums in Unsupervised Keyphrase Extraction: Making Sense of the State-of-the-Art (2010)

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Keyword/keyphrase extraction

2017-06-14 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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