Humans use document formatting to discover document and section titles, and important phrases. But when machines process a paper–especially documents OCRed from images–these cues are often invisible to downstream processes: words in footnotes or body text are treated as just as important as words in titles. It would be better for indexing and summarization tools to be guided by implicit document structure. In an ODNI-sponsored project, ARLIS looked at discovering formatting in OCRed text as a way to infer document structure. Most OCR engines output results as hOCR (an XML format), giving bounding boxes around characters. In theory, this also provides style information such as bolding and italicization, but in practice, this capability is limited. For example, the Tesseract OCR tool provides bounding boxes, but does not attempt to detect bold text (relevant to author emphasis and specialized fields in e.g. print dictionaries), and its discrimination of italicization is poor. Our project inferred font size from hOCR bounding boxes, and using that and other cues (e.g. the fact that titles tend to be short) determined which text constituted section titles; from this, a document outline can be created. We also experimented with algorithms for detecting bold text. Our best algorithm has a much improved recall and precision, although the exact numbers are font-dependent. The next step is to incorporate inferred structure into the output of machine translation. One way is to embed XML tags for inferred structure into the text extracted from the imaged document, and to either pass the strings enclosed by XML tags to the MT engine individually, or pass the tags through the MT engine without modification. This structural information can guide downstream bulk processing tasks such as summarization and search, and also enables building tables of contents for human users examining individual documents.

Lexicon schemas and their use are discussed in this paper from the perspective of lexicographers and field linguists. A variety of lexicon schemas have been developed, with goals ranging from computational lexicography (DATR) through archiving (LIFT, TEI) to standardization (LMF, FSR). A number of requirements for lexicon schemas are given. The lexicon schemas are introduced and compared to each other in terms of conversion and usability for this particular user group, using a common lexicon entry and providing examples for each schema under consideration. The formats are assessed and the final recommendation is given for the potential users, namely to request standard compliance from the developers of the tools used. This paper should foster a discussion between authors of standards, lexicographers and field linguists.

General morphological/phonological analysis using ordered phonological rules has appeared to be computationally expensive, because ambiguities in feature values arising when phonological rules are “un-applied” multiply with additional rules. But in fact those ambiguities can be largely ignored until lexical lookup, since the underlying values of altered features are needed only in the case of rare opaque rule orderings, and not always then.