Demo Data from EDBT 2026 / reviewers in the wild / expert
Sasi Prasanth Bandaru
dblp:191/6065
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Information extraction and text analysis › morphological analysis
morphological tagging |
0.3 | 1 | 2018 | 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 |
0.3 | 1 | 2018 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in SanskritabstractAmrith 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 |
EMNLP | 3 |
| 2016 | Word Segmentation in Sanskrit Using Path Constrained Random WalksabstractIn 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 |
COLING | 4 |