Automatic Mapping Clinical Notes to Medical - RMIT University

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

Given the lack of research in VSD using multisemantic-role based selectional preferences, the main contribution of the work presented in this paper is to show that it is possible to use the multi-semantic-role based selectional preferences extracted using the current state-of-the-art SRL systems to achieve a certain level of improvement for verb VSD. We also give detailed descriptions for all the features, feature selection algorithms and the tuning of the machine learner parameters that we have used in the construction of our VSD system, so that our system can be easily reproduced. The remainder of this paper is organized as follows: we first introduce the framework for constructing and evaluating the VSD systems in Section 2; we then give detailed descriptions of all the feature types that we experimented with during our research in Section 3; Section 4 introduces the two datasets used to train and evaluate the features; the feature selection algorithms are presented in Section 5; the results of our experiments are presented in Section 6; and finally we conclude in Section 7. 2 VSD Framework There are three components in our VSD Framework: extraction of the disambiguation features from the input text (feature extraction), selection of the best disambiguation features with respect to unknown data (feature selection), and the tuning of the machine learner’s parameters. Since feature extraction is explained in detail in Section 3, we will only disscuss the other two components here. Within our framework, feature selection is performed only on the training set. We first use the feature selection algorithms described in Section 5 to generate different feature sets, which are used to generate separate datasets. We then perform cross validation (CV) on each dataset, and the feature set with the best performance is chosen as the final feature set. The machine learning algorithm used in our study is Maxium Entropy (MaxEnt: Berger et al. (1996) 1 ). MaxEnt classifiers work by modelling the probability distribution of labels with respect to disambiguation features, the distribution of which is commonly smoothed based on a Gaus- 1 We used Zhang Le’s implementation which is available for download at http://homepages.inf.ed. ac.uk/s0450736/maxent toolkit.html 138 sian prior. Different values for the Gaussian prior often lead to significant differences in the classification of new data, motivating us to include the tuning of the Gaussian prior in VSD framework. 2 The tuning of the Gaussian prior is performed in conjunction with the feature selection. The CV for each feature set is performed multiple times, each time with a different parameterisation of the Gaussian prior. Therefore, the final classifier incorporates the best combination of feature set and parameterisation of the Gaussian prior for the given dataset. 3 Features Types Since selectional preference based WSD features and general WSD features are not mutually exclusive of each other, and it would be less convincing to evaluate the impact of selectional preference based features without a baseline derived from general WSD features, we decided to include a number of general WSD features in our experiments. The sources of these features include: Part-of-Speech tags extracted using a tagger described in Gimnez and Mrquez (2004); parse trees extracted using the Charniak Parser (Charniak, 2000); chunking information extracted using a statistical chunker trained on the Brown Corpus and the Wall Street Journal (WSJ) section of the Penn Treebank (Marcus et al., 1993); and named entities extracted using the system described in Cohn et al. (2005). 3.1 General WSD Features There are 3 broad types of general WSD features: n-gram based features of surrounding words and WordNet noun synsets, parse-tree-based syntactic features, and non-parse-tree based syntactic features. 3.1.1 N-gram based features The following n-gram based features have been experimented with: Bag of Words Lemmatized open class words in the entire sentence of the target verb. Words that occur multiple times are only counted once. Bag of Synsets The WordNet (Fellbaum, 1998) synsets for all the open class words in the entire 2 We experimented with the following settings for the standard deviation (with a mean of 0) of the Gaussian prior in all of our experiments: 0.1, 0.5, 1.0, 5.0, 10.0, 50.0, 100.0, 500.0, 1000.0.
sentence; hypernyms for nouns and verbs are also included. Bag of POS Tags The POS tag of each word within a window of 5 words surrounding the target verb, paired with its relative position and treated as a separate binary feature. Bag of Chunk Tags The chunk tags (in Inside-Outside-Beginning (IOB) format: Tjong Kim Sang and Veenstra (1999)) surrounding and including the target verb within a window of 5 words, paired with their relative positions. Bag of Chunk Types The chunk types (e.g. NP, VP) surrounding and including the target verb within a window of 5 words. Bag of Named Entities The named entities in the entire sentence of the target verb. Left Words Each lemmatized word paired with its relative position to the left of the target verb within a predefined window. Right Words Each lemmatized word paired with its relative position to the right of the target verb within a predefined window. Surrounding Words The union of Left Words and Right Words features. Left Words with Binary Relative Position Each lemmatized word and its position 3 to the left of the target verb within a predefined window. Right Words with Binary Relative Position Each lemmatized word and its binary position to the right of the target verb within a predefined window. Surrounding Words with Binary Relative Position The union of Left Words with Binary Position and Right Words with Binary Position features. Left POS-tags The POS tag of each word to the left of the target verb within a predefined window, paired with its relative position. Right POS-tags The POS tag of each word to the right of the target verb within a predefined window is paired with its relative position. 3 All words to the left of the target verb are given the “left” position, and all the to the right of the target verb are given the “right” position. 139 Surrounding POS Tags The Union of the Left POS-tags and Right POS-tags features. It may seem redundant that for the same window size, the Surrounding-Words (POS) features are the union of the Left-Words (POS) features and the Right-Words (POS) features. However, this redundancy of features makes it convenient to investigate the disambiguation effectiveness of the word collocations before and after the target verb, as well as the syntactic pattern before and after the target verb. Furthermore, we have also experimented with different window sizes for the Surrounding-Words (POS), Left-Words (POS) and Right-Words (POS) to determine the most appropriate window size. 4 3.1.2 Parse tree based features The parse tree based syntactic features are inspired by research on verb subcategorization acquisition such as Korhonen and Preiss (2003), and are intended to capture the differences in syntactic patterns of the different senses of the same verb. Given the position of the target verb v in the parse tree, the basic form of the corresponding parse tree feature is just the list of nodes of v’s siblings in the tree. Figure 1 shows the parse tree for a sentence containing the ambiguous verb call. Given the position of the target verb called in the parse tree, the basic form of the features that can be created will be (NP,PP). However, there are 3 additional types of variations that can be applied to the basic features. The first variation is to include the relative positions of the sibling node types as part of the feature: this variation will change the basic feature for call to {(1,NP),(2,PP)}. The second variation is to include the binary relative direction of the siblings to the target verb as part of the feature, i.e. is the sibling to the left or right of the target verb: this variation will change the basic feature for call to {(right,NP),(right,PP)}. The third variation is to include the parent node type as part of the sibling node type to add more information in the syntactic pattern. Figure 2 shows what the original parse tree looks like when every nonterminal is additionally annotated with its parent type. Since the third variation is compatible with the first two variations, we decided to combine them to create the following parse tree 4 In the ranking based evaluation method described in Section 5, only the Surrounding-Words (POS) feature types with the largest window size are used.
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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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TIME ACC COVER FAIL RATE % secs % %
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S.S. Source Sense Source S.S. Acc.
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Experiments with Sentence Classific
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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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Classifying Speech Acts using Verba
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our results. In classifying only th
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Word Relatives in Context for Word
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Algorithm Avg S2LS Avg S3LS Avg S2L
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Miriam Butt, Helge Dyvik, Tracy Hol
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References Aronson, A. R. (2001). E
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User Question Analyser QA System Se
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Page 164 and 165: Towards Cognitive Optimisation of a
Page 166 and 167: Natural Language Processing and XML
Page 168 and 169: Extracting Patient Clinical Profile
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