How we do things with words: Analyzing text as social and cultural data

In this article we describe our experiences with computational text analysis. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of best practices for working with thick social and cultural concepts. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that will resonate for many. And this leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis that involves social and cultural concepts, and the more we are able to bridge these divides, the more fruitful we believe our work will be

Incremental generation of plural descriptions : similarity and partitioning

Approaches to plural reference generation emphasise descriptive brevity, but often lack empirical backing. This paper describes a corpus-based study of plural descriptions, and proposes a psycholinguisticallymotivated algorithm for plural reference generation. The descriptive strategy is based on partitioning and incorporates corpusderived heuristics. An exhaustive evaluation shows that the output closely matches human data.peer-reviewe

Readers and Reading in the First World War

This essay consists of three individually authored and interlinked sections. In ‘A Digital Humanities Approach’, Francesca Benatti looks at datasets and databases (including the UK Reading Experience Database) and shows how a systematic, macro-analytical use of digital humanities tools and resources might yield answers to some key questions about reading in the First World War. In ‘Reading behind the Wire in the First World War’ Edmund G. C. King scrutinizes the reading practices and preferences of Allied prisoners of war in Mainz, showing that reading circumscribed by the contingencies of a prison camp created an unique literary community, whose legacy can be traced through their literary output after the war. In ‘Book-hunger in Salonika’, Shafquat Towheed examines the record of a single reader in a specific and fairly static frontline, and argues that in the case of the Salonika campaign, reading communities emerged in close proximity to existing centres of print culture. The focus of this essay moves from the general to the particular, from the scoping of large datasets, to the analyses of identified readers within a specific geographical and temporal space. The authors engage with the wider issues and problems of recovering, interpreting, visualizing, narrating, and representing readers in the First World War

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Text content and task performance in the evaluation of a natural language generation system

An important question in the evaluation of Natural Language Generation systems concerns the relationship between textual characteristics and task performance. If the results of task-based evaluation can be correlated to properties of the text, there are better prospects for improving the system. The present paper investigates this relationship by focusing on the outcomes of a task-based evaluation of a system that generates summaries of patient data, attempting to correlate these with the results of an analysis of the system’s texts, compared to a set of gold standard human-authored summaries.peer-reviewe

Robust Processing of Natural Language

Previous approaches to robustness in natural language processing usually treat deviant input by relaxing grammatical constraints whenever a successful analysis cannot be provided by ``normal'' means. This schema implies, that error detection always comes prior to error handling, a behaviour which hardly can compete with its human model, where many erroneous situations are treated without even noticing them. The paper analyses the necessary preconditions for achieving a higher degree of robustness in natural language processing and suggests a quite different approach based on a procedure for structural disambiguation. It not only offers the possibility to cope with robustness issues in a more natural way but eventually might be suited to accommodate quite different aspects of robust behaviour within a single framework.Comment: 16 pages, LaTeX, uses pstricks.sty, pstricks.tex, pstricks.pro, pst-node.sty, pst-node.tex, pst-node.pro. To appear in: Proc. KI-95, 19th German Conference on Artificial Intelligence, Bielefeld (Germany), Lecture Notes in Computer Science, Springer 199