Name
Abstract | ||
|---|---|---|
Questions that require answers in the form of a list of entities and the identification of diverse biological entity classes present an interesting challenge that required new approaches not needed for the TREC 2006 Genomics track. We added some components to our automatic question answering system, including (i) a simple algorithm to select which keywords extracted from natural language questions should be treated as essential in the formation of queries, (ii) the use of different entity recognizers for various biological entity classes in the extraction of passages (iii) determining relevancy of candidate passages with the use of semantic relatedness based on MeSH and UMLS semantic network. We present here an overview of our approach, with preliminary analysis and insights as to the performance of our system. |
Year | Venue | Keywords |
|---|---|---|
2007 | TREC | semantic network,natural language,question answering system,semantic relatedness |
Field | DocType | Citations |
Semantic similarity,Question answering,Information retrieval,Computer science,Semantic network,Semantic equivalence,Natural language,Artificial intelligence,Natural language processing,MultiNet,Unified Medical Language System,Semantic computing | Conference | 3 |
PageRank | References | Authors |
0.41 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Tari | 1 | 178 | 13.56 |
| Phan Huy Tu | 2 | 143 | 9.23 |
| Barry Lumpkin | 3 | 22 | 1.61 |
| Robert Leaman | 4 | 914 | 39.98 |
| Graciela Gonzalez | 5 | 624 | 39.60 |
| Chitta Baral | 6 | 2353 | 269.58 |