A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain Knowledge from Wikipedia
نویسندگان
چکیده
Dialog topic tracking aims at analyzing and maintaining topic transitions in ongoing dialogs. This paper proposes a composite kernel approach for dialog topic tracking to utilize various types of domain knowledge obtained fromWikipedia. Two kernels are defined based on history sequences and context trees constructed based on the extracted features. The experimental results show that our composite kernel approach can significantly improve the performances of topic tracking in mixed-initiative human-human dialogs.
منابع مشابه
Interfacing Virtual Agents with Collaborative Knowledge: Open Domain Question Answering Using Wikipedia-Based Topic Models
This paper is concerned with the use of conversational agents as an interaction paradigm for accessing open domain encyclopedic knowledge by means of Wikipedia. More precisely, we describe a dialog-based question answering system for German which utilizes Wikipedia-based topic models as a reference point for context detection and answer prediction. We investigate two different perspectives to t...
متن کاملAdvertising Keyword Suggestion Using Relevance-Based Language Models from Wikipedia Rich Articles
When emerging technologies such as Search Engine Marketing (SEM) face tasks that require human level intelligence, it is inevitable to use the knowledge repositories to endow the machine with the breadth of knowledge available to humans. Keyword suggestion for search engine advertising is an important problem for sponsored search and SEM that requires a goldmine repository of knowledge. A recen...
متن کاملTalking Topically to Artificial Dialog Partners: Emulating Humanlike Topic Awareness in a Virtual Agent
During dialog, humans are able to track ongoing topics, to detect topical shifts, to refer to topics via labels, and to decide on the appropriateness of potential dialog topics. As a result, they interactionally produce coherent sequences of spoken utterances assigning a thematic structure to the whole conversation. Accordingly, an artificial agent that is intended to engage in natural and soph...
متن کاملAn End-to-End Trainable Neural Network Model with Belief Tracking for Task-Oriented Dialog
We present a novel end-to-end trainable neural network model for task-oriented dialog systems. The model is able to track dialog state, issue API calls to knowledge base (KB), and incorporate structured KB query results into system responses to successfully complete task-oriented dialogs. The proposed model produces well-structured system responses by jointly learning belief tracking and KB res...
متن کاملA Wikipedia Based Semantic Graph Model for Topic Tracking in Blogsphere
There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the “word-of-mouth” effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph, in which...
متن کامل