Alfio Gliozzo

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This paper summarizes FBK-irst participation at the lexical substitution task of the SEMEVAL competition. We submitted two different systems, both exploiting synonym lists extracted from dictionaries. For each word to be substituted, the systems rank the associated synonym list according to a similarity metric based on Latent Semantic Analysis and to the(More)
Domain information has been regarded as an emerging topic of interest in relation to WordNet. A lexical resource, WordNet Domains, is presented, where WordNet synsets have been annotated with domain labels such as Medicine, Architecture and Sport. This annotation reflects the lexicosemantic criteria adopted by humans involved in the annotation. However,(More)
This paper investigates the utility of an unsupervised partof-speech (PoS) system in a task oriented way. We use PoS labels as features for different supervised NLP tasks: Word Sense Disambiguation, Named Entity Recognition and Chunking. Further we explore, how much supervised tagging can gain from unsupervised tagging. A comparative evaluation between(More)
This paper describes a seminar course designed by IBM and Columbia University on the topic of Semantic Technologies, in particular as used in IBM WatsonTM — a large scale Question Answering system which famously won at Jeopardy! R © against two human grand champions. It was first offered at Columbia University during the 2013 spring semester, and will be(More)
We introduce an interactive visualization component for the JoBimText project. JoBimText is an open source platform for large-scale distributional semantics based on graph representations. First we describe the underlying technology for computing a distributional thesaurus on words using bipartite graphs of words and context features, and contextualizing(More)
This paper describes a seminar course designed by IBM and Columbia University on the topic of Semantic Technologies, in particular as used in IBM WatsonTM — a large scale Question Answering system which famously won at Jeopardy! R © against two human grand champions. It was first offered at Columbia University during the 2013 spring semester, and will be(More)
This paper is about the relations between the concept of Semantic Domain and the “Theory of Semantic Fields”, a structural model for lexical semantics proposed by Jost Trier at the beginning of the last century. The main limitation of the Trier’s notion is that it does not provide an objective criterion to aggregate words around fields, making the overall(More)
Finding quality descriptions on the web, such as those found in Wikipedia articles, of newer companies can be difficult: search engines show many pages with varying relevance, while multi-document summarization algorithms find it difficult to distinguish between core facts and other information such as news stories. In this paper, we propose an(More)
Knowledge Graphs (KGs) play a key role in many artificial intelligence applications. Large KGs are often constructed through a noisy automatic knowledge extraction process. Noise detection is, therefore, an important task for having high-quality KGs. We argue that the current noise detection approaches only focus on a specific type of noise (i.e., fact(More)
Traditional information retrieval models assume keyword-based queries and use unstructured document representations. There is an abundance of event-centered texts (e.g., breaking news) and event-oriented information needs that often involve structure that cannot be expressed using keywords. We present a novel retrieval model that uses a structured(More)