Talks. David Martinez and Meladel Mistica. (2011/10/14)

Date: October 14, 2011
Time: 15:00
Where: Computer Science Faculty, 3.2 room

[1]

Title: Word classes in Indonesian: A linguistic reality or a convenient
fallacy in natural language processing?
Speaker: Meladel Mistica (Australian National University)
Abstract[1]:
In this talk I will be presenting work on Indonesian (Bahasa Indonesia), and the claim that there is no noun-verb distinction within the language as it is spoken in regions such as Riau and Jakarta. We test this claim for the language as it is written by a variety of Indonesian speakers using empirical methods traditionally used in part-of-speech induction. In this study we use only morphological patterns that we generate from a pre-existing morphological analyser. We find that once the distribution of the data points in our experiments match the distribution of the text from which we gather our data, we obtain results that show a significant
distinction between the class of nouns and the class of verbs in Indonesian. Furthermore it shows promise that the labelling of word classes may be achieved only with morphological features, which could be applied to out-of-vocabulary items.

[2]

Title: Text classification of patient reports and event-modifier
identification for the biomedical literature
Speaker: David Martinez (NICTA – National ICT Australia)
Abstract[2]:
The first short talk describes the implementation and evaluation of a text classification system of pathology reports for the Royal Melbourne Hospital, which relied on document-level annotations obtained from the medical workflow. We observed that a basic machine learning framework with linguistic features carries the potential to make an impact in their process.
The second talk describes our work on modifiers of biomedical events over the BioNLP-2009 dataset. Our system combines a simple bag-of-words method with two grammar-based approaches, namely the English Resource Grammar and the RASP parser. We interpret the output of the respective parsers via MRS (Minimal Recursion Semantics), and feed them into a machine learner. Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.