This paper describes our research into methods for inferring social and instrumental roles and relationships from document and discourse corpora. The goal is to identify the roles of initial authors and participants in internet discussions with respect to leadership, influence and expertise. Web documents, forums and blogs provide data from which the relationships between these concepts are empirically derived and compared. Using techniques from Natural Language Processing (NLP), characterizations of authority and expertise are hypothesized and then tested to see if these pick out the same or different participants as may be chosen by techniques based on social network analysis (Huffaker 2010) see if they pick out the same discourse participants for any given level of these qualities (i.e. leadership, expertise and influence). Our methods could be applied, in principle, to any domain topic, but this paper will describe an initial investigation into two subject areas where a range of differing opinions are available and which differ in the nature of their appeals to authority and truth: ‘genetic engineering' and a ‘Muslim Forum'. The available online corpora for these topics contain discussions from a variety of users with different levels of expertise, backgrounds and personalities.

This paper examines how Natural Language Process (NLP) resources and online dialogue corpora can be used to extend coverage of Information Extraction (IE) templates in a Spoken Dialogue system. IE templates are used as part of a Natural Language Understanding module for identifying meaning in a user utterance. The use of NLP tools in Dialogue systems is a difficult task given 1) spoken dialogue is often not well-formed and 2) there is a serious lack of dialogue data. In spite of that, we have devised a method for extending IE patterns using standard NLP tools and available dialogue corpora found on the web. In this paper, we explain our method which includes using a set of NLP modules developed using GATE (a General Architecture for Text Engineering), as well as a general purpose editing tool that we built to facilitate the IE rule creation process. Lastly, we present directions for future work in this area.

This paper discusses how Information Extraction is used to understand and manage Dialogue in the EU-funded Companions project. This will be discussed with respect to the Senior Companion, one of two applications under development in the EU-funded Companions project. Over the last few years, research in human-computer dialogue systems has increased and much attention has focused on applying learning methods to improving a key part of any dialogue system, namely the dialogue manager. Since the dialogue manager in all dialogue systems relies heavily on the quality of the semantic interpretation of the user’s utterance, our research in the Companions project, focuses on how to improve the semantic interpretation and combine it with knowledge from the Knowledge Base to increase the performance of the Dialogue Manager. Traditionally the semantic interpretation of a user utterance is handled by a natural language understanding module which embodies a variety of natural language processing techniques, from sentence splitting, to full parsing. In this paper we discuss the use of a variety of NLU processes and in particular Information Extraction as a key part of the NLU module in order to improve performance of the dialogue manager and hence the overall dialogue system.

As web searches increase, there is a need to represent the search results in the most comprehensible way possible. In particular, we focus on search results from queries about people and places. The standard method for presentation of search results is an ordered list determined by the Web search engine. Although this is satisfactory in some cases, when searching for people and places, presenting the information indexed by time may be more desirable. We are developing a system called Cronopath, which generates a timeline of web search engine results by determining the time frame of each document in the collection and linking elements in the timeline to the relevant articles. In this paper, we propose evaluation guidelines for judging the quality of automatically generated timelines based on a set of common features.