Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of incorporating domain knowledge to text matching.

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Healthcare systems have increased patients' exposure to their own health materials to enhance patients' health levels, but this has been impeded by patients' lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople's comprehension of health material via a human subjects' study (n = 160).

In this paper we present a novel framework for morpheme segmentation which uses the morpho-syntactic regularities preserved by word representations, in addition to orthographic features, to segment words into morphemes. This framework is the first to consider vocabulary-wide syntactico-semantic information for this task. We also analyze the deficiencies of available benchmarking datasets and introduce our own dataset that was created on the basis of compositionality. We validate our algorithm across different datasets and languages and present new state-of-the-art results.

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