With this first issue of Volume 10 of Natural Language Engineering we would like to take this opportunity to reflect on the development of the journal, especially over the last year or two, and to look forward to a number of future developments.

This paper presents modifications to a standard probabilistic context-free grammar that enable a predictive parser to avoid garden pathing without resorting to any ad-hoc heuristic repair. The resulting parser is shown to apply efficiently to both newspaper text and telephone conversations with complete coverage and excellent accuracy. The distribution over trees is peaked enough to allow the parser to find parses efficiently, even with the much larger search space resulting from overgeneration. Empirical results are provided for both Wall St. Journal and Switchboard test corpora.

Existing software systems for automated essay scoring can provide NLP researchers with opportunities to test certain theoretical hypotheses, including some derived from Centering Theory. In this study we employ the Educational Testing Service's e-rater essay scoring system to examine whether local discourse coherence, as defined by a measure of Centering Theory's Rough-Shift transitions, might be a significant contributor to the evaluation of essays. Rough-Shifts within students' paragraphs often occur when topics are short-lived and unconnected, and are therefore indicative of poor topic development. We show that adding the Rough-Shift based metric to the system improves its performance significantly, better approximating human scores and providing the capability of valuable instructional feedback to the student. These results indicate that Rough-Shifts do indeed capture a source of incoherence, one that has not been closely examined in the Centering literature. They not only justify Rough-Shifts as a valid transition type, but they also support the original formulation of Centering as a measure of discourse continuity even in pronominal-free text. Finally, our study design, which used a combination of automated and manual NLP techniques, highlights specific areas of NLP research and development needed for engineering practical applications.

The topic of mood and modality (MOD) is a difficult aspect of language description because, among other reasons, the inventory of modal meanings is not stable across languages, moods do not map neatly from one language to another, modality may be realised morphologically or by free-standing words, and modality interacts in complex ways with other modules of the grammar, like tense and aspect. Describing MOD is especially difficult if one attempts to develop a unified approach that not only provides cross-linguistic coverage, but is also useful in practical natural language processing systems. This article discusses an approach to MOD that was developed for and implemented in the Boas Knowledge-Elicitation (KE) system. Boas elicits knowledge about any language, L, from an informant who need not be a trained linguist. That knowledge then serves as the static resources for an L-to-English translation system. The KE methodology used throughout Boas is driven by a resident inventory of parameters, value sets, and means of their realisation for a wide range of language phenomena. MOD is one of those parameters, whose values are the inventory of attested and not yet attested moods (e.g. indicative, conditional, imperative), and whose realisations include flective morphology, agglutinating morphology, isolating morphology, words, phrases and constructions. Developing the MOD elicitation procedures for Boas amounted to wedding the extensive theoretical and descriptive research on MOD with practical approaches to guiding an untrained informant through this non-trivial task. We believe that our experience in building the MOD module of Boas offers insights not only into cross-linguistic aspects of MOD that have not previously been detailed in the natural language processing literature, but also into KE methodologies that could be applied more broadly.

If you wanted to make some money out of natural language processing, an appropriate strategy might be to identify an area of technology that was relatively mature—one where the more fundamental technical problems had been resolved through a significant amount of research activity—and then identify potential applications for that technology.