A comparison of parsing technologies for the biomedical domain

This paper reports on a number of experiments which are designed to investigate the extent to which current nlp resources are able to syntactically and semantically analyse biomedical text. We address two tasks: parsing a real corpus with a hand-built widecoverage grammar, producing both syntactic analyses and logical forms; and automatically computing the interpretation of compound nouns where the head is a nominalisation (e.g., hospital arrival means an arrival at hospital, while patient arrival means an arrival of a patient). For the former task we demonstrate that exible and yet constrained `preprocessing ' techniques are crucial to success: these enable us to use part-of-speech tags to overcome inadequate lexical coverage, and to `package up' complex technical expressions prior to parsing so that they are blocked from creating misleading amounts of syntactic complexity. We argue that the xml-processing paradigm is ideally suited for automatically preparing the corpus for parsing. For the latter task, we compute interpretations of the compounds by exploiting surface cues and meaning paraphrases, which in turn are extracted from the parsed corpus. This provides an empirical setting in which we can compare the utility of a comparatively deep parser vs. a shallow one, exploring the trade-o between resolving attachment ambiguities on the one hand and generating errors in the parses on the other. We demonstrate that a model of the meaning of compound nominalisations is achievable with the aid of current broad-coverage parsers

Learning Parse and Translation Decisions From Examples With Rich Context

We present a knowledge and context-based system for parsing and translating natural language and evaluate it on sentences from the Wall Street Journal. Applying machine learning techniques, the system uses parse action examples acquired under supervision to generate a deterministic shift-reduce parser in the form of a decision structure. It relies heavily on context, as encoded in features which describe the morphological, syntactic, semantic and other aspects of a given parse state.Comment: 8 pages, LaTeX, 3 postscript figures, uses aclap.st

THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK

Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

Master of Science

thesisRepresentations in the form of concept maps have been shown to be a benefit to leaners. However, previous research examined the influence of these representations in learning in well-structured environments. Additionally, previous research suggests that increasing the activity of students in learning environments has also been shown to yield gains in learning, called the generation effect. The current study extends the literature by examining the influence generative activities and concept map representations have on an ill-structured reasoning process, namely thinking like a lawyer. Pre- and posttests targeting factual knowledge, recall, and transfer were used to assess learning, while verbal protocols were implemented to examine learning processes used by participants. Results were mixed. Representation and activity had no effect on factual knowledge, recall, and near transfer measures. Verbal protocol results showed that students who studied with the concept map representation condition produced a higher proportion of deep utterances during problem solving when using static representations compared to those that generated their representation. The opposite was true for students in the text list condition. Those who generated their text list representation during study produced a higher proportion of deep utterances in problem solving when compared to those who studied with a static list. Thus, a careful consideration of topical materials and learning environments is necessary to determine whether or not concept maps and generation effects will encourage deeper comprehension in learners

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in