Name
Title | ||
|---|---|---|
Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track. |
Abstract | ||
|---|---|---|
Abstract : Users may have a variety of tasks that give rise to issuing a particular query. The goal of the Tasks Track at TREC2015 was to identify all aspects or subtasks of a users task as well as the documents relevant to the entire task. This was broken into two parts: (1) Task Understanding which judged relevance of key phrases or queries to the original query (relative to a likely task that would have given rise to both); (2) Task Completion which performed document retrieval and measured usefulness to any task a user with the query might be peforming through either a completion measure that uses both relevance and usefulness criteria or more simply through an ad hoc retrieval measure of relevance alone. We submitted a run in the Task Understanding track. In particular, since the anchor text graph has proven useful in the general realm of query reformulation [2], we sought to quantify the value of extracting key phrases from anchor text in the broader setting of the task understanding track. |
Year | Venue | Field |
|---|---|---|
2015 | TREC | Data mining,Graph,World Wide Web,Task analysis,Realm,Information retrieval,Computer science,Anchor text,Natural language processing,Artificial intelligence,Document retrieval,Task completion |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul N. Bennett | 1 | 1500 | 87.93 |
| Ryen White | 2 | 4546 | 222.75 |