Automatic Mapping Clinical Notes to Medical - RMIT University

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$journal of latex class files, vol. 6, no. 1, january ... - RMIT University$

a given word sequence Sorig as a perturbed word sequence Spert (which we write as Sorig → Spert.) The database may contain several different ways of perturbing the word sequence Sorig. The relative frequencies of the different perturbations can be used to estimate perturbation probabilities, as follows: P(Sorig→Spert) ≈ count(Sorig→Spert) count(Sorig→ ) (The denominator holds the count of all perturbations of Sorig in the error database.) Naturally, if we want useful counts, we cannot look up a complete sentence in the error database. Instead, we work with an n-gram model, in which the probability of a perturbed sentence is approximated by the probability of a perturbation in an nword sequence centred on the perturbed word. In our model, the best approximation is a perturbed trigram; thus if the student’s input string is I saw dog, the probability of a perturbation creating I saw the dog is given by count(saw GAP dog → saw the dog) count(saw GAP dog → ) Again, it is unlikely these counts are going to be high enough, so we also derive additional backedoff estimates, two based on bigrams and one based on unigrams: count(GAP dog → the dog) count(GAP dog → ) count(saw GAP → saw the) count(saw GAP → ) count(GAP → the) count(GAP → ) See van der Ham (2005) for details of the backoff and discounting schemes used to derive a single probability from these different approximations. 5.2 Integrating perturbations into utterance disambiguation When an incoming utterance is received, a set of perturbations is generated. Naturally, we do not want to hypothesise all possible perturbations, but only the most likely ones—i.e. those whose score exceeds some threshold. The threshold is currently set at 0.8. We also want to keep the number of hypothesised perturbations to a minimum. Currently we only allow one perturbation per utterance, except for a special class of particularly 126 common spelling mistakes involving placement of macron accents, of which we allow three. (Where there are multiple perturbations, their scores are multiplied.) Each perturbed sentence is passed to the utterance interpretation module. Most of the perturbations result in ungrammatical sentences, and so fail at the first hurdle. However, for any which can be parsed, one or more full updates is created. The complete set of updates produced from the original sentence and all its selected perturbations are then passed to the disambiguation module. The disambiguation module must now take into account both the interpretation score and the perturbation score when deciding between alternative interpretations. At any level, the module computes an aggregate score Sagg, which is the product of the perturbation score Spert (weighted by a perturbation penalty) and the interpretation score Sint: Sagg = Spert × Sint pert penalty (The perturbation penalty is a system parameter, which determines the importance of perturbation scores relative to interpretation scores; it is currently set to 1.) To choose between alternative interpretations at a given level, we now take all interpretations whose aggregate score is within the winning margin of the highest aggregate score. 5.3 Responding to the user’s utterance After the utterance disambiguation process is complete, either a single interpretation remains, or a set of interpretations whose aggregate scores are too close to call at any of the three levels. In either case, how the system responds depends on whether the remaining interpretations derive from the unperturbed utterance or from a perturbed version. If a single interpretation remains, then if it derives from the original utterance, the dialogue manager responds to it in the usual way. However, if it derives from a perturbed utterance, then the system is confident that an error has occurred, and that it knows what the error is. In this case the system enters a subdialogue with the user to address the error. Our system’s current strategy is simply to report the error explicitly: (4) I think you mean [perturbed utterance]. Please try again!
(If the student’s utterance was responding to a forward-looking dialogue act—e.g. a question— the system then reiterates this forward-looking act, to recreate the context for the student’s second attempt.) Note that a good tutor would probably give the student an opportunity to correct her error herself; we are still exploring ways of doing this without irritating the student. If more than one interpretation remains when utterance disambiguation is complete, what happens again depends on where the interpretations come from. If they all come from the unperturbed utterance, an ordinary clarifiction question is asked (see Lurcock (2005) for details of how clarification questions are generated). If they all come from a single perturbed utterance, we simply present the suggested correction, as above; if the student then enters the hypothesised correction, a regular clarification question will be asked. However, it is also possible that the interpretations come from several different perturbations. In this case, we formulate our error feedback as a question: (5) Do you mean [perturbation 1] or (...) or [perturbation n]? 2 interpretations are compared by the disambiguation module, the perturbed version is preferred, because it is cheaper to incorporate into the current dialogue context: the chief refers back to an existing discourse entity, while the chef requires the accommodation of a new one. Here is a final example, this time at the level of whole-word perturbations: (8) S: What is your name? U: Your name is Sally. S: I think you mean ‘My name is Sally’. The error database contains enough instances of the perturbation your→my to cause the system to create this candidate perturbation; an interpretation for this perturbation is thus created alongside that of the original utterance. Again, the interpretation deriving from the perturbation is easier to incorporate into the dialogue context, since it answers the system’s question, so the perturbed sentence is preferred over the original, even though the original contains no syntactic errors. 7 Future work: incorporating a treatment of unknown words 6 Some examples In this section, we give some examples of our error-correction scheme in action. First, a simple character-level correction: (6) U: I am hapy3 The error correction scheme has performed reasonably well in informal user trials. However there is one fairly major problem still to be addressed, relating to unknown words. If a word in the student’s utterance is not found in the system’s lex- S: I think you mean ‘I am happy’. icon, there are two possibilities: either the student has made an error, or the word is one which the system simply does not know. In the current The perturbation here is motivated by syntactic scheme, only the first possibility is considered. well-formedness: the original utterance does not We have already implemented a treatment of parse, but the perturbed utterance does. unknown words, in which the system assumes an Here is another character-level correction, this unknown word is of a lexical type already defined time informed by contextual appropriateness: in the grammar, and proceeds by asking the user (7) U: S: U: S: questions embedding the word in example sen- I saw my chief tences to help identify this type (see van Scha- Okay gen and Knott (2004)). However, word-authoring The chef is happy subdialogues would be a distraction for a student; I think you mean ‘The chief is happy’. and in any case, it is fairly safe to assume that There are two things to note about this example. all unknown words used by the student are proper Firstly, note that the user’s original utterance is names. We therefore use a simpler treatment re- syntactically correct, so a full interpretation will lated to the constraint-relaxation scheme of Fou- be derived for this utterance as well as for the vry (2003), in which the system temporarily adds version perturbing chef to chief. When these two an unknown word to the class of proper names and 2 The question is formulated as a multiple choice question, using the same format as some types of syntactic clarification question. then attempts to reparse the sentence. A successful parse is then interpreted as evidence that the unknown word is indeed a proper name. 127
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Proceeding of the Australasian Lang
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Program Chairs: Committees Lawrence
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Page 166 and 167: Natural Language Processing and XML
Page 168 and 169: Extracting Patient Clinical Profile
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