OBJECTIVE: This paper describes a successful approach to de-identification that was developed to participate in a recent AMIA-sponsored challenge evaluation. METHOD: Our approach focused on rapid adaptation of existing toolkits for named entity recognition using two existing toolkits, Carafe and LingPipe. RESULTS: The "out of the box" Carafe system achieved a very good score (phrase F-measure of 0.9664) with only four hours of work to adapt it to the de-identification task. With further tuning, we were able to reduce the token-level error term by over 36% through task-specific feature engineering and the introduction of a lexicon, achieving a phrase F-measure of 0.9736. CONCLUSIONS: We were able to achieve good performance on the de-identification task by the rapid retargeting of existing toolkits. For the Carafe system, we developed a method for tuning the balance of recall vs. precision, as well as a confidence score that correlated well with the measured F-score.

We use machine learning techniques to find the best combination of local focus and lexical distance features for identifying the anchor of mereological bridging references.We find that using first mention, utterance distance, and lexical distance computed using either Google or WordNet results in an accuracy significantly higher than obtained in previous experiments.

Centering theory is the best-known framework for theorizing about local coherence and salience; however, its claims are articulated in terms of notions which are only partially specified, such as “utterance,” “realization,” or “ranking.” A great deal of research has attempted to arrive at more detailed specifications of these parameters of the theory; as a result, the claims of centering can be instantiated in many different ways. We investigated in a systematic fashion the effect on the theory's claims of these different ways of setting the parameters. Doing this required, first of all, clarifying what the theory's claims are (one of our conclusions being that what has become known as “Constraint 1” is actually a central claim of the theory). Secondly, we had to clearly identify these parametric aspects: For example, we argue that the notion of “pronoun” used in Rule 1 should be considered a parameter. Thirdly, we had to find appropriate methods for evaluating these claims. We found that while the theory's main claim about salience and pronominalization, Rule 1—a preference for pronominalizing the backward-looking center (CB)—is verified with most instantiations, Constraint 1–a claim about (entity) coherence and CB uniqueness—is much more instantiation-dependent: It is not verified if the parameters are instantiated according to very mainstream views (“vanilla instantiation”), it holds only if indirect realization is allowed, and is violated by between 20% and 25% of utterances in our corpus even with the most favorable instantiations. We also found a trade-off between Rule 1, on the one hand, and Constraint 1 and Rule 2, on the other: Setting the parameters to minimize the violations of local coherence leads to increased violations of salience, and vice versa. Our results suggest that “entity” coherence—continuous reference to the same entities—must be supplemented at least by an account of relational coherence.

The definitions of the basic concepts, rules, and constraints of centering theory involve underspecified notions such as 'previous utterance', 'realization', and 'ranking'. We attempted to find the best way of defining each such notion among those that can be annotated reliably, and using a corpus of texts in two domains of practical interest. Our main result is that trying to reduce the number of utterances without a backward-looking center (CB) results in an increased number of cases in which some discourse entity, but not the CB, gets pronominalized, and viceversa.

This paper describes the latest version of the SOLE concept-tospeech system, which uses linguistic information provided by a natural language generation system to improve the prosody of synthetic speech. We discuss the types of linguistic information that prove most useful and the implications for text-to-speech systems. 1. INTRODUCTION The purpose of the SOLE project is to make use of automaticallygenerated, high-level linguistic information to improve the quality of the intonation of synthetic speech. After choosing an initial set of linguistic constructs thought to have some influence on prosody, we developed an SGML-based mark-up language to serve as a general interface between NLG and speech synthesis systems, and trained our synthesis system to recognise correlations between the mark-up and intonational contours so that it can make use of this mark-up when synthesising. As a result, many of the errors that the synthesiser makes with regard to knowing when to accent or deaccent ...