Researchers and developers of educational software have experimented with natural language processing (NLP) capabilities and related technologies since the 1960's. Automated essay scoring was perhaps the first application of this kind (Page 1966). Over a decade later, Writer's Workbench, a text-editing application, was developed as a tool for classroom teachers (MacDonald, Frase, Gingrich and Keenan 1982). Intelligent tutoring applications, though more in the spirit of artificial intelligence, were also being developed during this time (Carbonell 1970; Brown, Burton and Bell 1974; Stevens and Collins 1977; Burton and Brown 1982; Clancy 1987).

We present the RAGS (Reference Architecture for Generation Systems) framework: a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.

The recent scaling down of mobile device form factors has increased the importance of predictive text entry. It is now also becoming an important communication tool for the disabled. Techniques related to predictive text entry software are discussed in a generalized, language-independent manner. The essence of predictive text entry is twofold, consisting of (1) the design of codes for text entry, and (2) the use of adaptive language models for decoding. Code design is examined in terms of the information-theoretical efficiency. Four adaptive language models are introduced and compared, and experimental results on text entry with these models are shown for English, Thai and Japanese.

Natural Language Generation (NLG) can be used to generate textual summaries of numeric data sets. In this paper we develop an architecture for generating short (a few sentences) summaries of large (100KB or more) time-series data sets. The architecture integrates pattern recognition, pattern abstraction, selection of the most significant patterns, microplanning (especially aggregation), and realisation. We also describe and evaluate SumTime-Turbine, a prototype system which uses this architecture to generate textualsummaries of sensor data from gas turbines.

Folk wisdom holds that incorporating a part-of-speech tagger into a system that performs deep linguistic analysis will improve the speed and accuracy of the system. Previous studies of tagging have tested this belief by incorporating an existing tagger into a parsing system and observing the effect on the speed of the parser and accuracy of the results. However, not much work has been done to determine in a fine-grained manner exactly how much tagging can help to disambiguate or reduce ambiguity in parser output. We take a new approach to this issue by examining the full parse-forest output of a large-scale LFG-based English grammar (Riezler et al. (2002)) running on the XLE grammar development platform (Maxwell and Kaplan (1993); Maxwell and Kaplan (1996)); and partitioning the parse outputs into equivalence classes based on the tag sequences for each parse. If we find a large number of tag-sequence equivalence classes for each sentence, we can conclude that different parses tend to be distinguished by their tags; a small number means that tagging would not help much in reducing ambiguity. In this way, we can determine how much tagging would help us in the best case, if we had the “perfect tagger” to give us the correct tag sequence for each sentence. We show that if a perfect tagger were available, a reduction in ambiguity of about 50% would be available. Somewhat surprisingly, about 30% of the sentences in the corpus that was examined would not be disambiguated, even by the perfect tagger, since all of the parses for these sentences shared the same tag sequence. Our study also helps to inform research on tagging by providing a targeted determination of exactly which tags can help the most in disambiguation.

In spite of difficulty in defining the syllable unequivocally, and controversy over its role in theories of spoken and written language processing, the syllable is a potentially useful unit in several practical tasks which arise in computational linguistics and speech technology. For instance, syllable structure might embody valuable information for building word models in automatic speech recognition, and concatenative speech synthesis might use syllables or demisyllables as basic units. In this paper, we first present an algorithm for determining syllable boundaries in the orthographic form of unknown words that works by analogical reasoning from a database or corpus of known syllabifications. We call this syllabification by analogy (SbA). It is similarly motivated to our existing pronunciation by analogy (PbA) which predicts pronunciations for unknown words (specified by their spellings) by inference from a dictionary of known word spellings and corresponding pronunciations. We show that including perfect (according to the corpus) syllable boundary information in the orthographic input can dramatically improve the performance of pronunciation by analogy of English words, but such information would not be available to a practical system. So we next investigate combining automatically-inferred syllabification and pronunciation in two different ways: the series model in which syllabification is followed sequentially by pronunciation generation; and the parallel model in which syllabification and pronunciation are simultaneously inferred. Unfortunately, neither improves performance over PbA without syllabification. Possible reasons for this failure are explored via an analysis of syllabification and pronunciation errors.

This paper describes in detail an algorithm for the unsupervised learning of natural language morphology, with emphasis on challenges that are encountered in languages typologically similar to European languages. It utilizes the Minimum Description Length analysis described in Goldsmith (2001), and has been implemented in software that is available for downloading and testing.

There has been a contemporary surge of interest in the application of stochastic models of parsing. The use of tree-adjoining grammar (TAG) in this domain has been relatively limited due in part to the unavailability, until recently, of large-scale corpora hand-annotated with TAG structures. Our goals are to develop inexpensive means of generating such corpora and to demonstrate their applicability to stochastic modeling. We present a method for automatically extracting a linguistically plausible TAG from the Penn Treebank. Furthermore, we also introduce labor-inexpensive methods for inducing higher-level organization of TAGs. Empirically, we perform an evaluation of various automatically extracted TAGs and also demonstrate how our induced higher-level organization of TAGs can be used for smoothing stochastic TAG models.

A part-of-speech tagger is a fundamental and indispensable tool in computational linguistics, typically employed at the critical early stages of processing. Although taggers are widely available that achieve high accuracy in very general domains, these do not perform nearly as well when applied to novel specialized domains, and this is especially true with biological text. We present a stochastic tagger that achieves over 97.44% accuracy on MEDLINE abstracts. A primary component of the tagger is its lexicon which enumerates the permitted parts-of-speech for the 10000 words most frequently occurring in MEDLINE. We present evidence for the conclusion that the lexicon is as vital to tagger accuracy as a training corpus, and more important than previously thought.

To respond correctly to a free form factual question given a large collection of text data, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer. This work presents a machine learning approach to question classification. Guided by a layered semantic hierarchy of answer types, we develop a hierarchical classifier that classifies questions into fine-grained classes. This work also performs a systematic study of the use of semantic information sources in natural language classification tasks. It is shown that, in the context of question classification, augmenting the input of the classifier with appropriate semantic category information results in significant improvements to classification accuracy. We show accurate results on a large collection of free-form questions used in TREC 10 and 11.

For one aspect of grammatical annotation, part-of-speech tagging, we investigate experimentally whether the ceiling on accuracy stems from limits to the precision of tag definition or limits to analysts' ability to apply precise definitions, and we examine how analysts' performance is affected by alternative types of semi-automatic support. We find that, even for analysts very well-versed in a part-of-speech tagging scheme, human ability to conform to the scheme is a more serious constraint than precision of scheme definition. We also find that although semi-automatic techniques can greatly increase speed relative to manual tagging, they have little effect on accuracy, either positively (by suggesting valid candidate tags) or negatively (by lending an appearance of authority to incorrect tag assignments). On the other hand, it emerges that there are large differences between individual analysts with respect to usability of particular types of semi-automatic support.