This paper presents the phonological, morphological, and syntactic
distinctions between formal and informal Persian, showing that these two
variants have fundamental differences that cannot be attributed solely to
pronunciation discrepancies. Given that informal Persian exhibits particular
characteristics, any computational model trained on formal Persian is unlikely
to transfer well to informal Persian, necessitating the creation of dedicated
treebanks for this variety. We thus detail the development of the open-source
Informal Persian Universal Dependency Treebank, a new treebank annotated within
the Universal Dependencies scheme. We then investigate the parsing of informal
Persian by training two dependency parsers on existing formal treebanks and
evaluating them on out-of-domain data, i.e. the development set of our informal
treebank. Our results show that parsers experience a substantial performance
drop when we move across the two domains, as they face more unknown tokens and
structures and fail to generalize well. Furthermore, the dependency relations
whose performance deteriorates the most represent the unique properties of the
informal variant. The ultimate goal of this study that demonstrates a broader
impact is to provide a stepping-stone to reveal the significance of informal
variants of languages, which have been widely overlooked in natural language
processing tools across languages.

探讨波斯语（Persian）的正式形式和非正式形式之间的音韵、形态和句法区别，提出正式形式的树库在计算模型转换到非正式形式表现糟糕的问题，并开发了基于通用依赖理论的非正式波斯语树库，研究表明现有依赖树库训练的依存分析器在跨越两个领域时表现不佳，而非正式形式的语言的独特特征更容易出现错误。

非正式波斯通用依存树库

Informal Persian Universal Dependency Treebank

Formal and distributional semantic models offer complementary benefits in
modeling meaning. The categorical compositional distributional (DisCoCat) model
of meaning of Coecke et al. (arXiv:1003.4394v1 [cs.CL]) combines aspected of
both to provide a general framework in which meanings of words, obtained
distributionally, are composed using methods from the logical setting to form
sentence meaning. Concrete consequences of this general abstract setting and
applications to empirical data are under active study (Grefenstette et al.,
arxiv:1101.0309; Grefenstette and Sadrzadeh, arXiv:1106.4058v1 [cs.CL]). . In
this paper, we extend this study by examining transitive verbs, represented as
matrices in a DisCoCat. We discuss three ways of constructing such matrices,
and evaluate each method in a disambiguation task developed by Grefenstette and
Sadrzadeh (arXiv:1106.4058v1 [cs.CL]).

DisCoCat model 在组合分布式意义、语义逻辑、范畴语法等方法方面表现出很好的性能，本文扩展研究着重探讨 DisCoCat 的矩阵方法在处理及消除动词歧义方面的应用。