KMI -Panlingua- IITKGP @ SIGTYP 2020: Exploring rules and hybrid systems for automatic prediction of typological features (2020)

Abstract: This paper enumerates SigTyP 2020 Shared Task on the prediction of typological features as performed by the KMI-Panlingua-IITKGP team. The task entailed the prediction of missing values in a particular language, provided, the name of the language family, its genus, location (in terms of latitude and longitude coordinates and name of the country where it is spoken) and a set of feature-value pair are available. As part of fulfillment of the aforementioned task, the team submitted 3 kinds of system - 2 rule-based and one hybrid system. Of these 3, one rule-based system generated the best performance on the test set. All the systems were ‘constrained’ in the sense that no additional dataset or information, other than those provided by the organisers, was used for developing the systems.

## introduction this paper is a detailed documentation of the kmi-panlingua-iitkgp team's system submission at the sigtyp 2020 shared task on the prediction of typological features. the objective behind this task is to develop a computational model that predicts (missing) linguistic features of a language, given its location, language family, genus, and a set of feature-value pair. the shared task organisers provided the dataset used for this purpose, which has been extracted from worlds atlas of language structures (wals) (see section 2 for details). since the provided dataset was not large and comprised of unevenly distributed features, we prepared and experimented with three different systems and compare them with each other to provide the best model. of these three systems, 2 are rule-based, in which, one is frequency-based system (see subsection 3.1) and the other is statistical system (see subsection 3.2). the third one is a hybrid (see subsection 3.3). the statistical system provides the best results among the three (see section 4). this paper promises two major contributions. first, it provides an automatic system that enables extraction of feature-value pair of a given language -a tedious job if done manually. second, it compares three different systems and provides evidence that a statistical model gives better results for the given data set. ## dataset the dataset foot_0 used for this experiment was extracted from world atlas of language structures (wals) @xcite . it covered the typological features of close to 2,000 languages @xcite . these typological features were organised in 8 columns (including language id, language name, latitude, longitude, genus, family, country codes, and feature-value. the feature values were separated by ' ' for each language). the task was divided into two sub-tasks: (a) constrained and (b) unconstrained. the dataset was sub-divided into training, dev, and test sets. out of 1,125 training instances, genus had 280 different features, country had 258 different features, family had 127 different features and features had 185 different feature-values. out of 84 dev instances, genus had 50 different features, country had 32, family had 36, while features had 182 different feature-values. out of 149 test instances, genus had 42 different features, country had 30, family had 35, while features had 183 different feature-values ( see ## experiments our task consisted of 3 experiments and this section enumerates and discusses each one of the systems in detail. all the 3 systems are based on the notion of shared typological properties of languages belonging to the same language family (or sub-family, represented as genus in the dataset) and shared areal properties of languages belonging to different language families but being in regular contact by virtue of being in close contact, mainly because of speakers residing in close geographical proximity. ## results ## conclusion in this paper, we presented two rule-based systems and one hybrid system to predict typological features of a given language. we demonstrated that the statistical, a rule-based system, gave the best performance on the test set. only the data set provided by the organisers was used for developing the systems.