Satoshi Sekine
Datos Biográficos
| ID | 3483225 |
|---|---|
| NOMBRE | Satoshi Sekine |
| NOMBRES | Satoshi |
| APELLIDO | Sekine |
| FIRMA | SEKINE S |
| AFILIACIONES | New York University |
| VERIFICADO | No |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAS | 21 |
| TOTAL COMO AUTOR | 2 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2000 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2007 |
| ÍNDICE H | 1 |
A survey of named entity recognition and classification
This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as fe…
Term recognition using corpora from different fields
We present a system used in the term recognition competition, one of the subtasks covered by the NTCIR tmrec group, and we evaluate its term recognition results. We regard that terms are lexical items, characteristic of a field, which have the following three features: (1) they appear frequently in documents of the target field; (2) they are not common words in the target field; and (3) they appear less frequently in the corpora of other fields. …
A survey of named entity recognition and classification
This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as fe…
Term recognition using corpora from different fields
We present a system used in the term recognition competition, one of the subtasks covered by the NTCIR tmrec group, and we evaluate its term recognition results. We regard that terms are lexical items, characteristic of a field, which have the following three features: (1) they appear frequently in documents of the target field; (2) they are not common words in the target field; and (3) they appear less frequently in the corpora of other fields. …
A survey of named entity recognition and classification
This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as fe…
Natural Language Processing Techniques (2 obras) · Artificial Intelligence (1 obras) · Authorship Attribution and Profiling (1 obras) · Biomedical Text Mining and Ontologies (1 obras) · Computer Science (1 obras) · Information retrieval (1 obras) · Mathematics (1 obras) · Named entity (1 obras) · Natural language processing (1 obras) · Semantic Web and Ontologies (1 obras)