Abstract

Software engineering (SE) courses aim to make students well-versed in solving authentic and complex problems by applying varied SE knowledge skills along with problem solving, critical thinking, use of tools, communication skills etc. Thus they have multiple, complex and some higher order cognitive learning goals. Traditional assessment tools like multiple choice questions, subjective questions, etc., may not always be an optimal choice for such a SE course and may not satisfy learning sciences principle and guidelines of assessment design. Case studies in SE have been used since long for research and investigation, recently in teaching, but not as assessment tools. In this paper, we propose and examine the use of SE case studies as assessment tools. We also describe an approach for objective and easy evaluation of solutions. This work reasons that carefully designed case studies can be effective as well as efficient assessment tools for SE education such that they are not only closely aligned with learning goals, but are also in accordance with learning sciences.

Abstract

A software architect engages in solving Software Engineering (SE) problems throughout his career. Thus inculcating problem solving skills should be one of the learning objectives of SE academic and training programs. But structured problem solving is usually latent or missing in most of the current curriculum. In this paper, we describe an effective learning environment for SE education and training with problem solving as an integral part.

Abstract

Automatic Document summarization is proving to be an increasingly important task to overcome the information overload. The primary task of document summarization process is to pick subset of sentences as a representative of whole document set. We treat this as a decision making problem and estimate the risk involve in making this decision. We calculate the risk of information loss associated with each sentence and extract sentences based on ascending order of their risk. The experimental result shows that the proposed approach performs better than various state of the art approaches.

Abstract

A new method for sentence extraction on the basis of language model with relative entropy is presented in this paper. The proposed technique first builds a sentence language model and document cluster language model respectively for the sentence and the documents. The sentences are then ranked according to the relative entropies of the estimated document language model with respect to the estimated sentence language model. The overall results on DUC and MSE corpus demonstrate that the proposed approach outperforms some of the best reported results for generic multi-document summarization.

Abstract

Telugu is the third most spoken language in India and one of the fifteen most spoken languages in the world. But, there is no standardized input method for Telugu, which has a widespread use. Since majority of users of Telugu typing tools on the computers are familiar with English, we propose a transliteration based text input method in which the users type Telugu using Roman script. We have shown that simple edit-distance based approach can give a light-weight system with good efficiency for a text input method. We have tested the approach with three datasets – general data, countries and places and person names. The approach has worked considerably well for all the datasets and holds promise as an efficient text input method.

Abstract

In this paper, we describe a sentence position based summarizer that is built based on a sentence position policy, created from the evaluation testbed of recent summarization tasks at Document Understanding Conferences (DUC). We show that the summarizer thus built is able to outperform most systems participating in task focused summarization evaluations at Text Analysis Conferences (TAC) 2008. Our experiments also show that such a method would perform better at producing short summaries (upto 100 words) than longer summaries. Further, we discuss the baselines traditionally used for summarization evaluation and suggest the revival of an old baseline to suit the current summarization task at TAC: the Update Summarization task.

Abstract

Cross Language Information Retrieval (CLIR) between languages of the same origin is an interesting topic of research. The similarity of the writing systems used for these languages can be used effectively to not only improve CLIR, but to overcome the problems of textual variations, textual errors, and even the lack of linguistic resources like stemmers to an extent. We have conducted CLIR experiments between three languages which use writing systems (scripts) of Brahmi-origin, namely Hindi, Bengali and Marathi. We found significant improvements for all the six language pairs using a method for fuzzy text search based on Surface Similarity. In this paper we report these results and compare them with a baseline CLIR system and a CLIR system that uses Scaled Edit Distance (SED) for fuzzy string matching.

Abstract

In the context of the Document Understanding Conferences, the task of Query-Focused Multi-Document Summarization is intended to improve agreement in content among humangenerated model summaries. Query-focus also aids the automated summarizers in directing the summary at specific topics, which may result in better agreement with these model summaries. However, while query focus correlates with performance, we show that highperforming automatic systems produce summaries with disproportionally higher query term density than human summarizers do. Experimental evidence suggests that automatic systems heavily rely on query term occurrence and repetition to achieve good performance.

Abstract

Document Retrieval assumes that a document is independent of its relevance, and non-relevance. Previous works showed that the same assumption is being considered for passage retrieval in the context of Question Answering. In this paper, we relax this assumption and describe a method for estimating the prior of a passage being relevant, and non-relevant to a question. These prior probabilities are used in the process of ranking passages. We also describe a trivial method for identifying relevant and nonrelevant text to a question using the Web and AQUAINT corpus as information sources. An empirical evaluation on TREC 2006 Question Answering test set showed that in the context of Question Answering prior probabilities are necessary in ranking the passages.

Abstract

Retrieving answer containing passages is a challenging task in Question Answering. In this paper we describe a novel query expansion method which aims to rank the answer containing passages better. It uses content and structured information (link structure and category information) of Wikipedia to generate a set of terms semantically related to the question. As Boolean model allows a fine-grained control over query expansion, these semantically related terms are added to the original query to form an expanded Boolean query. We conducted experiments on TREC 2006 QA data. The experimental results show significant improvements of about 24.6%, 11.1% and 12.4% in precision at 1, MRR at 20 and TDRR scores respectively using our query expansion method.