Semanlink - Classification AND Semanlink related

> 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

RESEARCH TRACK: Web Content Analysis, Semantics and Knowledge

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[CFP](https://www2018.thewebconf.org/call-for-papers/research-tracks-cfp/web-content-analysis/)

> In previous years, ‘content analysis’ and ‘semantic and knowledge’ were in separate track. This year, we combined these tracks to emphasize the close relationship between these topics; **the use of content to curate knowledge and the use of knowledge to guide content analysis and intelligent usage**.

Some of the accepted papers:
### [Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN](https://doi.org/10.1145/3178876.3186005)

[Hierarchical Text Classification](/tag/nlp_hierarchical_text_classification): Text classification to a hierarchical taxonomy of topics, using graph representation of text, and CNN over this graph

Renvoie à ce qui a été vu dans le tutorial "Graph-based Text Representations"

from the abstract:

> a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-of-words representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels

Conversion of text to graph: potentially given a single document

### [Weakly-supervised Relation Extraction by Pattern-enhanced Embedding Learning](https://doi.org/10.1145/3178876.3186024 )

Extraction de relations de corpus de textes de façon semi-supervisée, dans un contexte où on a peu de données labellisées décrivant les relations.

Par exemple, des données labellisées indique que le texte "Beijing, capital of China" correspond à la relation entre entités : ("Beijing", "Capital Of", "China), et on voudrait pouvoir extraire les entités et relations pertinentes à partir de texte tel que "Paris, France's capital,..."

Le papier décrit une méthode qui combine deux modules, l'un basé sur l'extraction automatique de patterns (par ex "[Head], Capital Of [Tail]") et l'autre sur la "sémantique distributionnelle" (du type "word embeddings"). Ces deux modules collaborent, le premier permettant de créer des instances de relations augmentant la base de connaissance sur lequel entrainer le second, et le second aidant le premier à déterminer des patterns informatifs ("co-entrainement")

### [Scalable Instance Reconstruction in Knowledge Bases via Relatedness Affiliated Embedding](https://doi.org/10.1145/3178876.3186017)

Knowledge base completion problem: usually, it is formulated as a link prediction problem, but not here. A novel knowledge embedding model ("Joint Modelling and Learning of Relatedness and Embedding")

### [Improving Word Embedding Compositionality using Lexicographic Definitions](https://doi.org/10.1145/3178876.3186007)

comment obtenir les meilleures représentations de texte à partir de représentations de mots (word embeddings) ? L'auteur utilise des ressources lexicographiques (wordnet) pour ses tests : l'embedding obtenu pour la définition d'un mot est-il proche de celui du mot ?

Le papier s'appuie sur une [thèse du même auteur](https://esc.fnwi.uva.nl/thesis/centraal/files/f1554608041.pdf), claire et bien écrite.

### [CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information](https://doi.org/10.1145/3178876.3186030)

Amélioration de l'extraction de triplets (nom phrase, property, nom phrase) à partir de texte en calculant des embeddings pour les "nom phrases" (~entités)

### [Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations](https://doi.org/10.1145/3178876.3186009)

Topic modeling for short texts, leveraging the word-context semantic correlations in the training

### [Towards Annotating Relational Data on the Web with Language Models](https://doi.org/10.1145/3178876.3186029)

### A paper by [David Blei](/tag/david_blei): (Dynamic Embeddings for Language Evolution)

2018-01-27 About

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

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2017-06-27 About

Hierarchical classification: Combining Bayes with SVM

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2014-05-18 About

nltk - hierarchical classification + topic model training data for internet articles and social media - Stack Overflow

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2014-04-23 About