Advances in language engineering may be dependent on theoretical principles originating from linguistics, since both share a common object of enquiry, natural language structures. We outline an approach to term extraction that rests on theoretical claims about the structure of words. We use the structural properties of compound words to specifically elicit the sets of terms defined by type hierarchies such as hyponymy and meronymy. The theoretical claims revolve around the head-modifier principle, which determines the formation of a major class of compounds. Significantly it has been suggested that the principle operates in languages other than English. To demonstrate the extendibility of our approach beyond English, we present a case study of term extraction in Chinese, a language whose written form is the vehicle of communication for over 1.3 billion language users, and therefore has great significance for the development of language engineering technologies.

Named entity recognition identifies and classifies entity names in a text document into some predefined categories. It resolves the “who”, “where” and “how much” problems in information extraction and leads to the resolution of the “what” and “how” problems in further processing. This paper presents a Hidden Markov Model (HMM) and proposes a HMM-based named entity recognizer implemented as the system PowerNE. Through the HMM and an effective constraint relaxation algorithm to deal with the data sparseness problem, PowerNE is able to effectively apply and integrate various internal and external evidences of entity names. Currently, four evidences are included: (1) a simple deterministic internal feature of the words, such as capitalization and digitalization; (2) an internal semantic feature of the important triggers; (3) an internal gazetteer feature, which determines the appearance of the current word string in the provided gazetteer list; and (4) an external macro context feature, which deals with the name alias phenomena. In this way, the named entity recognition problem is resolved effectively. PowerNE has been benchmarked with the Message Understanding Conferences (MUC) data. The evaluation shows that, using the formal training and test data of the MUC-6 and MUC-7 English named entity tasks, and it achieves the F-measures of 96.6 and 94.1, respectively. Compared with the best reported machine learning system, it achieves a 1.7 higher F-measure with one quarter of the training data on MUC-6, and a 3.6 higher F-measure with one ninth of the training data on MUC-7. In addition, it performs slightly better than the best reported handcrafted rule-based systems on MUC-6 and MUC-7.

With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a large bracketed corpus since late 1998. The first two installments of the corpus, 250 thousand words of data, fully segmented, POS-tagged and syntactically bracketed, have been released to the public via LDC (www.ldc.upenn.edu). In this paper, we discuss several Chinese linguistic issues and their implications for our treebanking efforts and how we address these issues when developing our annotation guidelines. We also describe our engineering strategies to improve speed while ensuring annotation quality.

Multimodal interfaces are systems that allow input and/or output to be conveyed over multiple channels such as speech, graphics, and gesture. In addition to parsing and understanding separate utterances from different modes such as speech or gesture, multimodal interfaces also need to parse and understand composite multimodal utterances that are distributed over multiple input modes. We present an approach in which multimodal parsing and understanding are achieved using a weighted finite-state device which takes speech and gesture streams as inputs and outputs their joint interpretation. In comparison to previous approaches, this approach is significantly more efficient and provides a more general probabilistic framework for multimodal ambiguity resolution. The approach also enables tight-coupling of multimodal understanding with speech recognition. Since the finite-state approach is more lightweight in computational needs, it can be more readily deployed on a broader range of mobile platforms. We provide speech recognition results that demonstrate compensation effects of exploiting gesture information in a directory assistance and messaging task using a multimodal interface.

Based on constraint optimization techniques, an architecture for robust parsing of natural language utterances has been developed. The resulting system is able to combine possibly contradicting evidence from a variety of information sources, using a plausibility-based arbitration procedure to derive fairly rich structural representations, comprising aspects of syntax, semantics and other description levels of language. The results of a series of experiments are reported which demonstrate the high potential for robust behaviour with respect to ungrammaticality, incomplete utterances, and temporal pressure.

This paper describes a novel approach to multi-document summarization, which explicitly addresses the problem of detecting, and retaining for the summary, multiple themes in document collections. We place equal emphasis on the processes of theme identification and theme presentation. For the former, we apply Iterative Residual Rescaling (IRR); for the latter, we argue for graphical display elements. IRR is an algorithm designed to account for correlations between words and to construct multi-dimensional topical space indicative of relationships among linguistic objects (documents, phrases, and sentences). Summaries are composed of objects with certain properties, derived by exploiting the many-to-many relationships in such a space. Given their inherent complexity, our multi-faceted summaries benefit from a visualization environment. We discuss some essential features of such an environment.

This paper reports on a number of experiments which are designed to investigate the extent to which current NLP resources are able to syntactically and semantically analyse biomedical text. We address two tasks: (a) parsing a real corpus with a hand-built wide-coverage grammar, producing both syntactic analyses and logical forms and (b) automatically computing the interpretation of compound nouns where the head is a nominalisation (e.g. hospital arrival means an arrival at hospital, while patient arrival means an arrival of a patient). For the former task we demonstrate that flexible and yet constrained pre-processing techniques are crucial to success: these enable us to use part-of-speech tags to overcome inadequate lexical coverage, and to package up complex technical expressions prior to parsing so that they are blocked from creating misleading amounts of syntactic complexity. We argue that the XML-processing paradigm is ideally suited for automatically preparing the corpus for parsing. For the latter task, we compute interpretations of the compounds by exploiting surface cues and meaning paraphrases, which in turn are extracted from the parsed corpus. This provides an empirical setting in which we can compare the utility of a comparatively deep parser vs. a shallow one, exploring the trade-off between resolving attachment ambiguities on the one hand and generating errors in the parses on the other. We demonstrate that a model of the meaning of compound nominalisations is achievable with the aid of current broad-coverage parsers.

One way to keep in touch with what is happening in the commercial speech and language technology world is to pay occasional visits to the websites of HLT Central (at www.hltcentral.org) and LT World (at www.lt-world.org). Both sites provide links to news stories and press releases from companies and other organizations active in the area. The people who run these sites trawl the web for news stories of relevance, saving you the trouble of doing that yourself.

Journal of Natural Language Engineering, volume 9, issue 4, pp. 365–80, December 2003

Spelling errors that happen to result in a real word in the lexicon cannot be detected by a conventional spelling checker. We present a method for detecting and correcting many such errors by identifying tokens that are semantically unrelated to their context and are spelling variations of words that would be related to the context. Relatedness to context is determined by a measure of semantic distance initially proposed by Jiang and Conrath (1997). We tested the method on an artificial corpus of errors; it achieved recall of 23–50% and precision of 18–25%.