General Pipeline Architecture for Domain-Specific Dialogue Extraction from different IRC Channels

Inhaltsverzeichnis (Table of Contents)

• Chapter 1: Introduction
• Motivation: Why is the Topic so Important?
• Thesis Overview
• The Problem and Contribution
• Outline of The Thesis
• Chapter 2: Background
• Dialogue Systems
• Introduction
• Data-Driven vs Other Design Approaches
• McGill Ubuntu Dialogue Corpus
• Chapter 3: Methods and Techniques
• Natural Language Processing (NLP)
• Introduction
• Wikipedia-Based Explicit Semantic Analysis (ESA)
• Deep Learning
• Why Deep Learning?
• Deep Neural Networks: Definitions and Basics
• RNN and LSTM Networks

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This thesis aims to develop a general pipeline architecture for extracting one-on-one dialogues from various IRC channels, building upon existing work using the Ubuntu IRC channel. The thesis also explores the application of Wikipedia-Based Explicit Semantic Analysis (ESA) on the extracted dialogues. This work contributes to the advancement of data-driven dialogue systems, particularly in the area of best response selection.

• Development of a general pipeline architecture for extracting one-on-one dialogues from multiple IRC channels.
• Application of Wikipedia-Based Explicit Semantic Analysis (ESA) on extracted dialogues.
• Evaluation of ESA's effectiveness in improving dialogue interpretation.
• Exploration of the potential of deep learning methods in dialogue systems.
• Contribution to the advancement of data-driven dialogue systems.

Zusammenfassung der Kapitel (Chapter Summaries)

Chapter 1: Introduction outlines the importance of the topic, provides an overview of the thesis, and details the problem addressed and the contribution made. It also presents the structure of the thesis.

Chapter 2: Background discusses the concept of Dialogue Systems, with a particular focus on Data-Driven approaches. It introduces the McGill Ubuntu Dialogue Corpus, a significant dataset used for training dialogue systems.

Chapter 3: Methods and Techniques delves into Natural Language Processing (NLP), focusing on Wikipedia-Based Explicit Semantic Analysis (ESA) as a technique for improving dialogue interpretation. The chapter also explores Deep Learning, emphasizing its potential in the field of dialogue systems and introducing the concepts of Deep Neural Networks, RNNs, and LSTMs.

Schlüsselwörter (Keywords)

The central focus of this thesis lies on Dialogue Systems, Data-Driven Approaches, IRC Channel Dialogue Extraction, Natural Language Processing (NLP), Wikipedia-Based Explicit Semantic Analysis (ESA), Deep Learning, and best response selection in unstructured dialogue systems.