A Supervised Approach for Enriching the Relational Structure of Frame Semantics in FrameNet

Frame semantics is a theory of linguistic meanings, and is considered to be a useful framework for shallow semantic analysis of natural language. FrameNet, which is based on frame semantics, is a popular lexical semantic resource. In addition to providing a set of core semantic frames and their frame elements, FrameNet also provides relations between those frames (hence providing a network of frames i.e. FrameNet). We address here the limited coverage of the network of conceptual relations between frames in FrameNet, which has previously been pointed out by others. We present a supervised model using rich features from three different sources: structural features from the existing FrameNet network, information from the WordNet relations between synsets projected into semantic frames, and corpus-collected lexical associations. We show large improvements over baselines consisting of each of the three groups of features in isolation. We then use this model to select frame pairs as candidate relations, and perform evaluation on a sample with good precision

Computational Linguistics Resources for Indo-Iranian Languages

Can computers process human languages? During the last fifty years, two main approaches have been used to find an answer to this question: data- driven (i.e. statistics based) and knowledge-driven (i.e. grammar based). The former relies on the availability of a vast amount of electronic linguistic data and the processing capabilities of modern-age computers, while the latter builds on grammatical rules and classical linguistic theories of language.In this thesis, we use mainly the second approach and elucidate the development of computational (”resource”) grammars for six Indo-Iranian languages: Urdu, Hindi, Punjabi, Persian, Sindhi, and Nepali. We explore different lexical and syntactical aspects of these languages and build their resource grammars using the Grammatical Framework (GF) – a type theo- retical grammar formalism tool.We also provide computational evidence of the similarities/differences between Hindi and Urdu, and report a mechanical development of a Hindi resource grammar starting from an Urdu resource grammar. We use a functor style implementation that makes it possible to share the commonalities between the two languages. Our analysis shows that this sharing is possible upto 94% at the syntax level, whereas at the lexical level Hindi and Urdu differed in 18% of the basic words, in 31% of tourist phrases, and in 92% of school mathematics terms.Next, we describe the development of wide-coverage morphological lexicons for some of the Indo-Iranian languages. We use existing linguistic data from different resources (i.e. dictionaries and WordNets) to build uni-sense and multi-sense lexicons.Finally, we demonstrate how we used the reported grammatical and lexical resources to add support for Indo-Iranian languages in a few existing GF application grammars. These include the Phrasebook, the mathematics grammar library, and the Attempto controlled English grammar. Further, we give the experimental results of developing a wide-coverage grammar based arbitrary text translator using these resources. These applications show the importance of such linguistic resources, and open new doors for future re- search on these languages

An Open Source Persian Computational Grammar

In this paper, we describe a multilingual open-source computational grammar of Persian, developed in Grammatical Framework (GF) – A type-theoretical grammar formalism. We discuss in detail the structure of different syntactic (i.e. noun phrases, verb phrases, adjectival phrases, etc.) categories of Persian. First, we show how to structure and construct these categories individually. Then we describe how they are glued together to make well-formed sentences in Persian, while maintaining the grammatical features such as agreement, word order, etc. We also show how some of the distinctive features of Persian, such as the ezafe construction, are implemented in GF. In order to evaluate the grammar’s correctness, and to demonstrate its usefulness, we have added support forPersian in a multilingual application grammar (the Tourist Phrasebook) using the reported resource grammar

An Open Source Persian Computational Grammar

In this paper, we describe a multilingual open-source computational grammar of Persian, developed in Grammatical Framework (GF) – A type-theoretical grammar formalism. We discuss in detail the structure of different syntactic (i.e. noun phrases, verb phrases, adjectival phrases, etc.) categories of Persian. First, we show how to structure and construct these categories individually. Then we describe how they are glued together to make well-formed sentences in Persian, while maintaining the grammatical features such as agreement, word order, etc. We also show how some of the distinctive features of Persian, such as the ezafe construction, are implemented in GF. In order to evaluate the grammar’s correctness, and to demonstrate its usefulness, we have added support forPersian in a multilingual application grammar (the Tourist Phrasebook) using the reported resource grammar

An Open Source Urdu Resource Grammar

We develop a grammar for Urdu in Grammatical Framework (GF). GF is a programming language for defining multilingual grammar applications. GF resource grammar library currently supports 16 languages. These grammars follow an Interlingua approach and consist of morphology and syntax modules that cover a wide range of features of a language. In this paper we explore different syntactic features of the Urdu language, and show how to fit them in the multilingual framework of GF. We also discuss how we cover some of the distinguishing features of Urdu such as, ergativity in verb agreement (see Sec 4.2). The main purpose of GF resource grammar library is to provide an easy way to write natural language applications without knowing the details of syntax, morphology and lexicon. To demonstrate it, we use Urdu resource grammar to add support for Urdu in the work reported in (Angelov and Ranta, 2010) which is an implementation o

A Supervised Approach for Enriching the Relational Structure of Frame Semantics in FrameNet

International audienceFrame semantics is a theory of linguistic meanings, and is considered to be a useful framework for shallow semantic analysis of natural language. FrameNet, which is based on frame semantics, is a popular lexical semantic resource. In addition to providing a set of core semantic frames and their frame elements, FrameNet also provides relations between those frames (hence providing a network of frames i.e. FrameNet). We address here the limited coverage of the network of conceptual relations between frames in FrameNet, which has previously been pointed out by others. We present a supervised model using rich features from three different sources: structural features from the existing FrameNet network, information from the WordNet relations between synsets projected into semantic frames, and corpus-collected lexical associations. We show large improvements over baselines consisting of each of the three groups of features in isolation. We then use this model to select frame pairs as candidate relations, and perform evaluation on a sample with good precision

Computational evidence that Hindi and Urdu share a grammar but not the lexicon

Hindi and Urdu share a grammar and a basic vocabulary, but are often mutually unintelligible because they use different words in higher registers and sometimes even in quite ordinary situations. We report computational translation evidence of this unusual relationship (it differs from the usual pattern, that related languages share the advanced vocabulary and differ in the basics). We took a GF resource grammar for Urdu and adapted it mechanically for Hindi, changing essentially only the script (Urdu is written in Perso-Arabic, and Hindi in Devanagari) and the lexicon where needed. In evaluation, the Urdu grammar and its Hindi twin either both correctly translated an English sentence, or failed in exactly the same grammatical way, thus confirming computationally that Hindi andUrdu share a grammar. But the evaluation also found that the Hindi and Urdu lexicons differed in 18% of the basic words, in 31 % of tourist phrases, and in 92 % of school mathematics terms