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

This paper describes the techniques for the automatic detection of subtopic/subplan boundary in Hindi dialogue using structure of dialogue, dialogue acts, shallow linguistic features and word co-occurrence. Our experiments illustrate that the use of dialogue structure, word co-occurrence and wordnet improves the boundary identification for Hindi natural dialogues.

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

Attribute information in a natural language query is one of the key features for converting a natural language query into a Structured Query Language1 (SQL) in Natural Language Interface to Database systems. In this paper, we explore the task of classifying the attributes present in a natural language query into different SQL clauses in a SQL query. In particular, we investigate the effectiveness of various features and Conditional Random Fields for this task. Our system uses a statistical classifier trained on manually prepared data. We report our results on three different domains and also show how our system can be used for generating a complete SQL query.

Abstract

In a task oriented domain, recognizing the intention of a speaker is important so that the conversation can proceed in the correct direction. This is possible only if there is a way of labeling the utterance with its proper intent. One such labeling techniques is Dialog Act (DA) tagging. This work focuses on discussing various n-gram DA tagging techniques. In this paper, a new method is proposed for DA tagging in Telugu using n-gram karakas with back-off as n-gram language modeling technique at n-gram level and Memory Based Learning at utterance level. The results show that the proposed method is on par with manual DA tagging.

Abstract

This paper revisits the work of (Kuncham et al., 2015) which developed a statistical sandhi splitter (SSS) for agglutinative languages that was tested for Telugu and Malayalam languages. Handling compound words is a major challenge for Natural Language Processing (NLP) applications for agglutinative languages. Hence, in this paper we concentrate on testing the effect of SSS on the NLP applications like Machine Translation, Dialogue System and Anaphora Resolution and show that the accuracy of these applications is consistently improved by using SSS. We shall also discuss in detail the performance of SSS on these applications.

Abstract

The challenge of anaphora resolution has been taken up from long time. However, most of the work did not include for dialogues. In this paper we discuss the types of pronouns and anaphora in Telugu language and make an attempt to build a rule based pronominal anaphora resolution algorithm for human to human conversations. The model mainly consists of two parts, creating a knowledge base with a set of pronouns along with its morphological information and designing an algorithm which uses this knowledge base to give an output. In this process we have worked on normal pronominal anaphora and suggested a set of rules applicable for Telugu dialogues. However, since there was no corpus for the Telugu language, we built a corpus and tested the algorithm on it. Results show that the suggested algorithm produced an output with an accuracy of 61.1%.

Abstract

Sandhi splitting is a primary and an important step for any natural language processing (NLP) application for languages which have agglutinative morphology. This paper presents a statistical approach to build a sandhi splitter for agglutinative languages. The input to the model is a valid string in the language and the output is a split of that string into meaningful word/s. The approach adopted comprises of two stages namely Segmentation and Word generation, both of which use conditional random fields (CRFs). Our approach is robust and language independent. The results for two Dravidian languages viz. Telugu and Malayalam show an accuracy of 89.07% and 90.50% respectively.

Abstract

This paper discusses the handling of multisentence queries in a mixed-initiative dialogue system based on a hierarchically structured knowledge base, in a way that is domain independent. The system is rule-based and uses dependency relations and part-of-speech tags obtained from the Stanford Parser coupled with the hierarchical structure of the knowledge base to identify the user’s goal. The system was tested for its accuracy over answering questions, and also subjective testing was done to evaluate the dialogue flow; primarily over the books domain. We show examples of the system developed over the domains of books, movies and restaurants to demonstrate the domain independence.

Abstract

Case-based teaching is a well-known teaching methodology consisting of learning by reading, discussing and analyzing real-life cases and scenarios. We present a Case-Oriented Learning Environment (COSEEd) for teaching Software Engineering concepts to undergraduate and graduate students in a first course of Software Engineering. The novelty of the proposed model lies in being a complete learning environment framework, consisting of pedagogy, broad level learning objectives, assessment, resources and management details, all designed specifically for Software Engineering. Learning and teaching is centered around well-designed SE case studies from authentic software development instances. We describe the COSEEd model, a sample case-study and share out insights as well as lessons learnt while applying the proposed model in practice. We implement and evaluate the proposed model in Software Engineering courses at a University in India focused on the core areas of Information Technology. We use empirical studies on student perception and actual performance to determine the effectiveness of COSEEd towards achieving various learning goals of SE.