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<Paper uid="C04-1054">
  <Title>Using knowledge from WordNet for conceptual</Title>
  <Section position="4" start_page="0" end_page="0" type="metho">
    <SectionTitle>
4 Mismatches in Doctor-Patient
Communication
</SectionTitle>
    <Paragraph position="0"> The skills of a physician in general practice comprise the ability to acquire relevant and reliable information through communication with patients through the use of non-expert language and to convey diagnostic and therapeutic information in ways tailored to the individual patient.</Paragraph>
    <Paragraph position="1"> Since the physician, too, is a member of the wider community of non-experts, and continues to use the non-expert language for everyday purposes, one might assume that there are no difficulties in principle keeping him from being able to formulate medical knowledge in a vocabulary that the patient can understand. As (Slaughter, 2002) and (C. A. Smith, et al., 2002) have shown, however, there are limits to this competence. The former examines dialogue between physicians and patients in the form of question-answer pairs, focusing especially on the relations documented in the UMLS Semantic Network. Only some 30% of the relations used by professionals in their answers directly match the relations used by consumers in formulating their questions. An example of one such question-answer pair is taken from (Slaughter, p. 224): Question Text: My seven-year-old son developed a rash today that I believe to be chickenpox. My concern is that a friend of mine had her 10-day-old baby at my home last evening before we were aware of the illness. My son had no contact with the infant, as he was in bed during the visit, but I have read that chickenpox is contagious up to two days prior to the actual rash. Is there cause for concern at this point? Answer Text: (a) Chickenpox is the common name for varicella infection. [...] (b) You are correct in that a person with chickenpox can be contagious for 48 hours before the first vesicle is seen. [...] (c) The fact that your son did not come in close contact with the infant means he most likely did not transmit the virus. (d) Of concern, though, is the fact that newborns are at higher risk of complications of varicella, including pneumonia. [...] (e) There is a very effective means to prevent infection after exposure. A form of antibody to varicella called varicella-zoster immune globulin (VZIG) can be given up to 48 hours after exposure and still prevent disease.</Paragraph>
    <Paragraph position="2"> Such examples illustrate also that there are lexically rooted mismatches in communication (which may in part reflect legal and ethical considerations) between experts and non-experts.</Paragraph>
    <Paragraph position="3"> Professionals often do not re-use the concepts and relations made explicit in the questions put to them by consumers. In our example, the questioner requests a yes/no-judgment on the possibility of contagion in a 10-day-old baby. In fact, however, only section (c) of the answer responds to this question, and this in a way which involves multiple departures from the type of non-expert language which the questioner can be presumed to understand. Rather, physicians expand the range of concepts and relations addressed (for example through discussion of issues of prevention, etc.).</Paragraph>
    <Paragraph position="4"> In all cases, the information source, whether it be a primary care physician or an online information system, must respond primarily with generic information (i.e. with information that is independent of case or context), and this is so even where requests relate to specific and episodic phenomena (occurrences of pain, fever, reactions to drugs, etc.). (Patel, et al., 2002) In our example, all sections except for (c) are of this generic kind. They contain answers in the form of context-independent statements about causality, about types of persons or diseases, about typical or possible courses of a disease. MFN is accordingly designed to map the generic medical information which non-experts are able to understand.</Paragraph>
  </Section>
  <Section position="5" start_page="0" end_page="0" type="metho">
    <SectionTitle>
5 Non-Expert Language in Online
Communication
</SectionTitle>
    <Paragraph position="0"> Understanding patients requires both explicit medical knowledge and tacit linguistic competence dispersed across large numbers of more or less isolated practitioners. This is not a problem so long as this knowledge is to be applied locally, in face-to-face communication with patients. However, as a result of recent developments in technology, including telemedicine and internet-based medical query systems, we now face a situation where such dispersed, practical (human) knowledge does not suffice.</Paragraph>
    <Paragraph position="1"> (Ely, et al., 2000) and (Jacquemart and Zweigenbaum, 2003) have shown that clinical questions are expressed in a small number of different syntactic-semantic patterns (about 60 patterns account for 90% of the questions). Such yes/no questions are of the forms: Do hair dyes cause cancer?, Can I use aspirin to treat a hangover? With the right sort of information resource, questions such as these can easily be transformed automatically into statements providing correct answers: Hair dyes can cause bladder cancer, Aspirin doesn't help in case of a hangover , and these answers can be linked further to relevant and authoritative sources.</Paragraph>
    <Paragraph position="2"> MEDLINEplus is described in its online documentation as a source of medical information for both experts and non-experts 'that is authoritative and up to date.' Enquirers can use MEDLINEplus like a dictionary, choosing health topics by keywords. Alternatively, they can use the system's search feature to gain access to a database of relevant online documents selected for reliability and accessibility on the basis of pre-established criteria.</Paragraph>
    <Paragraph position="3"> Table 1 shows the problems that can arise when a system fails to take account of the special features of the knowledge and vocabulary of typical non-expert users. Here success in finding the needed information depends too narrowly on the precise formulation of the query text. Thus tremble and trembling call forth different responses (one lists caffeine, the other phobias), even though the terms in question differ only in a morphological affix that does not involve a meaning distinction. Such problems are characteristic of information services of this kind. Experienced internet users are of course familiar with the limitations of search engines, and so they are able to manipulate their query texts in order to get more and better results. Even experienced users, however, will not be able to overcome the arbitrary sensitivities of an information system, and the latter cannot have the goal of bringing non-experts' ways of using language into line with that of the system.</Paragraph>
  </Section>
  <Section position="6" start_page="0" end_page="0" type="metho">
    <SectionTitle>
6 Corpus- and Fact-Based Approaches to
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Information Retrieval
</SectionTitle>
      <Paragraph position="0"> (Patel, et al., 2002) make clear that if a medical information system is to mediate between experts and non-experts, then it must rest on an understanding of both expert and non-expert medical vocabulary. But terms, or word forms, are not always associated with word meanings in a clear-cut and unambiguous fashion; and the problem of polysemy is compounded when different speaker populations are involved. A lexical database must represent all and only the meanings of each given term in such a way that these meanings can be clearly discriminated and mapped onto word occurrences in natural text and speech. Achieving these ends is one of the hardest challenges facing both theoretical and applied linguistic science today. It is generally agreed that the meanings of highly polysemous terms cannot be discriminated without consideration of their contexts (e.g., Pustejovsky, 1995). People manage polysemy without apparent difficulties; but modeling human speakers' capacity for lexical disambiguation in automatic language processing tasks is hard. The idea underlying the present proposal draws on currently emerging NLP methodologies that harness the ability of powerful and fast computers to store and manipulate both lexical databases and large collections of text collections or corpora. The strategy is to train automatic systems on large numbers of semantically annotated sentences that are naturally used and understood by human beings, and to exploit standard pattern-recognition and statistical techniques for purposes of disambiguation. Words and the representation of their senses, stored in lexical databases, can be linked for this purpose to specific occurrences in corpora.</Paragraph>
    </Section>
  </Section>
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