Automatic Mapping Clinical Notes to Medical - RMIT University

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

Algorithm 1 The algorithm of combine, which merges two coverages to produce a new coverage. 1: Let I be the set of IDs of the training instances 2: Let Coverage 1 and Coverage 2 be the two coverages to be merged 3: Let NewCoverage be the combined coverage of Coverage 1 and Coverage 2 4: for i ∈ I do 5: NewCoverage[i] = max(Coverage 1 [i], Coverage 2 [i]) 6: end for 7: Return NewCoverage Algorithm 2 The incremental process of determining the final set of feature types 1: Let I be the set of IDs of the training instances 2: CurrentCoverage = {(i → 0.0)|i ∈ I} 3: Let F be the set of feature types 4: Combine(coverage 1 , coverage 2 ) = {(i → max(coverage 1 [i], coverage 2 [i]))|i ∈ I} 5: while True do 6: NCs ← {} ⊲ Initialize a temporary list to hold the new combined coverages 7: for fi ∈ F do 8: NewCoverage i ← Combine(CurrentCoverage, Coverage fi ) 9: Add NewCoverage i to NCs 10: end for 11: Let NewCoverage ⋆ ∈ NCs be the one with highest overall coverage. ⊲ Break tie with the lowest applicability. 12: if CurrentCoverage has the same overall coverage as NewCoverage ⋆ then 13: Break 14: end if 15: CurrentCoverage ← NewCoverage ⋆ 16: end while Individual Feature ranking Feat. Coverage Majority Dataset Fully FS Maxent Fully All Maxent Fully Class Oracled Oracled Oracled Auto. Features Oracled Auto. Baseline SENSEVAL-2 .623 .615 .556 .540 .574 .588 .583 .396 MSC .774 .743 .602 .577 .690 .712 .712 ∗ .617 Table 2: Disambiguation accuracies with SRL features ( ∗ indicates significantly higher performance than the all features baseline (paired t-test, p > 90%)) Individual Feature ranking Feat. Coverage Majority Dataset Fully FS Maxent Fully All Maxent Fully Class Oracled Oracled Oracled Auto. Features Oracled Auto. Baseline SENSEVAL-2 .606 .595 .544 .529 .558 .583 .576 .396 MSC .780 .747 .554 .532 .714 .721 .693 .617 Table 3: Disambiguation accuracies without SRL features 144
that for most verbs, there was very little correlation between the training data and the test data in terms of the value of N verses the disambiguation performance. This is why this algorithm consistently selected suboptimal feature sets which resulted in the poor performance. This mismatching problem is especially severe for the MSC corpus which contains many verb senses that occur only once. On the other hand, the performance of the coverage based algorithm tends to be similar to or better than that of using all the features. Recall that this algorithm selects feature types in such a way that the coverage of the final feature set is maximized. As a result, even feature types which performed poorly on the training set may be selected as long as they performed well on some of the training instances that other features performed poorly on. Therefore, the features chosen by this algorithm are less likely to overfit the training data. Overall, it can be observed that for both datasets, most of our automatic classifiers outperform the majority class baseline, which is very encouraging. For the SENSEVAL-2 dataset, the classifiers with SRL features consistently outperform those without. However, for the MSC dataset, the results of the nested cross validation showed that the performance of the automatic classifiers with the SRL features does not consistently outperform those without the SRL features; the oracled classifiers constructed without the SRL features actually consistently outperformed those with the SRL features. The differences between the results obtained from the two datasets make it difficult to conclude categorically whether SRL can indeed help VSD. However, a closer examination of the datasets reveals that errors in the output of the semantic role labeler, the intransitivity of the verbs, unresolved anaphors, and verbal multiword expressions (verbal MWEs) are the main reasons for the lack of positive contribution from the SRL features. Most of the verbs on which SRL features performed poorly are intransitive, which means that only one argument type semantic role is available – the subject or the agent role, which mostly occurs to the left of the verb. However, the feature ranking algorithm showed that most of the useful features occur to the right of the verb, which is why SRL features tend to perform poorly on in- 145 transitive verbs. The unresolved anaphors also limited the effectiveness of SRL features because they carry almost no disambiguation information, no matter which semantic role they take, and they occur in a significant number of sentences. The anaphor problem is slightly more pronounced in the movie script corpus, because its texts tend to describe consecutive actions performed by the same actor(s) and involving the same objects in the scene, and therefore anaphors tend to occur more frequently. Verbal MWEs such as take off are not detected as a single lexical item, and the verbs themselves tend to have no suitable sense as far as WordNet is concerned. However, they often occur more than once in the data, and since the annotators were forced always to pick at least one sense, these expressions tend to end up as noise in the data. Finally, it is also possible that the lack of training data in the MSC corpus contributed to the poor performance, since almost every verb in the MSC corpus contains two or more senses which occur less than twice. 7 Conclusions and Future Work In this paper, we have presented our research on using multi-semantic-role based selectional preferences obtained from a state-of-the-art semantic role labeler. We have shown that this particular type of selectional preferences can indeed improve the performance of verb sense disambiguation. However, this improvement still depends on the performance of the semantic role labeler, the transitivity of the verbs, the resolution of anaphors, and the identification of verbal MWEs. In future research, we hope to focus on integrating more competent anaphora resolution systems and verbal MWE detectors into our existing VSD framework, and investigating how to mitigate the errors in the semantic role labeler. Acknowledgements We would like to thank the three anonymous reviewers for their valuable input on this research. The research in this paper has been supported by the Australian Research Council through Discovery Project grant number DP0663879, and also NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation.
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Proceeding of the Australasian Lang
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Program Chairs: Committees Lawrence
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Towards the Evaluation of Referring
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The cat ate the hat that I made NP/
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Figure 2: Cells affected by adding
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were used because the accuracy for
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TIME ACC COVER FAIL RATE % secs % %
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model. We explore different estimat
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S.S. Source Sense Source S.S. Acc.
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acy. The difference in correctly as
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Experiments with Sentence Classific
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datasets with skewed distribution t
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words produced by the feature selec
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Sentence Class NB DT SVM Apology 1.
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Computational Semantics in the Natu
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S[sem = ] -> NP[sem=?subj] VP[sem=?
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Any string which cannot be decompos
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satisfy(Formula,model(D,F),G,pos):n
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Classifying Speech Acts using Verba
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The principles are dichotomous —
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our results. In classifying only th
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Word Relatives in Context for Word
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B. Snyder and M. Palmer. 2004. The
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in English, neither the determiner
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Feature type Positions/description
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Miriam Butt, Helge Dyvik, Tracy Hol
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ently mostly solved by employing hu
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Figure 1: Augmented SCT Lexicon Tok
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medical terms can be composed by ad
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References Aronson, A. R. (2001). E
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technique to most question types, i
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User Question Analyser QA System Se
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Figure 2: Precision Figure 3: Cover
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Analysis and Review. Springer-Verla
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2 Background 2.1 Pronoun Reference
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ous accounts of the effects of cohe
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Page 152 and 153: References Timothy Baldwin and Fran
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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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