Marjorie McShane

Bootstrapping morphological analyzers by combining human elicitation and machine learning

This paper presents a semiautomatic technique for developing broad-coverage finite-state morphological analyzers for use in natural language processing applications. It consists of three componentselicitation of linguistic information from humans, a machine learning bootstrapping scheme, and a testing environment. The three components are applied iteratively until a threshold of output quality is attained. The initial application of this technique is for the morphology of low-density languages in the context of the Expedition project at NMSU Computing Research Laboratory. This elicit-build-test technique compiles lexical and inectional information elicited from a human into a finite-state transducer lexicon and combines this with a sequence of morphographemic rewrite rules that is induced using transformation-based learning from the elicited examples. The resulting morphological analyzer is then tested against a test set, and any corrections are fed back into the learning procedure, which then builds an improved analyzer.

Using a Natural Language Understanding System to Generate Semantic Web Content

We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over fifteen years, uses a specia lly constructed NLP-oriented ontology and an ontologicalsemantic lexicon to translate English text into a custom ontology-motivated knowledge representation language, the language of text meaning representations (TMRs). OntoSem concentrates on a variety of ambiguity resolution tasks as well as processing unexpected input and reference. To adapt OntoSem’s representation to the Semantic Web, we developed a translation system, OntoSem2OWL, between the TMR language into the Semantic Web language OWL. We next used OntoSem and OntoSem2OWL to support SemNews, an e xperimental web service that monitors RSS news sources, processes the summaries of the news stories and publishes a structured representation of the meaning of the text in the news story.

Semantically Rich Human-Aided Machine Annotation

This paper describes a semantically rich, human-aided machine annotation system created within the Ontological Semantics (OntoSem) environment using the DEKADE toolset. In contrast to mainstream annotation efforts, this method of annotation provides more information at a lower cost and, for the most part, shifts the maintenance of consistency to the system itself. In addition, each tagging effort not only produces knowledge resources for that corpus, but also leads to improvements in the knowledge environment that will better support subsequent tagging efforts.

Knowledge-Based Modeling and Simulation of Diseases with Highly Differentiated Clinical Manifestations

This paper presents the cognitive model of gastroesophageal reflux disease (GERD) developed for the Maryland Virtual Patient simulation and mentoring environment. GERD represents a class of diseases that have a large number of clinical manifestations. Our model at once manages that complexity while offering robust automatic function in response to open-ended user actions. This ontologically grounded model is largely based on script-oriented representations of causal chains reflecting the actual physiological processes in virtual patients. A detailed description of the GERD model is presented along with a high-level description of the environment for which it was developed.

An NLP Lexicon as a Largely Language-Independent Resource

This paper describes salient aspects of the OntoSem lexicon of English, a lexicon whose semantic descriptions can either be grounded in a language-independent ontology, rely on extra-ontological expressive means, or exploit a combination of the two. The variety of descriptive means, as well as the conceptual complexity of semantic description to begin with, necessitates that OntoSem lexicons be compiled primarily manually. However, once a semantic description is created for a lexeme in one language, it can be reused in others, often with little or no modification. Said differently, the challenge in building a semantic lexicon is describing semantics; once the semantics are described, it is relatively straightforward to connect given meanings to the appropriate head words in other languages. In this paper we provide a brief overview of the OntoSem lexicon and processing environment, orient our approach to lexical semantics among others in the field, and describe in more detail what we mean by the largely language-independent lexicon. Finally, we suggest reasons why our resources might be of interest to the larger community.

Evaluating the performance of the OntoSem semantic analyzer

This paper describes an innovative evaluation regimen developed for the text meaning representations (TMRs) produced by the Ontological Semantic (OntoSem) general purpose syntactic-semantic analyzer. The goal of evaluation is not only to determine the quality of TMRs for given texts, but also to assign blame for various classes of errors, thus suggesting directions for continued work on both knowledge resources and processors. The paper includes descriptions of the OntoSem processing environment, the evaluation regime itself and results from our first evaluation effort.

Embedding Knowledge Elicitation and MT Systems within a Single Architecture

This paper describes Expedition, an environment designed to facilitate the quick ramp-up of MT systems from practically any alphabetic language (L) into English. The central component of Expedition is a knowledge-elicitation system that guides a linguistically naive bilingual speaker through the process of describing L in terms of its ecological, morphological, grammatical, lexical, and transfer information. Expedition also includes a module for converting the elicited information into the format expected by the underlying MT system and an MT engine that relies on both the elicited knowledge and resident knowledge about English. The Expedition environment is integrated using a configuration and control system. Expedition represents an innovative approach to answering the need for rapid-configuration MT by preparing an MT system in which the only missing link is information about L, which is elicited in a structured fashion such that it can be directly exploited by the system. In this paper we report on the current state of Expedition with an emphasis on the knowledge elicitation system.

Inconsistency as a diagnostic tool in a society of intelligent agents

OBJECTIVE To use the detection of clinically relevant inconsistencies to support the reasoning capabilities of intelligent agents acting as physicians and tutors in the realm of clinical medicine. METHODS We are developing a cognitive architecture, OntoAgent, that supports the creation and deployment of intelligent agents capable of simulating human-like abilities. The agents, which have a simulated mind and, if applicable, a simulated body, are intended to operate as members of multi-agent teams featuring both artificial and human agents. The agent architecture and its underlying knowledge resources and processors are being developed in a sufficiently generic way to support a variety of applications. RESULTS We show how several types of inconsistency can be detected and leveraged by intelligent agents in the setting of clinical medicine. The types of inconsistencies discussed include: test results not supporting the doctors hypothesis; the results of a treatment trial not supporting a clinical diagnosis; and information reported by the patient not being consistent with observations. We show the opportunities afforded by detecting each inconsistency, such as rethinking a hypothesis, reevaluating evidence, and motivating or teaching a patient. CONCLUSIONS Inconsistency is not always the absence of the goal of consistency; rather, it can be a valuable trigger for further exploration in the realm of clinical medicine. The OntoAgent cognitive architecture, along with its extensive suite of knowledge resources an processors, is sufficient to support sophisticated agent functioning such as detecting clinically relevant inconsistencies and using them to benefit patient-centered medical training and practice.

Mood and modality: out of theory and into the fray

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.

Resolving Paraphrases to Support Modeling Language Perception in an Intelligent Agent

When interacting with humans, intelligent agents must be able not only to understand natural language inputs but also to remember them and link their content with the contents of their memory of event and object instances. As inputs can come in a variety of forms, linking to memory can be successful only when paraphrasing relations are established between the meaning of new input and the content of the agents memory. This paper discusses a variety of types of paraphrases relevant to this task and describes the way we implement this capability in a virtual patient application.