4,189 research outputs found
Learning Graph Embeddings from WordNet-based Similarity Measures
We present path2vec, a new approach for learning graph embeddings that relies
on structural measures of pairwise node similarities. The model learns
representations for nodes in a dense space that approximate a given
user-defined graph distance measure, such as e.g. the shortest path distance or
distance measures that take information beyond the graph structure into
account. Evaluation of the proposed model on semantic similarity and word sense
disambiguation tasks, using various WordNet-based similarity measures, show
that our approach yields competitive results, outperforming strong graph
embedding baselines. The model is computationally efficient, being orders of
magnitude faster than the direct computation of graph-based distances.Comment: Accepted to StarSem 201
Towards a Universal Wordnet by Learning from Combined Evidenc
Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
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