  Parsing is the process of breaking a sentence into its grammatical components
and identifying the syntactic structure of the sentence. The syntactically
correct sentence structure is achieved by assigning grammatical labels to its
constituents using lexicon and syntactic rules. In linguistics, parser is
extremely useful due to the number of different applications like name entity
recognition, QA systems and information extraction, etc. The two most common
techniques used for parsing are phrase structure and dependency Structure.
Because Urdu is a low-resource language, there has been little progress in
building an Urdu parser. A comparison of several parsers revealed that the
dependency parsing approach is better suited for order-free languages such as
Urdu. We have made significant progress in parsing Urdu, a South Asian language
with a complex morphology. For Urdu dependency parsing, a basic feature model
consisting of word location, word head, and dependency relation is employed as
a starting point, followed by more complex feature models. The dependency
tagset is designed after careful consideration of the complex morphological
structure of the Urdu language, word order variation, and lexical ambiguity and
it contains 22 tags. Our dataset comprises of sentences from news articles, and
we tried to include sentences of different complexity (which is quite
challenging), to get reliable results. All experiments are performed using
MaltParser, exploring all 9 algorithms and classifiers. We have achieved a 70
percent overall best-labeled accuracy (LA), as well as an 84 percent overall
best-unlabeled attachment score (UAS) using the Nivreeager algorithm. The
comparison of output data with treebank test data that has been manually parsed
is then used to carry out error assessment and to identify the errors produced
by the parser.
