| Title |
Question answering system using Q & A site corpus Query expansion and answer candidate evaluation
|
|---|---|
| Published in |
SpringerPlus, August 2013
|
| DOI | 10.1186/2193-1801-2-396 |
| Pubmed ID | |
| Authors |
Kanako Komiya, Yuji Abe, Hajime Morita, Yoshiyuki Kotani |
| Abstract |
Question Answering (QA) is a task of answering natural language questions with adequate sentences. This paper proposes two methods to improve the performance of the QA system using a Q&A site corpus. The first method is for the relevant document retrieval module. We proposed modification of measure of mutual information for the query expansion; we calculate it between two words in each question and a word in its answer in the Q&A site corpus not to choose the words that are not suitable. The second method is for the candidate answer evaluation module. We proposed to evaluate candidate answers using the two measures together, i.e., the Web relevance score and the translation probability. The experiments were carried out using a Japanese Q&A site corpus. They revealed that the first proposed method was significantly better than the original method when their accuracies and MRR (Mean Reciprocal Rank) were compared and the second method was significantly better than the original methods when their MRR were compared. |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Russia | 1 | 6% |
| Unknown | 17 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 33% |
| Researcher | 4 | 22% |
| Student > Ph. D. Student | 3 | 17% |
| Student > Bachelor | 1 | 6% |
| Unknown | 4 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 10 | 56% |
| Business, Management and Accounting | 2 | 11% |
| Engineering | 1 | 6% |
| Unknown | 5 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 August 2013.
All research outputs
#15,279,577
of 22,721,584 outputs
Outputs from SpringerPlus
#932
of 1,852 outputs
Outputs of similar age
#122,770
of 198,941 outputs
Outputs of similar age from SpringerPlus
#48
of 89 outputs
Altmetric has tracked 22,721,584 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,852 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 198,941 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.