This research addresses the question, "how do we evaluate systems like LOLITA?" LOLITA is the Natural Language Processing (NLP) system under development at the University of Durham. It is intended as a platform for building NL applications. We are therefore interested in questions of evaluation for such general NLP systems. The thesis has two, parts. The first, and main, part concerns the participation of LOLITA in the Sixth Message Understanding Conference (MUC-6). The MUC-relevant portion of LOLITA is described in detail. The adaptation of LOLITA for MUC-6 is discussed, including work undertaken by the author. Performance on a specimen article is analysed qualitatively, and in detail, with anonymous comparisons to competitors' output. We also examine current LOLITA performance. A template comparison tool was implemented to aid these analyses. The overall scores are then considered. A methodology for analysis is discussed, and a comparison made with current scores. The comparison tool is used to analyse how systems performed relative to each-other. One method, Correctness Analysis, was particularly interesting. It provides a characterisation of task difficulty, and indicates how systems approached a task. Finally, MUC-6 is analysed. In particular, we consider the methodology and ways of interpreting the results. Several criticisms of MUC-6 are made, along with suggestions for future MUC-style events. The second part considers evaluation from the point of view of general systems. A literature review shows a lack of serious work on this aspect of evaluation. A first principles discussion of evaluation, starting from a view of NL systems as a particular kind of software, raises several interesting points for single task evaluation. No evaluations could be suggested for general systems; their value was seen as primarily economic. That is, we are unable to analyse their linguistic capability directly.

The overwhelming amount of online news presents a challenge called news information overload. To mitigate this challenge we propose a system to generate a causal network of news topics. To extract this information from distributed news sources, a system called Forest was developed. Forest retrieves documents that potentially contain causal information regarding a news topic. The documents are processed at a sentence level to extract causal relations and news topic references, these are the phases used to refer to a news topic. Forest uses a machine learning approach to classify causal sentences, and then renders the potential cause and effect of the sentences. The potential cause and effect are then classified as news topic references, these are the phrases used to refer to a news topics, such as “The World Cup” or “The Financial Meltdown”. Both classifiers use an algorithm developed within our working group, the algorithm performs better than several well known classification algorithms for the aforementioned tasks. In our evaluations we found that participants consider causal information useful to understand the news, and that while we can not extract causal information for all news topics, it is highly likely that we can extract causal relation for the most popular news topics. To evaluate the accuracy of the extractions made by Forest, we completed a user survey. We found that by providing the top ranked results, we obtained a high accuracy in extracting causal relations between news topics.

The overwhelming amount of online news presents a challenge called news information overload. To mitigate this challenge we propose a system to generate a causal network of news topics. To extract this information from distributed news sources, a system called Forest was developed. Forest retrieves documents that potentially contain causal information regarding a news topic. The documents are processed at a sentence level to extract causal relations and news topic references, these are the phases used to refer to a news topic. Forest uses a machine learning approach to classify causal sentences, and then renders the potential cause and effect of the sentences. The potential cause and effect are then classified as news topic references, these are the phrases used to refer to a news topics, such as “The World Cup” or “The Financial Meltdown”. Both classifiers use an algorithm developed within our working group, the algorithm performs better than several well known classification algorithms for the aforementioned tasks. In our evaluations we found that participants consider causal information useful to understand the news, and that while we can not extract causal information for all news topics, it is highly likely that we can extract causal relation for the most popular news topics. To evaluate the accuracy of the extractions made by Forest, we completed a user survey. We found that by providing the top ranked results, we obtained a high accuracy in extracting causal relations between news topics.