LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories
We explore the capabilities of a bidirectional LSTM model to learn representations of word senses from semantically annotated corpora. We show that the utilization of an architecture that is aware of word order, like an LSTM, enables us to create better representations. We assess our proposed model on various standard benchmarks for evaluating semantic representations, reaching state-of-the-art performance on the SemEval-2014 word-to-sense similarity task.
[site] [paper] [presentation]
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
We propose a new model that jointly learns word and sense embeddings and represents them in a unified vector space by exploiting large corpora and knowledge obtained from semantic networks. We evaluate the main features of our approach qualitatively and quantitatively in various tasks, highlighting the advantages of the proposed method with respect to state-of-the-art word- and sense-based models.
[site] [source] [paper] [poster]
[Tutorial] Semantic Representations of Word Senses and Concepts
This tutorial will first provide a brief overview of the recent literature concerning word representation (both count based and neural network based). It will then describe the advantages of moving from the word level to the deeper level of word senses and concepts, providing an extensive review of state of the art systems. Approaches covered will not only include those which draw upon knowledge resources such as WordNet, Wikipedia, BabelNet or FreeBase as reference, but also the so called multi prototype approaches which learn sense distinctions by using different clustering techniques. Our tutorial will discuss the advantages and potential limitations of all approaches, showing their most successful applications to date. We will conclude by presenting current open problems and lines of future work.
Embeddings for Word Sense Disambiguation: An Evaluation Study
We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance.
[source] [paper] [bib] [poster]
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity
We propose a multi-faceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement.
[site] [paper] [bib] [presentation] [video] [vectors]
Negated Findings Detection in Radiology Reports in Spanish: an Adaptation of NegEx to Spanish
We present SpRadNeg, an adaptation of NegEx to the Spanish language. NegEx is an English rule-based negation detection algorithm. We have tested SpRadNeg with radiology reports, obtaining a precision of 0.87 and a recall of 0.49. We also propose a method to automatize text annotation based on Machine Learning techniques with 0.91 precision and 0.89 recall.