| Title |
GraPHIA: a computational model for identifying phonological jokes
|
|---|---|
| Published in |
Cognitive Processing, July 2008
|
| DOI | 10.1007/s10339-008-0221-3 |
| Pubmed ID | |
| Authors |
Narayanan Srinivasan, Vani Pariyadath |
| Abstract |
Currently in humor research, there exists a dearth of computational models for humor perception. The existing theories are not quantifiable and efforts need to be made to quantify the models and incorporate neuropsychological findings in humor research. We propose a new computational model (GraPHIA) for perceiving phonological jokes or puns. GraPHIA consists of a semantic network and a phonological network where words are represented by nodes in both the networks. Novel features based on graph theoretical concepts are proposed and computed for the identification of homophonic jokes. The data set for evaluating the model consisted of homophonic puns, normal sentences, and ambiguous nonsense sentences. The classification results show that the feature values result in successful identification of phonological jokes and ambiguous nonsense sentences suggesting that the proposed model is a plausible model for humor perception. Further work is needed to extend the model for identification of other types of phonological jokes. |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 14% |
| India | 2 | 7% |
| France | 1 | 4% |
| Unknown | 21 | 75% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 6 | 21% |
| Student > Ph. D. Student | 6 | 21% |
| Researcher | 4 | 14% |
| Student > Doctoral Student | 3 | 11% |
| Student > Master | 3 | 11% |
| Other | 2 | 7% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 10 | 36% |
| Agricultural and Biological Sciences | 4 | 14% |
| Arts and Humanities | 2 | 7% |
| Linguistics | 2 | 7% |
| Computer Science | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 4 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 17 June 2014.
All research outputs
#5,872,813
of 22,757,541 outputs
Outputs from Cognitive Processing
#82
of 337 outputs
Outputs of similar age
#24,064
of 81,562 outputs
Outputs of similar age from Cognitive Processing
#1
of 4 outputs
Altmetric has tracked 22,757,541 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 337 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has done well, scoring higher than 75% of its peers.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them