This paper proposes a framework for automatic recognition of domain-specific entities from text, given limited background knowledge, e.g. in form of an ontology. The algorithm exploits several lightweight natural language processing techniques, such as tokenization and stemming, as well as statistical techniques, such as singular value decomposition (SVD) to suggest domain relatedness of unknown entities
One major problem in text mining and semantic retrieval is that detected entity mentions have to be ...
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Infor...
This paper presents an overview of automatic methods for building domain knowledge structures (domai...
This paper proposes a framework for automatic recognition of domain-specific entities from text, giv...
International audienceWe present a system for entity identification in free text. Each entity extrac...
With domain ontology, a meaningful index of document indexing, such as the domain events structure i...
This paper introduces Named Entity Recognition approach for text corpus. Supervised Statistical meth...
The intensive construction of domain-specific knowledge bases (DSKB) has posed an urgent demand for ...
We investigate a largely unsupervised approach to learning interpretable, domain-specific entity typ...
Though the utility of domain Ontologies is now widely acknowledged in the IT (Information Technol...
We propose a novel Named Entity Recognition (NER) system based on a machine learning technique and a...
We propose a novel Named Entity Recognition (NER) system based on a machine learning technique and a...
Automatically building domain-specific ontologies is a highly challenging task as it requires extrac...
Abstract. Text mining has an important role to play in aiding experts to construct domain specific o...
Predicting which entities are likely to be mentioned in scientific articles is a task with significa...
One major problem in text mining and semantic retrieval is that detected entity mentions have to be ...
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Infor...
This paper presents an overview of automatic methods for building domain knowledge structures (domai...
This paper proposes a framework for automatic recognition of domain-specific entities from text, giv...
International audienceWe present a system for entity identification in free text. Each entity extrac...
With domain ontology, a meaningful index of document indexing, such as the domain events structure i...
This paper introduces Named Entity Recognition approach for text corpus. Supervised Statistical meth...
The intensive construction of domain-specific knowledge bases (DSKB) has posed an urgent demand for ...
We investigate a largely unsupervised approach to learning interpretable, domain-specific entity typ...
Though the utility of domain Ontologies is now widely acknowledged in the IT (Information Technol...
We propose a novel Named Entity Recognition (NER) system based on a machine learning technique and a...
We propose a novel Named Entity Recognition (NER) system based on a machine learning technique and a...
Automatically building domain-specific ontologies is a highly challenging task as it requires extrac...
Abstract. Text mining has an important role to play in aiding experts to construct domain specific o...
Predicting which entities are likely to be mentioned in scientific articles is a task with significa...
One major problem in text mining and semantic retrieval is that detected entity mentions have to be ...
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Infor...
This paper presents an overview of automatic methods for building domain knowledge structures (domai...