Automatic Mapping Clinical Notes to Medical - RMIT University

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

model. We explore different estimates of the most frequent super-sense, and find that aggregating the counts of the fine-grained senses is a significantly better heuristic for this task. We believe that one of the reasons coarse grained senses are useful is that they largely ameliorate the inter-annotator agreement issues of fine grained sense tagging. Not only are there fewer senses to choose from, but the senses are more distinct, and therefore should be less easily confused. The super-sense tags are therefore probably less noisy than the fine-grained senses, which would make corpus-based estimates of their frequency more reliable. The best performing fine grained frequency heuristic we present exploits this property of supersenses, by making the assumption that the most frequent sense of a word will be the most frequent member of the most frequent supersense. Essentially, when the overall most frequent sense of a word is a member of a minority supersense, we find that it is better to avoid selecting that sense. This system scores 63.8% on the SEN- SEVAL 3 all words task, significantly higher than the relevant baseline of 62.5% — using no contextual information at all. 2 Preliminaries: WordNet Sense Ranks, Frequencies and Supersenses This paper discusses different methods of selecting a ‘default’ sense of a word from the WordNet (Fellbaum, 1998) sense inventory. These methods draw on four different sources of information associated with the lexicon: WordNet sense ranks, WordNet sense counts, the SemCor sense tagged corpus, and WordNet lexical file numbers. Each word entry in WordNet consists of a lemma under a part-of-speech and an inventory of its senses. These senses are ranked by the lexicographers according to their frequencies in “various semantic concordance texts” (Fellbaum, 1998). These frequencies are often given in the database. We refer to them as WordNet counts to distinguish them from the frequencies we obtain from the SemCor corpus. The SemCor corpus is a subset of the semantic concordance texts used to calculate WordNet counts. Each WordNet sense is categorised under one of forty-five lexicographer files. Each lexicographer file covers only one part of speech. The main categorisation is applied to nouns and verbs, as there is only one file for adverbs, and three for adjectives. 10 Lexical files are interesting because they represent broad, or coarse-grained, semantic categories; and therefore a way around the commonly noted problem that WordNet senses are generally too fine grained. We describe a first-sense heuristic that takes advantage of this property of the lexicographer files (often referred to as ‘supersenses’ (Ciaramita and Johnson, 2003) — we use both terms interchangeably). We also discuss first supersense heuristics, as increasing attention is being paid to supervised supersense tagging (Ciaramita and Altun, 2006). 3 First Order Models for Word Sense Disambiguation Supervised word sense disambiguation (WSD) is the task of finding the most likely sense s from a sense inventory S given a usage context C: arg max P (s|C) (1) s∈S These models are usually compared to models of the form: arg max P (s) (2) s∈S in order to evaluate how much the context is informing the model. Since we are primarily interested in the argmax, it is usually sufficient to simply define a function that selects the most likely sense, even if a full probability distribution is not defined. Selecting the sense listed first in WordNet is one such function. As a WSD system, a first order model has an inherent upper bound, as if a word is used with more than one sense, a first order model cannot get all examples correct. However, Figure 1 shows that on the SENSEVAL 3 data, this upper bound is far higher than the performance of state-of-the-art WSD systems. The upper bound was calculated by selecting the most frequent sense of each word in the test data. It is effectively a system with oracle frequency information. Because the correct sense will always be given to words that only occur once, it is interesting to see how the upper bound decays if the system is forced to use the first-sense heuristic instead for words that occur less than n times in the test data. For n > 14, the oracle system either falls back to or makes the same prediction as the first-sense system for every instance, and so the systems are effectively identical. McCarthy et al. (2004) described a first order word sense disambiguation system that ac-
Accuracy 72 71 70 69 68 67 66 65 64 63 62 0 2 4 6 8 10 12 14 16 Threshold Oracle Baseline Supersense Frequency Method First Sense Figure 1: Upper performance bound of one-sense per term strategy for SenseEval quired ‘predominant’ senses automatically from un-annotated data using distributional similarity as a proxy for context, and WordNet-based semantic similarity as a sense distinction heuristic. The authors reported an accuracy of 64%, but their system was evaluated on the nouns from the SENSE- VAL 2 all words task, and hence cannot be directly compared with the results we report. 4 Experiments In this section we describe several default sense heuristics and evaluate them on the SENSEVAL 3 English all words test data set. All of the systems use the tokenisation, lemmatisation and part-ofspeech tags supplied in the data. Table 1 presents the results for the systems described below. Each system selects a source of information to select a supersense (None, Word- Net, SemCor) and a fine grained sense (WordNet or SemCor). When supersenses are being used, the fine grained sense is chosen from within the selected supersense, effectively filtering out the senses that belong to other lexical files. 4.1 Supersense: None; Fine Sense: WordNet This is the default sense heuristic used as the baseline in SENSEVAL 3, and is the most common heuristic used for WSD. The heuristic involves simply choosing the lowest numbered sense in 11 the WordNet sense inventory. As this is the only heuristic we explore that does not require extra data, it is the only one that has perfect coverage of WordNet’s vocabulary — there is guaranteed to be a sense rank for every WordNet lemma. When there is a coverage problem for one of the other heuristics, such as a case where a word has not occurred in the frequency estimation corpus, that heuristic is allowed to fall back to the WordNet sense rank, rather than being forced to select a sense arbitrarily. 4.2 Supersense: None; Fine Sense: SemCor As noted in Section 2, SemCor is effectively a subset of the information used to produce the Word- Net sense ranks. We evaluated a default sense heuristic that preferred SemCor-only frequncy estimates for words that occurred at least once in the SemCor corpus. This only results in a different prediction from the first-sense heuristic 7% of the time. Nevertheless, the systems perform significantly differently. 4.3 Supersense: WordNet; Fine Sense: WordNet WordNet sense ranks can also be straightforwardly used as the basis of a default supersense heuristic. Ciaramita and Altun (2006) select the supersense of the first fine grained sense
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