Semanlink - Data mining ; Artificial Intelligence AND J'y étais

> Here it is shown that multiple word senses reside
in linear superposition within the word
embedding and simple sparse coding can recover
vectors that approximately capture the
senses

> Each extracted word sense is accompanied by one of about  2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense.

> The success of the approach is mathematically explained using a variant of
the random walk on discourses model

("random walk": a generative model for language). Under the assumptions of this model,  there
exists a linear relationship between the vector of a
word w and the vectors of the words in its contexts (It is not the average of the words in w's context, but in a given corpus the matrix of the linear relationship does not depend on w. It can be estimated, and so we can compute the embedding of a word from the contexts it belongs to)

[Related blog post](/doc/?uri=https%3A%2F%2Fwww.offconvex.org%2F2016%2F07%2F10%2Fembeddingspolysemy%2F)

2018-08-28 About

Module google/universal-sentence-encoder | TensorFlow

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2018-05-23 About

Improving Word Embedding Compositionality using Lexicographic Definitions

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2018-05-10 About

That Makes Sense: Joint Sense Retrofitting from Contextual and Ontological Information

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post-processing method for generating low-dimensional sense embedding. Emploies the ontological and contextual information simultaneously.

(Poster at the Web Conf) [Github](https://github.com/y95847frank/Joint-Retrofitting)

Calcule des "sense embeddings", en partant de word embeddings pré-calculés (par ex avec word2vec), et de données de type lexicographiques (ex wordnet), en contraignant, pour un sens, la distance entre sense et word embedding.

Abstract:

> While recent word embedding models demonstrate their abilities to capture syntactic and semantic information, the demand for sense level embedding is getting higher. In this study, we propose a novel joint sense embedding learning model that retrofits the word representation into sense representation from contextual and ontological information. The experiments show the effectiveness and robustness of our model that outperforms previous approaches in four public available benchmark datasets.

> Given a trained word embedding and a lexical ontology that contains sense level relationships (e.g., synonym, hypernym, etc.), our model generates new sense vectors via constraining the distance between the sense vector and its word form vector, its sense neighbors and its contextual neighbors

[Influenced by](/doc/?uri=https%3A%2F%2Farxiv.org%2Fabs%2F1411.4166) (which post-processes and modifies word vectors to incorporate knowledge from semantic lexicons, while this creates new sense vectors)

2018-05-10 About

Improving the Compositionality of Word Embeddings (2017)

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(MS thesis, a [paper at TheWebConf 2018](/doc/?uri=https%3A%2F%2Fdoi.org%2F10.1145%2F3178876.3186007))

> This thesis explores a method to find better encodings of meaning a computer can work with. We specifically want to combine encodings of word meanings in such a way that a good encoding of their joint meaning is created. The act of combining multiple representations of meaning into a new representation of meaning is called semantic composition.

Analysis of four word embeddings (Word2Vec, GloVe, fastText and Paragram)  in terms of their semantic compositionality. A method to tune these embeddings towards better compositionality, using a simple neural network architecture with definitions and lemmas from WordNet.

> Since dictionary definitions are semantically similar to their associated lemmas, they are the ideal candidate for our tuning method, as well as evaluating for compositionality. Our architecture allows for the embeddings to be composed using simple arithmetic operations, which makes these embeddings specifically suitable for production applications such as web search and data mining. We also explore more elaborate and involved compositional models, such as recurrent composition and convolutional composition.

2018-02-13 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