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Learning from Errors. Systematic Analysis of Complex Writing Errors for Improving Writing Technology
(2015)
In this paper, we describe ongoing research on writing errors with the ultimate goal to develop error-preventing editing functions in word-processors. Drawing from the state-of-the-art research in errors carried out in various fields, we propose the application of a general concept for action-slips as introduced by Norman. We demonstrate the feasibility of this approach by using a large corpus of writing errors in published texts. The concept of slips considers both the process and the product: some failure in a procedure results in an error in the product, i.e., is visible in the written text. In order to develop preventing functions, we need to determine causes of such visible errors.
In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.