Presentation on theme: "A Probabilistic Representation of Systemic Functional Grammar Robert Munro Department of Linguistics, SOAS, University of London."— Presentation transcript:
A Probabilistic Representation of Systemic Functional Grammar Robert Munro Department of Linguistics, SOAS, University of London
2 Outline Introduction Functions in the nominal group Machine learning Testing framework Classification vs unmarked function Gradational realization Delicacy Conclusions
3 Introduction An exploration of the ability of machine learning to learn and represent functional categories as fundamentally probabilistic Gauged in terms of the ability to: computationally learn functions from labeled examples and apply to new texts. represent functions probabilistically: a gradation of potential realization between categories. explore finer layers of delicacy.
4 Functions in the nominal group Functions: Deictic, Ordinative, Quantitative, Epithet, Classifier, Thing (Halliday 1994) Gradations: Here, red functions also functions as an Epithet. The uptake of such marked classifiers will not be uniform. Overlap does not necessarily limit significance. Deictic The Ordin. first Quant. three Epith. tasty Class. red Thing wines
5 Machine Learning Machine learning: computational inference from specific examples. A learner named Seneschal was developed for the task here: probabilistic seeks sub-categories (improves both classification and analysis) allows categories to overlap not too dependent on the size of the data set
6 Machine Learning y x ? ?? ? ? ? ? The task here: Given categories & with known values for x and y, infer a probabilistic model (potentially with sub-categories) that can classify new examples:
7 Machine Learning It is important that attributes (x,y,z...) : represent features that distinguish functions can be discovered automatically (for large scales) are meaningful for analysis…? Compared to manually constructed parsers: greater scales than are practical more features/dimensions than are possible (100s are common)
8 Testing Framework The model was learned from 10,000 labeled words from Reuters sports newswires from 1996 23 features: part-of-speech and its context punctuation group / phrase contexts collocational tendencies probability of repetition
9 Testing Framework Accuracy: The ability to correctly identify the dominant function in 4 test corpora (1,000 words each): 1.Reuters Sports Newswires (1996) 2.Reuters Sports Newswires (2003) 3.Bio-informatics abstracts 4.Extract from Virginia Woolfs The Voyage Out
10 Testing Framework Gradational model of realization: calculated as the probability of a word realizing other functions, averaged between all clusters. Finer layers of delicacy: Manual analysis of clusters found within a function.
11 Unmarked function Unmarked function: function defined by only part-of-speech (POS) and word order. eg: adjective = Epithet, non-final noun = Classifier Previous functional parsers have assumed that most instances are unmarked: POS taggers are almost 100% accurate word order is trivial …so the problem is solved?
12 Unmarked function This is a false assumption. Across the corpora: < 40% of non-final adjs realized Epithets < 50% of Classifiers were nouns 44% of Classifiers were marked!
13 Unmarked function This task halved the classification error:
14 Gradational Realization Deictic The Ordin. first Quant. three Epith. tasty Class. red Thing wines Deictic Ordin. Quant. Epith. Class. Thing Nominal functions are typically represented deterministically: Although described as probabilistic, With relationships existing between all functions
15 Delicacy Deictic Numerative Epithet Expansive Thing Demonstrative Possessive Ordinative Quantitative Hyponymic Classifier First Name Intermediary Last Name non-Nom. Stated Described Discursive Nominative Named Entity Group-Releasing Nominal Tabular
16 Delicacy More delicate functions for Classifiers (Matthiessen 1995) : Hyponymic: describing a taxonomy or general type-of relationship eg: red wine, gold medal,neural network architecture' Expansive: expands the description of the Head. eg: knee surgery, optimization problems',sprint champion,
18 Delicacy More delicate descriptions can be found: more features more instances / registers other algorithms / parameters Methodology can be applied to: other parts of a grammar other languages
19 Conclusions Gradational modeling of functional realization is desirable Sophisticated methods are necessary for computationally modeling functions: Markedness is common Machine learning is a useful tool and participant in linguistic analysis.
20 Thank you Acknowledgments: Geoff Williams Sanjay Chawla The slides and extended paper will be published at: www.robertmunro.com/research/