Caroline Barrière
Biographic Data
| ID | 9292364 |
|---|---|
| NAME | Caroline Barrière |
| GIVEN NAMES | Caroline |
| FAMILY NAME | Barrière |
| SIGNATURE | BARRIÈRE C |
| AFFILIATIONS | University of Ottawa |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2001 |
| LATEST PUBLICATION YEAR | 2008 |
| H-INDEX | 0 |
Pattern-based approaches to semantic relation extraction
Peer reviewed: Yes
Web-based extraction of semantic relation instances for terminology work
This article describes the implementation and evaluation of WWW2REL, a domain-independent and pattern-based knowledge discovery system which extracts semantic relation instances from text fragments on the WWW so as to assist terminologists updating or expanding existing ontologies. Unlike most comparable systems, WWW2REL is special in that it can be applied to any semantic relation type and operates directly on unannotated and uncategorized WWW t…
Building a concept hierarchy from corpus analysis
Corpus analysis is today at the heart of building Terminological Knowledge Bases (TKBs). Important terms are usually first extracted from a corpus and then related to one another via semantic relations. This research brings the discovery of semantic relations to the forefront to allow the discovery of less stable lexical units or unlabeled concepts, which are important to include in a TKB to facilitate knowledge organization. We suggest a concept…
Hierarchical refinement and representation of the causal relation
This research looks at the complexity inherent in the causal relation and the implications for its representation in a Terminological Knowledge Base (TKB). Supported by a more general study of semantic relation hierarchies, a hierarchical refinement of the causal relation is proposed. It results from a manual search of a corpus which shows that it efficiently captures and formalizes variations expressed in text. The feasibility of determining suc…
Investigating the causal relation in informative texts
Our work investigates the causal relation as it is expressed in informative texts. We view causal relations as important because of the dynamic dimension they bring to a domain model. Thorough study of a corpus leads us to distinguish two prominent classes of indicators of the causal relation: conjunctional phrases, and verbs. This paper identifies multiple knowledge-rich patterns within each class and studies their usage, frequency and noise. Re…
No prominent works on this page.
Investigating the causal relation in informative texts
Our work investigates the causal relation as it is expressed in informative texts. We view causal relations as important because of the dynamic dimension they bring to a domain model. Thorough study of a corpus leads us to distinguish two prominent classes of indicators of the causal relation: conjunctional phrases, and verbs. This paper identifies multiple knowledge-rich patterns within each class and studies their usage, frequency and noise. Re…
Hierarchical refinement and representation of the causal relation
This research looks at the complexity inherent in the causal relation and the implications for its representation in a Terminological Knowledge Base (TKB). Supported by a more general study of semantic relation hierarchies, a hierarchical refinement of the causal relation is proposed. It results from a manual search of a corpus which shows that it efficiently captures and formalizes variations expressed in text. The feasibility of determining suc…
Building a concept hierarchy from corpus analysis
Corpus analysis is today at the heart of building Terminological Knowledge Bases (TKBs). Important terms are usually first extracted from a corpus and then related to one another via semantic relations. This research brings the discovery of semantic relations to the forefront to allow the discovery of less stable lexical units or unlabeled concepts, which are important to include in a TKB to facilitate knowledge organization. We suggest a concept…
Pattern-based approaches to semantic relation extraction
Peer reviewed: Yes
Web-based extraction of semantic relation instances for terminology work
This article describes the implementation and evaluation of WWW2REL, a domain-independent and pattern-based knowledge discovery system which extracts semantic relation instances from text fragments on the WWW so as to assist terminologists updating or expanding existing ontologies. Unlike most comparable systems, WWW2REL is special in that it can be applied to any semantic relation type and operates directly on unannotated and uncategorized WWW t…
Artificial Intelligence (5 works) · Computer Science (5 works) · Natural language processing (5 works) · Data mining (4 works) · Natural Language Processing Techniques (4 works) · Semantic Web and Ontologies (4 works) · Relationship extraction (3 works) · Semantic relation (3 works) · Advanced Text Analysis Techniques (2 works) · Information retrieval (2 works)