Abstract

Shallow discourse parsing enables us to study discourse as a coherent piece of information rather than a sequence of clauses, sentences and paragraphs. In this paper, we identify arguments of explicit discourse relations in Hindi. This is the first such work carried out for Hindi. Building upon previous work carried out on discourse connective identification in Hindi, we propose a hybrid pipeline which makes use of both sub-tree extraction and linear tagging approaches. We report state-ofthe-art performance for this task.

Abstract

Extractive summarization is the process of precisely choosing a set of sentences from a corpus which can actually be a representative of the original corpus in a limited space. In addition to exhibiting a good content coverage, the final summary should be readable as well as structurally and topically coherent. In this paper we present a holistic, multi-document summarization approach which takes care of the content coverage, sentence ordering, maintenance of topical coherence, topical order and inter-sentence structural relationships. To achieve this we have introduced a novel concept of a Local Coherent Unit (LCU). Our results are comparable with the peer systems for content coverage and sentence ordering measured in terms of ROUGE and τ score respectively. The human evaluation preference for readability and coherence of summary are significantly better for our approach vis a vis other approaches. The approach is scalable to bigger realtime corpus as well.

Abstract

In this paper, we present a Paninian grammar based heuristic model1 to resolve entity-pronoun references in Hindi dialogue. We explore the use of Paninian based dependency structures as a source of syntactico-semantic information. Our experiments illustrate that the use of dependency and dialogue structures help to resolve specific types of references. We also show that named entity, discourse information like subtopic boundary and animacy features increase the overall resolution accuracy to 64% for user-user interaction data and 59% for play-story corpora.

Abstract

This paper presents a rule-based reorder-ing approach for English-Hindi machinetranslation. We have used the concept ofpada,from P ̄an.inian Grammar to framethe reordering rules. Apadais a wordform which is ready to participate in asentence. The rules are generic enoughto apply on any English-Indian languagepair. We tested the rules on English-Hindilanguage pair and obtained better compre-hensibility score as compared to GoogleTranslate on the same test set. In assess-ing the effectiveness of the rules on padaswhich are analogous to minimal phrases inEnglish, we achieved upto 93% accuracyon the test data.

Abstract

Sindhi is an Indo-Aryan language spoken by more than 58 million speakers around the world. It is currently a resource poor language which is harmed by the literature being written in multiple scripts. Though the language is widely spoken,primarily, across two countries, the written form is not standardized. In this paper, we seek to develop resources for basic language processing for Sindhi language, in one of its preferred scripts (Devanagari), because a language that seeks to survive in the modern information society requires language technology products. This paper presents our work on building a stochastic Part-of-Speech tagger for Sindhi-Devanagari using conditional random fields with linguistically motivated features. The paper also discusses the steps taken to construct a part-of-speech annotated corpus for Sindhi in Devanagari script. We have also explained in detail the features that were used for training the tagger, which resulted in a part of speech tagger nearing 92% average accuracy.

Abstract

In this paper we explain the identification of karaka relations in an English sentence. We explain the genesis of the problem and present two different approaches, rule based and statistical. We briefly describe about rule based and focus more on statistical approach. We process a sentence through various stages and extract features at each stage. We train our data and identify Karaka relations using Support Vector Machines (SVM). We also explain the impact of our work on Natural Language Interfaces for Database systems.

Abstract

Sandhi splitting is the primary task for computational processing of text in Sanskrit and Dravidian languages. In these languages, words can join together with morpho-phonemic changes at the point of joining. This phenomenon is known as Sandhi. Sandhi splitter splits the string of conjoined words into individual words. Accurate execution of sandhi splitting is crucial for text processing tasks such as POS tagging, topic modelling and document indexing. We have tried different approaches to address the challenges of sandhi splitting in Malayalam, and finally, we have thought of exploiting the phonological changes that take place in the words while joining. This resulted in a hybrid method which statistically identifies the split points and splits using predefined character level linguistic rules. Currently, our system gives an accuracy of 91.1%.

Abstract

Word sketches are one-page automatic, corpus-based summaries of a word’s grammatical and collocational behaviour. These are widely used for studying a language and in lexicography. Sketch Engine is a leading corpus tool which takes as input a corpus and generates word sketches for the words of that language. It also generates a thesaurus and ‘sketch differences’, which specify similarities and differences between near-synonyms. In this paper, we present the functionalities of Sketch Engine for Hindi. We collected HindiWaC, a web crawled corpus for Hindi with 240 million words. We lemmatized, POS tagged the corpus and then loaded it into Sketch Engine.

Abstract

Even though, a lot of research has already been done on Machine Translation, translating complex sentences has been a stumbling block in the process. To improve the performance of machine translation on complex sentences, simplifying the sentences becomes imperative. In this paper, we present a rule based approach to address this problem by simplifying complex sentences in Hindi into multiple simple sentences. The sentence is split using clause boundaries and dependency parsing which identifies different arguments of verbs, thus changing the grammatical structure in a way that the semantic information of the original sentence stay preserved.

Abstract

In this paper, we discuss our efforts to annotate nominals in the Hindi Treebank with the semantic property of animacy. Although the treebank already encodes lexical information at a number of levels such as morph and part of speech, the addition of animacy information seems promising given its relevance to varied linguistic phenomena. The suggestion is based on the theoretical and computational analysis of the property of animacy in the context of anaphora resolution, syntactic parsing, verb classification and argument differentiation.