Semanlink - Favoris ; Machine learning AND Embedding evaluation

> 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

Towards a Seamless Integration of Word Senses into Downstream NLP Applications (2017)

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2017-10-21 About

An overview of word embeddings and their connection to distributional semantic models - AYLIEN (2016)

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2017-07-20 About

Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014)

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2017-05-18 About