Hypernymy relations (those where an hyponym term shares a "isa" relationship with his hypernym) play a key role for many Natural Language Processing (NLP) tasks, e.g. ontology learning, automatically building or extending knowledge bases, or word sense disambiguation and induction. In fact, such relations may provide the basis for the construction of more complex structures such as taxonomies, or be used as effective background knowledge for many word understanding applications. We present a publicly available database containing more than 400 million hypernymy relations we extracted from the CommonCrawl web corpus. We describe the infrastructure we developed to iterate over the web corpus for extracting the hypernymy relations and store th...
University of Minnesota M.S. thesis.July 2018. Major: Computer Science. Advisor: Ted Pedersen. 1 com...
Relational phrases (e.g., “got married to”) and their hypernyms (e.g., “is a relative of”) are centr...
In this paper, we show how unsupervised sense representations can be used to improve hypernymy extra...
WebIsADb is a publicly available database containing more than 400 million hypernymy relations we ex...
Many of the tasks in computational linguistics, such as information retrieval, document classificati...
The list of hyponym-hypernym pairs was obtained by applying lexical-syntactic patterns described in ...
Lexical resources are machine-readable dictionaries or lists of words, where semantic relationships ...
HypoTerm is a data-driven semantic relation finder that starts from a list of automatically extracte...
Recent developments in computational terminology call for the design of multiple and complementary t...
The task of finding hypernyms from large text corpora is a fundamental problem in NLP. It provides a...
International audienceExtracting hypernym relations from text is one of the key steps in the constru...
Identifying semantic relations in natural language text is an important component of many knowledge ...
International audienceExtracting hypernym relations from text is one of the key steps in the automat...
Hypernymy is a basic semantic relation in computational linguistics that expresses the “is-a” relati...
International audienceExtracting hypernym relations from text is one of the key steps in the constru...
University of Minnesota M.S. thesis.July 2018. Major: Computer Science. Advisor: Ted Pedersen. 1 com...
Relational phrases (e.g., “got married to”) and their hypernyms (e.g., “is a relative of”) are centr...
In this paper, we show how unsupervised sense representations can be used to improve hypernymy extra...
WebIsADb is a publicly available database containing more than 400 million hypernymy relations we ex...
Many of the tasks in computational linguistics, such as information retrieval, document classificati...
The list of hyponym-hypernym pairs was obtained by applying lexical-syntactic patterns described in ...
Lexical resources are machine-readable dictionaries or lists of words, where semantic relationships ...
HypoTerm is a data-driven semantic relation finder that starts from a list of automatically extracte...
Recent developments in computational terminology call for the design of multiple and complementary t...
The task of finding hypernyms from large text corpora is a fundamental problem in NLP. It provides a...
International audienceExtracting hypernym relations from text is one of the key steps in the constru...
Identifying semantic relations in natural language text is an important component of many knowledge ...
International audienceExtracting hypernym relations from text is one of the key steps in the automat...
Hypernymy is a basic semantic relation in computational linguistics that expresses the “is-a” relati...
International audienceExtracting hypernym relations from text is one of the key steps in the constru...
University of Minnesota M.S. thesis.July 2018. Major: Computer Science. Advisor: Ted Pedersen. 1 com...
Relational phrases (e.g., “got married to”) and their hypernyms (e.g., “is a relative of”) are centr...
In this paper, we show how unsupervised sense representations can be used to improve hypernymy extra...