# topicModelling **Repository Path**: Samuelcoding/topicModelling ## Basic Information - **Project Name**: topicModelling - **Description**: 主题模型计算文本匹配 - **Primary Language**: Unknown - **License**: GPL-3.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-08-11 - **Last Updated**: 2020-12-18 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Topic Modeling The project contains implementations of topic models I have used during my thesis. Those implementations have been used for different papers. SentenceLDA ==== In particular, we have proposed the sentenceLDA in the SIGIR 2016 paper : [On a topic model for sentences](https://arxiv.org/pdf/1606.00253v1.pdf). CopulaLDA ==== In particular, we have proposed copulaLDA in the Coling 2016 paper : [Modeling topic dependencies in semantically coherent text spans with copulas ](TBD). References ====== In case you use the model, please cite our paper: ``` @InProceedings{balikas2016sigir, author = {Georgios Balikas and Massih-Reza Amini and Marianne Clausel}, title = {On a Topic Model for Sentences}, booktitle = {Proceedings of the 39th International {ACM} {SIGIR} conference on Research and Development in Information Retrieval, {SIGIR} 2016, Pisa, Italy, July 17-21, 2016}, pages = {921--924}, year = {2016}} ``` For the copulaLDA model, please also cite: ``` @InProceedings{balikas-EtAl:2016:COLING, author = {Balikas, Georgios and Amoualian, Hesam and Clausel, Marianne and Gaussier, Eric and Amini, Massih R}, title = {Modeling topic dependencies in semantically coherent text spans with copulas}, booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers}, month = {December}, year = {2016}, address = {Osaka, Japan}, publisher = {The COLING 2016 Organizing Committee}, pages = {1767--1776}, url = {http://aclweb.org/anthology/C16-1166} } ``` Notes ==== This is development code and may not be fully functional. That said, the code was tested with Python 2.7 and R 3.1.1 and was functional. Normally, you should be able to reproduce all the experiments reported in the papers.