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.

Abstract

Software Engineering (SE) educators worldwide are attempting to create learning environments that can effectively achieve their desired learning objectives. However, there exist additional needs that impact the learning process and the overall quality of a learning environment. We identified two sets of differentiating requirements, Climatic and Systemic, whose inclusion in design can assist in an effective, sustainable and usable SE learning environment. In this paper, we will describe the Systemic requirements, i.e. the desired system wide capabilities that impact the sustainability of a SE learning environment by affecting its operationalization and use in short and long term. We will also discuss, through few examples, the interactions between various differentiating requirements. Current SE course design and evaluation consider these as challenges to deal later, instead of addressing them through a conscientious design. Such courses find it hard to sustain and evolve with time, despite using powerful pedagogies. We intend to change this design approach by identifying and recording the various needs (as requirements) and their influence on the learning environment. Our aim is to draw attention to these differentiating requirements and help the educators look beyond learning objectives and move towards a more holistic and systematic design of SE learning environments.

Abstract

For the task assignment problem in an expert crowdsourcing platform, we propose that the dynamically arriving workers report their preferences for the tasks as ordinal preferences to the platform. We model then the task assignment problem as a dynamic two sided matching problem. In this paper we study the dynamic two sided matching when the men (the workers) side of the market is arriving dynamically and the women (the requesters) side is available since beginning. We assume strict preferences of the agents. Using a deferred acceptance algorithm as a building block, we first develop f AP ODA, a class of strategy-proof online mechanisms. We design f AP ODA and f T hODA in this class. As no mechanism can achieve stability in our settings, we propose a weaker notion of stability, namely, progressive stability. We introduce an online mechanism f RODA that achieve the progressive stability. For achieving good rank-efficiency, we design an online matching mechanism f BOMA. We study all the four mechanisms empirically for stability and rank-efficiency.

Abstract

Hashtags are semantico-syntactic constructs used across various social networking and microblogging platforms to enable users to start a topic specific discussion or classify a post into a desired category. Segmenting and linking the entities present within the hashtags could therefore help in better understanding and extraction of information shared across the social media. However, due to lack of space delimiters in the hashtags (e.g #nsavssnowden), the segmentation of hashtags into constituent entities (“NSA” and “Edward Snowden” in this case) is not a trivial task. Most of the current state-of-the-art social media analytics systems like Sentiment Analysis and Entity Linking tend to either ignore hashtags, or treat them as a single word. In this paper, we present a context aware approach to segment and link entities in the hashtags to a knowledge base (KB) entry, based on the context within the tweet. Our approach segments and links the entities in hashtags such that the coherence between hashtag semantics and the tweet is maximized. To the best of our knowledge, no existing study addresses the issue of linking entities in hashtags for extracting semantic information. We evaluate our method on two different datasets, and demonstrate the effectiveness of our technique in improving the overall entity linking in tweets via additional semantic information provided by segmenting and linking entities in a hashtag.

Abstract

Crowdsourcing marketplaces link large populations of workers to an even larger number of tasks. Thus, it is necessary to have mechanisms for matching workers with interesting and suitable tasks. Earlier work has addressed the problem of finding optimal workers for a given set of tasks. However, workers also have preferences and will stay with a platform only if it gives them interesting tasks. We therefore analyze several matching mechanisms that take into account workers’ preferences as well. We propose that the workers pay premiums to get preferred matches and auction-based models where preferences are expressed through variations of the payment for a task. We analyze the properties of two matching different mechanisms: Split Dynamic VCG (SDV) and e-Auction. We compare both the mechanisms with Arrival Priority Serial Dictatorship (APSD) empirically for efficiency.

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.