[Paper Review] Topic Detection from Conversational Dialogue Corpus with Parallel Dirichlet Allocation Model and Elbow Method

Haider Khalid, Vincent Wade|arXiv (Cornell University)|Jun 5, 2020

Topic Modeling19 references4 citations

TL;DR

This paper proposes a topic detection method for conversational dialogue corpora using Parallel Latent Dirichlet Allocation (PLDA) combined with TF-IDF and Bag-of-Words (BOW) feature representation, enhanced by K-means clustering with the Elbow Method to determine optimal topic clusters. The approach improves topic coherence and consistency, outperforming traditional LDA and clustering techniques in topic refinement and detection accuracy.

ABSTRACT

A conversational system needs to know how to switch between topics to continue the conversation for a more extended period. For this topic detection from dialogue corpus has become an important task for a conversation and accurate prediction of conversation topics is important for creating coherent and engaging dialogue systems. In this paper, we proposed a topic detection approach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering a vocabulary of known similar words based on TF-IDF scores and Bag of Words (BOW) technique. In the experiment, we use K-mean clustering with Elbow Method for interpretation and validation of consistency within-cluster analysis to select the optimal number of clusters. We evaluate our approach by comparing it with traditional LDA and clustering technique. The experimental results show that combining PLDA with Elbow method selects the optimal number of clusters and refine the topics for the conversation.

Motivation & Objective

• To address the challenge of topic switching in conversational dialogue systems by enabling accurate and coherent topic detection.
• To improve topic modeling in dialogue corpora by integrating PLDA with lexical clustering based on TF-IDF and BOW representations.
• To determine the optimal number of topics using the Elbow Method for consistent and interpretable clustering results.
• To validate the effectiveness of the proposed PLDA-based approach against traditional LDA and clustering techniques.
• To enhance topic coherence and system engagement by refining topic boundaries through cluster validation.

Proposed method

• The method employs Parallel Latent Dirichlet Allocation (PLDA) to model topics across multiple dialogue utterances, capturing topic dependencies across turns.
• It uses TF-IDF scores to cluster semantically similar words, improving vocabulary representation for topic modeling.
• Bag-of-Words (BOW) representation is applied to convert dialogue text into numerical vectors for model input.
• K-means clustering is applied to the BOW and TF-IDF features to group similar utterances into topic clusters.
• The Elbow Method is used to determine the optimal number of clusters by analyzing within-cluster sum of squares (WCSS) for consistency and stability.
• The resulting topic clusters are evaluated for coherence and interpretability through validation of within-cluster homogeneity.

Experimental results

Research questions

• RQ1 How can topic detection in conversational dialogue corpora be improved using PLDA and lexical clustering?
• RQ2 What is the optimal number of topics for a given dialogue corpus, and how can it be reliably determined?
• RQ3 How does the integration of TF-IDF and BOW features enhance topic modeling in dialogue systems?
• RQ4 To what extent does the proposed PLDA-Elbow method outperform traditional LDA and clustering in topic coherence and detection accuracy?
• RQ5 Can the Elbow Method effectively guide topic cluster selection in dialogue-based topic detection tasks?

Key findings

• The proposed PLDA-based approach with Elbow Method selection achieved better topic coherence and cluster consistency than traditional LDA and standalone clustering.
• The integration of TF-IDF and BOW features improved the semantic representation of dialogue utterances, enhancing topic modeling accuracy.
• The Elbow Method successfully identified the optimal number of clusters, ensuring stable and interpretable topic boundaries.
• K-means clustering with Elbow validation demonstrated higher within-cluster homogeneity, indicating more consistent topic groups.
• The combination of PLDA and the Elbow Method refined topic detection, leading to more coherent and engaging dialogue system behavior.
• Experimental results confirmed that the proposed method outperformed baseline LDA and clustering techniques in topic refinement and detection performance.