Discussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods.

@inproceedings{nguyen-joty-boussaha-rijke-neuir-17, abstract = {Discussion forums are an important source of information. They are often used to answer specific questions a user might have and to discover more about a topic of interest. Discussions in these forums may evolve in intricate ways, making it difficult for users to follow the flow of ideas. We propose a novel approach for automatically identifying the underlying thread structure of a forum discussion. Our approach is based on a neural model that computes coherence scores of possible reconstructions and then selects the highest scoring, i.e., the most coherent one. Preliminary experiments demonstrate promising results outperforming a number of strong baseline methods.}, address = {Tokyo, Japan}, author = {Dat Nguyen and Shafiq Joty and Basma Boussaha and Maarten Rijke}, booktitle = {Proceedings of the Neu-IR 2017 SIGIR Workshop on Neural Information Retrieval}, month = {Aug}, pages = {xx--xx}, publisher = {ACM}, series = {NeuIR'17}, title = {Thread Reconstruction in Conversational Data using Neural Coherence Models}, url = {papers/nguyen-joty-boussaha-rijke-neuir-17.pdf}, year = {2017} }