Massih R. Amini

Also published as: Massih-Reza Amini


Topical Coherence in LDA-based Models through Induced Segmentation
Hesam Amoualian | Wei Lu | Eric Gaussier | Georgios Balikas | Massih R. Amini | Marianne Clausel
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words. The coherence between topics is ensured through a copula, binding the topics associated to the words of a segment. In addition, this model relies on both document and segment specific topic distributions so as to capture fine grained differences in topic assignments. We show that the proposed model naturally encompasses other state-of-the-art LDA-based models designed for similar tasks. Furthermore, our experiments, conducted on six different publicly available datasets, show the effectiveness of our model in terms of perplexity, Normalized Pointwise Mutual Information, which captures the coherence between the generated topics, and the Micro F1 measure for text classification.


TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Georgios Balikas | Massih-Reza Amini
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

Modeling topic dependencies in semantically coherent text spans with copulas
Georgios Balikas | Hesam Amoualian | Marianne Clausel | Eric Gaussier | Massih R. Amini
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

The exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to be topically coherent. We propose copulaLDA, that extends LDA by integrating part of the text structure to the model and relaxes the conditional independence assumption between the word-specific latent topics given the per-document topic distributions. To this end, we assume that the words of text spans like noun-phrases are topically bound and we model this dependence with copulas. We demonstrate empirically the effectiveness of copulaLDA on both intrinsic and extrinsic evaluation tasks on several publicly available corpora.