Nancy Green

A HYBRID REASONING MODEL FOR INDIRECT ANSWERS

This paper presents our implemented computational model for interpreting and generating indirect answers to Yes-No questions. Its main features are 1) a discourse-plan-based approach to implicature, 2) a reversible architecture for generation and interpretation, 3) a hybrid reasoning model that employs both plan inference and logical inference, and 4) use of stimulus conditions to model a speakers motivation for providing appropriate, unrequested information. The model handles a wider range of types of indirect answers than previous computational models and has several significant advantages.

A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients

We developed a Bayesian network coding scheme for annotating biomedical content in layperson-oriented clinical genetics documents. The coding scheme supports the representation of probabilistic and causal relationships among concepts in this domain, at a high enough level of abstraction to capture commonalities among genetic processes and their relationship to health. We are using the coding scheme to annotate a corpus of genetic counseling patient letters as part of the requirements analysis and knowledge acquisition phase of a natural language generation project. This paper describes the coding scheme and presents an evaluation of intercoder reliability for its tag set. In addition to giving examples of use of the coding scheme for analysis of discourse and linguistic features in this genre, we suggest other uses for it in analysis of layperson-oriented text and dialogue in medical communication.

Saying it in graphics: from intentions to visualizations

The authors propose a methodology for automatically realizing communicative goals in graphics. It features a task model that mediates the communicative intent and the selection of graphical techniques. The methodology supports the following functions: isolating assertions presentable in graphics; mapping such assertions into tasks for the potential reader, and selecting graphical techniques that support those tasks. They illustrate the methodology by redesigning a textual argument into a multimedia one with the same rhetorical and content structures but employing graphics to achieve some of the intentions.

A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention

This paper presents a model of perceptual task effort for use in hypothesizing the message that a bar chart is intended to convey. It presents our rules, based on research by cognitive psychologists, for estimating perceptual task effort, and discusses the results of an eye tracking experiment that demonstrates the validity of our model. These rules comprise a model that captures the relative difficulty that a viewer would have performing one perceptual task versus another on a specific bar chart. The paper outlines the role of our model of relative perceptual task effort in recognizing the intended message of an information graphic. Potential applications of this work include using this message to provide (1) a more complete representation of the content of the document to be used for searching and indexing in digital libraries, and (2) alternative access to the information graphic for visually impaired users or users of low-bandwidth environments.

CONVERSATIONAL IMPLICATURES IN INDIRECT REPLIES

In this paper we present algorithms for the interpretation and generation of a kind of particularized conversational implicature occurring in certain indirect replies. Our algorithms make use of discourse expectations, discourse plans, and discourse relations. The algorithms calculate implicatures of discourse units of one or more sentences. Our approach has several advantages. First, by taking discourse relations into account, it can capture a variety of implicatures not handled before. Second, by treating implicatures of discourse units which may consist of more than one sentence, it avoids the limitations of a sentence-at-a-time approach. Third, by making use of properties of discourse which have been used in models of other discourse phenomena, our approach can be integrated with those models. Also, our model permits the same information to be used both in interpretation and generation.

Towards Creation of a Corpus for Argumentation Mining the Biomedical Genetics Research Literature

Argumentation mining involves automatically identifying the premises, conclusion, and type of each argument as well as relationships between pairs of arguments in a document. We describe our plan to create a corpus from the biomedical genetics research literature, annotated to support argumentation mining research. We discuss the argumentation elements to be annotated, theoretical challenges, and practical issues in creating such a corpus.

Identifying Argumentation Schemes in Genetics Research Articles

This paper presents preliminary work on identification of argumentation schemes, i.e., identifying premises, conclusion and name of argumentation scheme, in arguments for scientific claims in genetics research articles. The goal is to develop annotation guidelines for creating corpora for argumentation mining research. This paper gives the specification of ten semantically distinct argumentation schemes based on analysis of argumentation in several journal articles. In addition, it presents an empirical study on readers’ ability to recognize some of the argumentation schemes.

Natural language generation of biomedical argumentation for lay audiences

This article presents an architecture for natural language generation of biomedical argumentation. The goal is to reconstruct the normative arguments that a domain expert would provide, in a manner that is transparent to a lay audience. Transparency means that an arguments structure and functional components are accessible to its audience. Transparency is necessary before an audience can fully comprehend, evaluate or challenge an argument, or re-evaluate it in light of new findings about the case or changes in scientific knowledge. The architecture has been implemented and evaluated in the Genetics Information Expression Assistant, a prototype system for drafting genetic counselling patient letters. Argument generation makes use of abstract argumentation schemes. Derived from the analysis of arguments used in genetic counselling, these mainly causal argument patterns refer to abstract properties of qualitative causal domain models.

Generation of Biomedical Arguments for Lay Readers

This paper presents the design of a discourse generator that plans the content and organization of lay-oriented genetic counseling documents containing arguments, and an experiment to evaluate the arguments. Due to the separation of domain, argument, and genre-specific concerns and the methodology used for acquiring a domain model, this approach should be applicable to argument generation in other domains.

An Integrated Discourse Recipe-Based Model for Task-Oriented Dialogue

We present an integrated discourse recipe-based model (DRM) for dialogue generation and interpretation. Discourse recipes are generalizations of discourse plans. The DRM has been implemented as part of a conversational agent that supports task-oriented dialogue between human and artificial pilots. The design of the DRM has been strongly influenced by its implementation in the Soar cognitive architecture. In the DRM, discourse recipes are acquired as a side effect of dialogue planning. The discourse recipes can be used for generation and interpretation in future situations in place of planning and reasoning from first principles. We describe the motivation for a discourse recipe-based approach and present the design of the DRM.