Extracting and Classifying Urdu Multiword Expressions

This paper describes a method for automatically extracting and classifying multiword expressions (MWEs) for Urdu on the basis of a relatively small unannotated corpus (around 8.12 million tokens). The MWEs are extracted by an unsupervised method and classified into two distinct classes, namely locations and person names. The classification is based on simple heuristics that take the co-occurrence of MWEs with distinct postpositions into account. The resulting classes are evaluated against a hand-annotated gold standard and achieve an f-score of 0.5 and 0.746 for locations and persons, respectively. A target application is the Urdu ParGram grammar, where MWEs are needed to generate a more precise syntactic and semantic analysis.