Automatic Mapping Clinical Notes to Medical - RMIT University

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

ased features: Type 1 Original sibling node types (zero level of parent node annotated) with relative positions. Type 2 Original sibling node types with relative binary directions. Type 3 One level of parent-node-annotated sibling node types with relative positions. Using the call example, this feature will be {(1, VP-NP),(2, VP-PP)}. Type 4 One level of parent-node-annotated sibling node types with relative binary directions. Using the call example, this feature will be {(right, VP-NP),(right, VP-PP)}. Type 5 Two levels of parent-node-annotated sibling node types with relative positions. Type 6 Two levels of parent-node-annotated sibling node types with relative binary directions. On top of the 6 types of purely syntactic based parse tree features, there is an additional type of parse tree based feature designed to capture the verb-argument structure of the target verb. The type of features only cover four particular types of parse tree nodes which are immediately after the pre-terminal node of the target verb. The four types of parse tree nodes are: ADVP, NP, PP and clausal nodes. For an ADVP node, we extract its head adverb H ADV , and treat the tuple of (ADVP, H ADV ) as a separate binary feature. For an NP node, we first extract its head nouns, then replace each head noun with its WordNet synsets and the hypernyms of these synsets, and treat each of these synsets as a separate binary feature. In order to cover the cases in which the head noun of a NP node is a quantity noun, e.g. a glass of water, the head nouns of PPs attached to the NP nodes are also included as head nouns. Furthermore, head nouns which are named entities identified by the system described in Cohn et al. (2005) are replaced by appropriate WordNet synsets. For a PP node, we first extract its head preposition, then we extract the head noun synsets in the same way as the NP node, and finally we combine each synset with the head preposition to form a separate binary feature. For a clausal node which is an SBAR, we extract the list of node types of its direct children 140 and arrange them in their original order, and treat this list as a single binary feature. For a clausal node which is not an SBAR, but has a single non-terminal child node, we first extract the type of this child node, then we extract the list of node types for the children of this child node. The tuple of (child-node-type, list-ofgrandchildren-node-types) is then treated as a separate binary feature. 3.1.3 Non-parse tree based syntactic features There are 3 types of non-parse-tree based syntactic features: Voice of the verb The voice of the target verb (active or passive). Quotatives Verbs that appear in directly quoted speech have a greater likelihood of occurring in the imperative and losing the surface subject, e.g. “Call the police!”. We therefore include this as a standalone feature. Additional Chunk based features A number of additional chunk based features are also used to capture the phrase level localized syntactic and collocation patterns from the context to the right of the target verb within a window of between 3 and 10 chunks. For example, using a window of 7, for the verb kick in the sentence: [I/PRP]NP [kicked/VBD]VP [him/PRP]NP [out/IN of/IN]PP [the/DT door/NN]NP [through/IN]PP [which/WDT]NP [he/PRP]NP [came/VBD]VP, the first 7 chunks after the chunk that contains kick will be used for feature extraction. These additional chunk based features are: Chunk-type-sequence The concatenation of all the relevant chunk types. For example, using the above kick example, this feature will be NP PP NP PP NP NP VP. Regular expression (RE) representation of the chunk types The consecutive identical chunk types in the Chunk-typesequence feature merged into a single symbol. For example, the chunk-type-sequence of NP PP NP PP NP NP VP will be represented as NP PP NP PP NP+ VP. First word of each chunk with the chunk type The list that contains the first word of each chunk will be treated as a separate binary feature. With the kick example, this feature would be him out the through which he came.
Last word of each chunk with the chunk type The list that contains the last word of each chunk will be treated as a separate feature. With the kick example, this feature would be him of door through which he came. First POS tag of each chunk with the chunk type The list that contains the first POS tag of each chunk will be treated as a separate feature. With the kick example, this feature would be PRP IN DT IN WDT PRP VBD. Last POS tag of each chunk with the chunk type The list that contains the last POS tag of each chunk will be treated as a separate feature. With the kick example, this feature would be PRP IN NN IN WDT PRP VBD. Chunk-type-sensitive combinations This feature is created by merging the chunk types and some additional information associated with the chunks. If a chunk is a PP, then the head preposition will also be part of the feature; if a chunk is an NP and the head word is a question word (who, what, when, how, where or why), the head word itself will be part of the feature, but if the head word is not a question word, its POS tag will be part of the feature; if a chunk is a VP, then the POS tag of the head verb will be part of the Figure 1: Basic parse tree feature example Figure 2: One level parent annotated parse tree 141 feature. Using the above kick example, this feature will be: (NP, PRP) (PP, out) (NP, NN) (PP, through) (NP, which) (NP, PRP) (VP, VBD). 3.2 Selectional Preference Based Features We used the ASSERT 5 system (Hacioglu et al., 2004) to extract the semantic roles from the target sentences. The following selectional preference based features have been experimented with: WordNet synsets of the head nouns of the SRs For each semantic role, the WordNet synsets of its head noun, paired with the corresponding semantic role. Semantic role’s relative positions These features are designed to capture the syntactic patterns of the target verb and its semantic roles. The relative position is set up such that the first semantic role to the left of the target verb will be given the position of −1, and the first one to the right will be given the position of +1. Lemmatized head noun of each semantic role Similar to the synset features, each semantic role is also paired with its head noun. 5 We used version 1.4b of this system which can be downloaded from http://oak.colorado.edu/assert/
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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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ance. Several additional example ut
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our results. In classifying only th
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Word Relatives in Context for Word
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create a collection of training exa
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Query Sense The nave was rebuilt in
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Algorithm Avg S2LS Avg S3LS Avg S2L
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B. Snyder and M. Palmer. 2004. The
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it is even used as a black box that
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ecognition in QA, so we will not us
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of noise introduced by the addition
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2 Existing annotated corpora Much o
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Our FUSE|tel spectrum of HD|sta 738
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N Word Correct Tagged 23 OH mol non
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L ATEX typesetting information into
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in English, neither the determiner
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con 4 (Sagot et al., 2006) and Morp
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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
Page 96 and 97: 2 Background 2.1 Pronoun Reference
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Page 100 and 101: Table 1: Overall Results for Object
Page 102 and 103: References Jennifer Arnold, Janet E
Page 104 and 105: In order for appropriate documents
Page 106 and 107: y Michael West. He found that his t
Page 108 and 109: low-scoring documents are “Click
Page 110 and 111: a) b) You must complete at least 9
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Page 116 and 117: Argument lowering Dependent geach
Page 118 and 119: who [u : np] WH(np, s, s/?gq) λP.
Page 120 and 121: algorithms, and report the performa
Page 122 and 123: 2.3 Coverage of the Human Data Out
Page 124 and 125: and minimal subsets of the data. Of
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Page 132 and 133: a given word sequence Sorig as a pe
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Page 136 and 137: Example 1: Original Headline: Europ
Page 138 and 139: |relations(sentence1) ∩ relations
Page 140 and 141: 2685 True False 1275 Bleu Dep Bleu
Page 142 and 143: Features Acc. C1-prec. C1-recall C1
Page 144 and 145: Given the lack of research in VSD u
Page 148 and 149: Preposition of the adjunct semantic
Page 150 and 151: Algorithm 1 The algorithm of combin
Page 152 and 153: References Timothy Baldwin and Fran
Page 154 and 155: dependencies in German with respect
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Page 158 and 159: a brevity penalty of BLEU n = 1 n =
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Page 162 and 163: Probabilities improve stress-predic
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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Automatic Mapping Clinical Notes to Medical - RMIT University

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