In this paper, we present a lexical resource where WordNet synsets are annotated with Subject Field Codes. We discuss both the methodological issues we dealt with and the annotation techniques used. A quantitative analysis of the resource coverage, as well as a qualitative evaluation of the proposed annotations, are reported.

We report on the development and employment of lexical entry templates in a large–coverage unification–based grammar of Spanish. The aim of the work reported in this paper is to provide robust deep linguistic processing in order to make the grammar more adequate for industrial NLP applications.

Systems that automatically discover semantic classes have emerged in part to address the limitations of broad-coverage lexical resources such as WordNet and Cyc. The current state of the art discovers many semantic classes but fails to label their concepts. We propose an algorithm labeling semantic classes and for leveraging them to extract is-a relationships using a top-down approach.

Our paper describes an experiment aimed to assessment of lexical coverage in web corpora in comparison with the traditional ones for two closely related Slavic languages from the lexicographers’ perspective. The preliminary results show that web corpora should not be considered ―inferior‖, but rather ―different‖.

BACKGROUND Radiology reporting is a clinically oriented form of documentation that reflects critical information for patients about their health care processes. Realizing its importance, many medical institutions have started providing radiology reports in patient portals. The gain, however, can be limited because of medical language barriers, which require a way for customizing these reports f...

Lexical mismatch is a problem that confounds automatic question answering systems. While existing lexical ontologies such as WordNet have been successfully used to match verbal synonyms (e.g., beat and defeat) and common nouns (tennis is-a sport), their coverage of proper nouns is less extensive. Question answering depends substantially on processing named entities, and thus it would be of sign...

Deep processing of natural language requires large scale lexical resources that have sufficient coverage at a sufficient level of detail and accuracy (i.e. both recall and precision). Hand-crafted lexicons are extremely labour-intensive to create and maintain, and require continuous updating and extension to retain their level of usability. In this paper we present a technique for extending lex...

Linked open data (LOD) presents an ideal platform for connecting the multilingual lexical resources used in natural language processing (NLP) tasks, but the use of machine translation to fill in gaps in lexical coverage for resource-poor languages means that large amounts of data are potentially unverified. For graph-based word sense disambiguation (WSD), one approach has been to first translat...

This paper describes our work in integrating three different lexical resources: FrameNet, VerbNet, and WordNet, into a unified, richer knowledge-base, to the end of enabling more robust semantic parsing. The construction of each of these lexical resources has required many years of laborious human effort, and they all have their strengths and shortcomings. By linking them together, we build an ...

Inductive and deductive inferences in a Crowdsourced Lexical-Semantic Network In Computational Linguistics, validated lexical-semantic networks are crucial resources. Regardless the construction strategies used, automatically inferring new relations from already existing ones may improve coverage and global quality of the resource. In this context, an inference engine aims at producing new conc...