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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

October 14, 2015

Speaker: Mennatallah El-Assady, PhD candidate

Title: Incremental Hierarchical Topic Modeling for Multi-Party Conversation Analysis

Abstract: Contributions in political debates or online forum discussions usually do not coincide in their structure, noisiness, and length. Due to this heterogeneity of conversational text data, its fully-automatic analysis remains a challenge for natural language processing.

In recent years, a broad spectrum of topic modeling approaches emerged that capture the thematic structure of parallel document collections with high accuracy. However, studies show that most of these techniques struggle with handling linearly-structured heterogeneous texts. Therefore, when applied on transcribed real-world conversations, these models tend to have a lower accuracy.

In this talk, I am going to present my work on the development of an incremental hierarchical topic modeling algorithm for analyzing linearly structured conversation data of multi-party political debates with respect to their topic distribution. The algorithm is tailored for handling noisy heterogeneous texts and has been successfully applied on several text corpora, yielding accurate topic structures.