Using Extended Tree Kernel to Recognize Metalanguage in Text

The problem of classifying text with respect to metalanguage and language-object patters is formulated and its application areas are proposed. We extend parse tree kernels from the level of individual sentences towards the level of paragraphs to classify texts at a high level of abstraction. The method targets the text classification tasks where keyword statistics is insufficient for text classification tasks. We build a set of extended trees for a paragraph of text from the individual parse trees for sentences. Conventional parse trees are extended across sentences based on anaphora and rhetoric structure relations between the phrases in different sentences. Tree kernel learning is applied to extended trees to take advantage of additional discourse-related information. We evaluate our approach in the security-related domain of the design documents. These are the documents which contain a formal well-structured presentation on how a system is built. Design documents need to be differentiated from product requirements, architectural, general design notes, templates, research results and other types of documents, which can share the same keywords. We also evaluate classification in the literature domain, classifying text in Kafka’s novel “The Trial” as metalanguage versus novel’s description in scholarly studies as a mixture of metalanguage and language-object.