237 research outputs found
An Annotated Dataset for Extracting Definitions and Hypernyms from the Web
No full textThis paper presents and analyzes an annotated corpus of definitions, created to train an algorithm for the automatic extraction of definitions and hypernyms fromWeb documents. As an additional resource, we also include a corpus of non-definitions with syntactic patterns similar to those of definition sentences, e.g.: "An android is a robot" vs. "Snowcap is unmistakable". Domain and style independence is obtained thanks to the annotation of a sample of the Wikipedia corpus and to a novel pattern generalization algorithm based on word-class lattices (WCL). A lattice is a directed acyclic graph (DAG), a subclass of nondeterministic finite state automata (NFA). The lattice structure has the purpose of preserving the salient differences among distinct sequences, while eliminating redundant information. The WCL algorithm will be integrated into an improved version of the GlossExtractor Web application (Velardi et al., 2008). This paper is mostly concerned with a description of the corpus, the annotation strategy, and a linguistic analysis of the data. A summary of the WCL algorithm is also provided for the sake of completeness
Efficient temporal mining of micro-blog texts and its application to event discovery
No full textIn this paper we present a novel method for clustering words in micro-blogs, based on the similarity of the related temporal series. Our technique, named SAX*, uses the Symbolic Aggregate ApproXimation algorithm to discretize the temporal series of terms into a small set of levels, leading to a string for each. We then define a subset of “interesting” strings, i.e. those representing patterns of collective attention. Sliding temporal windows are used to detect co-occurring clusters of tokens with the same or similar string. To assess the performance of the method we first tune the model parameters on a 2-month 1 % Twitter stream, during which a number of world-wide events of differing type and duration (sports, politics, disasters, health, and celebrities) occurred. Then, we evaluate the quality of all discovered events in a 1-year stream, “googling” with the most frequent cluster n-grams and manually assessing how many clusters correspond to published news in the same temporal slot. Finally, we perform a complexity evaluation and we compare SAX* with three alternative methods for event discovery. Our evaluation shows that SAX* is at least one order of magnitude less complex than other temporal and non-temporal approaches to micro-blog clustering. © 2015, The Author(s)
Semantic Integration “Axiomatizing WordNet: A Hybrid Methodology
No full textWorkshop Monday, October 20, 2003, Sundial Resort, Sanibel Island, Florida, USA, held in conjunction with The Second International Semantic Web Conference (ISWC 2003
Finding a domain-appropriate sense inventory for semantically tagging a corpus
No full textSemantically tagging a corpus is useful for many intermediate NLP tasks such as: acquisition
of word argument structures in sublanguages; acquisition of syntactic disambiguation cues;
terminology learning; etc. The general idea is that semantic tags allow the generalization of
observed word patterns, and facilitate the discovery of recurrent sublanguage phenomena and
selectional rules of various types. Yet, as opposed to POS tags in morphology, there is no
consensus in the literature about the type and granularity of the semantic tags to be used. In
this paper, we argue that an appropriate selection of semantic tags should be domain-dependent. We propose a method by which we select from WordNet an inventory of semantic
tags that are ‘optimal’ for a given corpus, according to a scoring
function defined as a linear
combination of general and corpus-dependent performance factors. We believe that an optimal
selection of a category inventory is a necessary premise for obtaining better results in all
lexically learning algorithms that are based on, or concerned with, semantic categorization of
words. Furthermore, an adequate inventory (one which intuitively ‘fits’ with the semantics of
a domain, e.g. phenomenon for Natural Science, or part, piece for a technical handbook) may
facilitate the manual annotation of large corpora.</jats:p
noisy-kgs-benchmark
No full textAuthors: Andrea Lenzi, Stefano Faralli, Paola Velardi
Affiliation: Computer Science Department, Sapienza University of Rome, Rome, Italy
Description: this is a repository for preserving, sharing, and maintaining a benchmark of Knowledge Graph Emabedding-based link prediction, deletion, and pruning systems with noisy data.</p
Ontology Enrichment Through Automatic Semantic Annotation of On-line Glossaries
No full textThe contribution of this paper is to provide a methodology for automatic ontology enrichment and for document annotation with the concepts and properties of a domain core ontology. Natural language definitions of available glossaries in a given domain are parsed and converted into formal (OWL) definitions, compliant with the core ontology property specifications.
To evaluate the methodology, we annotated and formalized a relevant fragment of the AAT glossary of art and architecture, using a subset of 10 properties defined in the CRM CIDOC cultural heritage core ontology, a recent W3C standard
Automatic adaptation of Proper Noun Dictionaries through cooperation of machine learning and probabilistic methods
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