Automatic Mapping Clinical Notes to Medical - RMIT University

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

satisfy(Formula,model(D,F),G,pos):nonvar(Formula), Formula = some(X,SubFormula), var(X), memberList(V,D), satisfy(SubFormula,model(D,F),[g(X,V)|G],pos). satisfy(Formula,model(D,F),G,neg):nonvar(Formula), Formula = some(X,SubFormula), var(X), setof(V,memberList(V,D),All), setof(V, ( memberList(V,D), satisfy(SubFormula,model(D,F),[g(X,V)|G],neg) ), All). Figure 3: Prolog Clauses for Existential Quantification tion, this will automatically be raised to the calling function. To sum up, an attractive consequence of this approach in Python is that no additional stipulations need to be added to the recursive clauses for interpreting Boolean connectives. By contrast, in the B&B modelChecker2.pl code, the clauses for existential quantification shown in Figure 3 need to be supplemented with a separate clause for the ‘undefined’ case. In addition, as remarked earlier, each Boolean connective receives an additional clause when undefined. 5 Specifying Models Models are specified by instantiating the Model class. At initialization, two parameters are called, determining the model’s domain and valuation function. In Table 4, we start by creating a Valuation object val (line 1), we then specify the valuation as a list v of constant-value pairs (lines 2–9), using relational notation. For example, the value for ’adam’ is the individual ’d1’ (i.e., a Python string); the value for ’girl’ is the set consisting of individuals ’g1’ and ’g1’; and the value for ’love’ is a set of pairs, as described above. We use the parse method to update val with this information (line 10). As mentioned earlier, a Valuation object has a domain property (line 11), and in this case dom will evaluate to the set set([’b1’, ’b2’, ’g1’, ’g2’, ’d1’]). It is convenient to use this set as the value for the model’s domain when it is initialized (line 12). We also declare an Assignment object (line 13), specifying that its domain is the 30 same as the model’s domain. Given model m and assignment g, we can evaluate m.satisfiers(formula, g), for various values of formulas. This is quite a handy way of getting a feel for how connectives and quantifiers interact. A range of cases is illustrated in Table 5. As pointed out earlier, all formulas are represented as Python strings, and therefore need to be parsed before being evaluated. 6 Conclusion In this paper, I have tried to show how various aspects of Python lend themselves well to the task of interpreting first-order formulas, following closely in the footsteps of Blackburn and Bos. I argue that at least in some cases, the Python implementation compares quite favourably to a Prolog-based approach. It will be observed that I have not considered efficiency issues. Although these cannot be ignored (and are certainly worth exploring), they are not a priority at this stage of development. As discussed at the outset, our main goal is develop a framework that can be used to communicate key ideas of formal semantics to students, rather than to build systems which can scale easily to tackle large problems. Clearly, there are many design choices to be made in any implementation, and an alternative framework which overlaps in part with what I have presented can be found in the Python code supplement to (Russell and Norvig, 2003). 6 One important distinction is that the approach adopted here 6 http://aima.cs.berkeley.edu/python
val = Valuation() 1 v = [(’adam’, ’b1’), (’betty’, ’g1’), (’fido’, ’d1’),\ 2 (’girl’, set([’g1’, ’g2’])),\ 3 (’boy’, set([’b1’, ’b2’])),\ 4 (’dog’, set([’d1’])),\ 5 (’love’, set([(’b1’, ’g1’),\ 6 (’b2’, ’g2’),\ 7 (’g1’, ’b1’),\ 8 (’g2’, ’b1’)]))] 9 val.parse(v) 10 dom = val.domain 11 m = Model(dom, val) 12 g = Assignment(dom, {’x’: ’b1’, ’y’: ’g2’}) 13 Figure 4: First-order model m Formula open in x Satisfiers ’(boy x)’ set([’b1’, ’b2’]) ’(x = x)’ set([’b1’, ’b2’, ’g2’, ’g1’, ’d1’]) ’((boy x) or (girl x))’ set([’b2’, ’g2’, ’g1’, ’b1’]) ’((boy x) and (girl x))’ set([]) ’(love x adam)’ set([’g1’]) ’(love adam x)’ set([’g2’, ’g1’]) ’(not (x = adam))’ set([’b2’, ’g2’, ’g1’, ’d1’]) ’some y.(love x y)’ set([’g2’, ’g1’, ’b1’]) ’all y.((girl y) implies (love y x))’ set([]) ’all y.((girl y) implies (love x y))’ set([’b1’]) ’((girl x) implies (dog x))’ set([’b1’, ’b2’, ’d1’]) ’all y.((dog y) implies (x = y))’ set([’d1’]) ’(not some y.(love x y))’ set([’b2’, ’d1’]) ’some y.((love y adam) and (love x y))’ set([’b1’]) is explicitly targeted at students learning computational linguistics, rather than being intended for a more general artificial intelligence audience. While I have restricted attention to rather basic topics in semantic interpretation, there is no obstacle to addressing more sophisticated topics in computational semantics. For example, I have not tried to address the crucial issue of quantifier scope ambiguity. However, work by Peter Wang (author of the NLTK module nltk_lite.contrib.hole) implements the Hole Semantics of B&B. This module contains a ‘plugging’ algorithm which converts underspecified representations into fully-specified first-order logic formulas that can be displayed textually or graphically. In future work, we plan to extend the semantics package in various directions, in particular by adding some basic inferencing mechanisms to NLTK. Acknowledgements I am very grateful to Steven Bird, Patrick Blackburn, Alex Lascarides and three anonymous reviewers for helpful feedback and comments. Figure 5: Satisfiers in model m 31 References Steven Bird. 2005. NLTK-Lite: Efficient scripting for natural language processing. In Proceedings of the 4th International Conference on Natural Language Processing (ICON), pages 11–18, New Delhi, December. Allied Publishers. Patrick Blackburn and Johan Bos. 2005. Representation and Inference for Natural Language: A First Course in Computational Semantics. CSLI Publications. D. R. Dowty, R. E. Wall, and S. Peters. 1981. Introduction to Montague Semantics. Studies in Linguistics and Philosophy. Reidel, Dordrecht. Richard Montague. 1974. The proper treatment of quantification in ordinary English. In R. H. Thomason, editor, Formal Philosphy: Selected Papers of Richard Montague, pages 247–270. Yale University Press, New Haven. Stuart Russell and Peter Norvig. 2003. Artifical Intelligence: A Modern Approach. Prentice Hall. 2nd edition. Guido van Rossum. 2006. Python Tutorial. March. Release 2.4.3.
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Natural Language Processing and XML
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Extracting Patient Clinical Profile
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