This paper presents a method for classifying and structuring free-text clinical trial eligibility criteria using the OMOP Common Data Model (CDM). Our method was applied to eligibility criteria text available from the largest clinical trial repository ClinicalTrials.gov. Structurally complex criteria were simplified and rewritten as simpler sentences connected by logical operators: AND or OR. S...

We present a data-driven approach for recognizing and classifying TimeML events in Italian. A high-performance stateof-the-art approach, TIPSem, is adopted and extended with Italian-specific semantic features from a lexical resource. The resulting approach has been evaluated over the official TempEval2 Italian test data. The analysis of the results shows a positive impact of the semantic featur...

The emerging field of semantic web technologies promises new stimulus for Software Engineering research. However, since the underlying concepts of the semantic web have a long tradition in the knowledge engineering field, it is sometimes hard for software engineers to overlook the variety of ontology-enabled approaches to Software Engineering. In this paper we therefore present some examples of...

The work we present here addresses cue-based noun classification in English and Spanish. Its main objective is to automatically acquire lexical semantic information by classifying nouns into previously known noun lexical classes. This is achieved by using particular aspects of linguistic contexts as cues that identify a specific lexical class. Here we concentrate on the task of identifying such...

This paper deals with classifying ambiguities for Multimodal Languages. It evolves the classifications and the methods of the literature on ambiguities for Natural Language and Visual Language, empirically defining an original classification of ambiguities for multimodal interaction using a linguistic perspective. This classification distinguishes between Semantic and Syntactic multimodal ambig...

Multiple emotions are often triggered in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using an ensemble based multi-label classification technique called RAKEL. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadn...

The constituents of English compounds (e.g., butter and fly for butterfly) and two-character Chinese words may differ in meaning from the whole word. Furthermore, the meanings of the words containing the same constituent (e.g., butter in “butterfingers”, or “buttermilk”) may or may not be consistent. Estimating semantic transparency of a constituent is usually difficult and subjective because o...

Approaching temporal link labelling as a classification task has already been explored in several works. However, choosing the right feature vectors to build the classification model is still an open issue, especially for event-event classification, whose accuracy is still under 50%. We find that using a simple feature set results in a better performance than using more sophisticated features b...

This paper addresses the problem of classifying Chinese unknown words into fine-grained semantic categories defined in a Chinese thesaurus. We describe three novel knowledge-based models that capture the relationship between the semantic categories of an unknown word and those of its component characters in three different ways. We then combine two of the knowledge-based models with a corpus-ba...

This paper presents a semi-latent topic model for semantic domain detection in spoken language understanding systems. We use labeled utterance information to capture latent topics, which directly correspond to semantic domains. Additionally, we introduce an ’informative prior’ for Bayesian inference that can simultaneously segment utterances of known domains into classes and divide them from ou...