Language Technologies Research Centre (LTRC)

Abstract Crucial study data in research articles, such as patient details, study design and results, need to be extracted and presented explicitly for the ease of applicability and validity judgment in evidence-based practice. To perform this extraction, we propose to use two soft classifications, one at the sentence level and the other at the word level, and exploit the correlations between them for better accuracy.

Abstract A Progressive summary helps a user to monitor changes in evolving news topics over a period of time. Detecting novel information is the essential part of progressive summarization that differentiates it from normal multi document summarization. In this work, we explore the possibility of detecting novelty at various stages of summarization. New scoring features, Re-ranking criterions and filtering strategies are proposed to identify “relevant novel” information.

Abstract The ACL Anthology was revamped in 2012 to its second major version, encompassing faceted navigation, social media use, as well as author-and reader-generated content and comments on published work as part of the revised frontend user interface. At the backend, the Anthology was updated to incorporate its publication records into a database. We describe the ACL Anthology's previous legacy, redesign and revamp process and technologies, and its resulting functionality.

This paper is an attempt to show that an intermediary
level of analysis is an effective way
for carrying out various NLP tasks for linguistically
similar languages. We describe a
process for developing a simple parser for
doing such tasks. This parser uses a grammar
driven approach to annotate dependency relations
(both inter and intra chunk) at an intermediary
level. Ease in identifying a particular
dependency relation dictates the degree of
analysis reached by the parser. To establish efficiency
of the simple parser we show the improvement
in its results over previous grammar
driven dependency parsing approaches for
Indian languages like Hindi. We also propose
the possibility of usefulness of the simple
parser for Indian languages that are similar in nature.

ABSTRACT In this paper, we present our approach towards dependency parsing of Hindi language as a part of Hindi Shared Task on Parsing, COLING 2012. Our approach includes the effect of using different settings available in Malt Parser following the two-step parsing strategy ie splitting the data into interChunks and intraChunks to obtain the best possible LAS1, UAS2 and LA3 accuracy. Our system achieved best LAS of 90.99% for Gold Standard track and second best LAS of 83.91% for Automated data.