question Answering with Subgraph Embeddings: Difference between revisions
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== Introduction == | == Introduction == | ||
Teaching machines are you answer questions automatically in a natural language has been a long standing goal in AI. There has been a rise in large scale structured knowledge bases (KBs), such as Freebase | Teaching machines are you answer questions automatically in a natural language has been a long standing goal in AI. There has been a rise in large scale structured knowledge bases (KBs), such as Freebase [3], to tackle the problem known as open-domain question answers (or open QA). However, the scale and difficulty for machines to interpret natural language still makes this problem challenging. | ||
open QA techniques can be classified into two main categories: | open QA techniques can be classified into two main categories: | ||
*Information retrieval based: retrieve a broad set of answers be first query the API of the KBs then narrow down the answer using heuristics | *Information retrieval based: retrieve a broad set of answers be first query the API of the KBs then narrow down the answer using heuristics [8,12,14]. | ||
*Semantic parsing based: | *Semantic parsing based: focus on the correct interpretation of the query. Querying the interpreted question from the KB should return the correct answer [1,9,2,7]. | ||
Both of these approaches require negligible interventions (hand-craft lexicons, grammars and KB schemas) to be effective. | |||
== Task Definition == | == Task Definition == | ||
== Embedding Questions and Answers == | == Embedding Questions and Answers == | ||
Revision as of 16:33, 9 November 2015
Introduction
Teaching machines are you answer questions automatically in a natural language has been a long standing goal in AI. There has been a rise in large scale structured knowledge bases (KBs), such as Freebase [3], to tackle the problem known as open-domain question answers (or open QA). However, the scale and difficulty for machines to interpret natural language still makes this problem challenging.
open QA techniques can be classified into two main categories:
- Information retrieval based: retrieve a broad set of answers be first query the API of the KBs then narrow down the answer using heuristics [8,12,14].
- Semantic parsing based: focus on the correct interpretation of the query. Querying the interpreted question from the KB should return the correct answer [1,9,2,7].
Both of these approaches require negligible interventions (hand-craft lexicons, grammars and KB schemas) to be effective.