Difference between revisions of "Harvesting Wikipedia Knowledge to Identify Topics in Ongoing Natural Language Dialogs"
(Harvesting Wikipedia Knowledge to Identify Topics in Ongoing Natural Language Dialogs - basic info) |
(Wikilinks) |
||
| Line 1: | Line 1: | ||
| − | '''Harvesting Wikipedia Knowledge to Identify Topics in Ongoing Natural Language Dialogs''' - scientific work related to Wikipedia quality published in 2011, written by Alexa Breuing, Ulli Waltinger and Ipke Wachsmuth. | + | '''Harvesting Wikipedia Knowledge to Identify Topics in Ongoing Natural Language Dialogs''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Alexa Breuing]], [[Ulli Waltinger]] and [[Ipke Wachsmuth]]. |
== Overview == | == Overview == | ||
| − | This paper introduces a model harvesting the crowd-sourced encyclopedic knowledge provided by Wikipedia to improve the conversational abilities of an artificial agent. More precisely, authors present a model for automatic topic identification in ongoing natural language dialogs. On the basis of a graph-based representation of the Wikipedia category system, model implements six tasks essential for detecting the topical overlap of coherent dialog contributions. Thereby the identification process operates online to handle dialog streams of constantly changing topical threads in real-time. The realization of the model and its application to conversational agent aims to improve human-agent conversations by transferring human-like topic awareness to the artificial interlocutor. | + | This paper introduces a model harvesting the crowd-sourced encyclopedic knowledge provided by [[Wikipedia]] to improve the conversational abilities of an artificial agent. More precisely, authors present a model for automatic topic identification in ongoing natural language dialogs. On the basis of a graph-based representation of the Wikipedia category system, model implements six tasks essential for detecting the topical overlap of coherent dialog contributions. Thereby the identification process operates online to handle dialog streams of constantly changing topical threads in real-time. The realization of the model and its application to conversational agent aims to improve human-agent conversations by transferring human-like topic awareness to the artificial interlocutor. |
Revision as of 21:28, 28 July 2019
Harvesting Wikipedia Knowledge to Identify Topics in Ongoing Natural Language Dialogs - scientific work related to Wikipedia quality published in 2011, written by Alexa Breuing, Ulli Waltinger and Ipke Wachsmuth.
Overview
This paper introduces a model harvesting the crowd-sourced encyclopedic knowledge provided by Wikipedia to improve the conversational abilities of an artificial agent. More precisely, authors present a model for automatic topic identification in ongoing natural language dialogs. On the basis of a graph-based representation of the Wikipedia category system, model implements six tasks essential for detecting the topical overlap of coherent dialog contributions. Thereby the identification process operates online to handle dialog streams of constantly changing topical threads in real-time. The realization of the model and its application to conversational agent aims to improve human-agent conversations by transferring human-like topic awareness to the artificial interlocutor.
