Name
Title | ||
|---|---|---|
Improving dialogue classification using a topic space representation and a Gaussian classifier based on the decision rule |
Abstract | ||
|---|---|---|
In this paper, we study the impact of dialogue representations and classification methods in the task of theme identification of telephone conversation services having highly imperfect automatic transcriptions. Two dialogue representations are firstly compared: the classical Term Frequency-Inverse Document Frequency with Gini purity criteria (TF-IDF-Gini) method and the Latent Dirichlet Allocation (LDA) approach. We then propose to study an original classification method that takes advantage of the LDA topic space representation, highlighted as the best dialogue representation. To do so, two assumptions about topic representation led us to choose a Gaussian process (GP) based method. This approach is compared with a Support Vector Machine (SVM) classification method. Results show that the GP approach is a better solution to deal with the multiple theme complexity of a dialogue, no matter the conditions studied (manual or automatic transcriptions). We finally discuss the impact of the topic space reduction on the classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6853571 | ICASSP |
Keywords | Field | DocType |
svm classification method,speech recognition,topic space representation,latent dirichlet allocation,theme classification,decision rule,term frequency-inverse document frequency-gini purity criteria,pattern classification,svm,dialogue representation,gaussian process based method,speech analytics,gaussian process,support vector machine,multiple theme complexity,gaussian processes,gaussian classifier,tf-idf-gini method,interactive systems,telephone conversation services,theme identification,dialogue classification,support vector machines,lda approach,automatic transcriptions,vectors,resource management,speech,accuracy,semantics | Decision rule,Transcription (linguistics),Latent Dirichlet allocation,Conversation,Imperfect,Pattern recognition,Computer science,Support vector machine,Gaussian process,Artificial intelligence,Gaussian classifier,Machine learning | Conference |
ISSN | Citations | PageRank |
1520-6149 | 14 | 0.62 |
References | Authors | |
17 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamed Morchid | 1 | 84 | 22.79 |
| richard dufour | 2 | 98 | 23.98 |
| Pierre-Michel Bousquet | 3 | 127 | 13.05 |
| Mohamed Bouallegue | 4 | 38 | 6.13 |
| georges linar es | 5 | 136 | 29.55 |
| Renato De Mori | 6 | 960 | 161.75 |