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Sasi Prasanth Bandaru

dblp:191/6065 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Information extraction and text analysis · 87% Language models and text generation · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Information extraction and text analysis › morphological analysis
morphological tagging
0.312018
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit · EMNLP 2018
Natural language and speech › Information extraction and text analysis
word segmentation
0.312018
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit · EMNLP 2018

Methods — techniques the papers use, named apart from their topics

energy-based model · 0.3
YearPublicationVenuePosition
2018 Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit
abstract
Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.
Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru, Gaurav Sahu, Vishnu Dutt Sharma, Pavankumar Satuluri, Pawan Goyal 0002
EMNLP3
2016 Word Segmentation in Sanskrit Using Path Constrained Random Walks
abstract
In Sanskrit, the phonemes at the word boundaries undergo changes to form new phonemes through a process called as sandhi. A fused sentence can be segmented into multiple possible segmentations. We propose a word segmentation approach that predicts the most semantically valid segmentation for a given sentence. We treat the problem as a query expansion problem and use the path-constrained random walks framework to predict the correct segments.
Amrith Krishna, Bishal Santra, Pavankumar Satuluri, Sasi Prasanth Bandaru, Bhumi Faldu, Yajuvendra Singh, Pawan Goyal 0002
COLING4