Abstract. In this paper we propose an approach for automatic construction of concept hier-archies from the snippets returned by Internet search engines using a number of well known techniques. We use surface lexical patterns to construct a set of candidate hypernyms of a given term and additional filtering that is based on both lexical patterns and distributional analysis. Preliminary experimental results for real life English examples are presented
In this paper we present a graph-based approach aimed at learning a lexical taxonomy automatically s...
Comunicació presentada a la 16th International Conference, (CICLing) celebrada del 14 al 20 d'abril...
HypoTerm is a data-driven semantic relation finder that starts from a list of automatically extracte...
University of Minnesota M.S. thesis.July 2018. Major: Computer Science. Advisor: Ted Pedersen. 1 com...
Hypernym discovery aims to extract such noun pairs that one noun is a hypernym of the other. Most pr...
© 2020 ABBYY PRODUCTION LLC. All rights reserved. This paper describes a combined approach to hypern...
International audienceGiven a set of terms from a given domain, how can we structure them into a tax...
Automated extraction of ontological knowledge from text corpora is a relevant task in Natural Langua...
This paper addresses the problem of categorizing terms or lexical entities into a predefined set of ...
In this paper, we show for the first time how distributionally-induced semantic classes can be helpf...
Hypernymy is a basic semantic relation in computational linguistics that expresses the “is-a” relati...
Lexical resources are machine-readable dictionaries or lists of words, where semantic relationships ...
Identifying semantic relations in natural language text is an important component of many knowledge ...
In this paper, we describe a weakly supervised boot-straping algorithm that reads Web texts and lear...
The task of finding hypernyms from large text corpora is a fundamental problem in NLP. It provides a...
In this paper we present a graph-based approach aimed at learning a lexical taxonomy automatically s...
Comunicació presentada a la 16th International Conference, (CICLing) celebrada del 14 al 20 d'abril...
HypoTerm is a data-driven semantic relation finder that starts from a list of automatically extracte...
University of Minnesota M.S. thesis.July 2018. Major: Computer Science. Advisor: Ted Pedersen. 1 com...
Hypernym discovery aims to extract such noun pairs that one noun is a hypernym of the other. Most pr...
© 2020 ABBYY PRODUCTION LLC. All rights reserved. This paper describes a combined approach to hypern...
International audienceGiven a set of terms from a given domain, how can we structure them into a tax...
Automated extraction of ontological knowledge from text corpora is a relevant task in Natural Langua...
This paper addresses the problem of categorizing terms or lexical entities into a predefined set of ...
In this paper, we show for the first time how distributionally-induced semantic classes can be helpf...
Hypernymy is a basic semantic relation in computational linguistics that expresses the “is-a” relati...
Lexical resources are machine-readable dictionaries or lists of words, where semantic relationships ...
Identifying semantic relations in natural language text is an important component of many knowledge ...
In this paper, we describe a weakly supervised boot-straping algorithm that reads Web texts and lear...
The task of finding hypernyms from large text corpora is a fundamental problem in NLP. It provides a...
In this paper we present a graph-based approach aimed at learning a lexical taxonomy automatically s...
Comunicació presentada a la 16th International Conference, (CICLing) celebrada del 14 al 20 d'abril...
HypoTerm is a data-driven semantic relation finder that starts from a list of automatically extracte...