Demo Data from EDBT 2026 / reviewers in the wild / expert
Prasanth Kolachina
dblp:66/8155
· DBLP profile ↗
5ranked-venue papers
2as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 5 · 2 first-author
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 |
Learning theory · 100% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Learning theory › learning dynamics
learning curve prediction |
0.1 | 1 | 2012 | Prediction of Learning Curves in Machine Translation · ACL (1) 2012 |
Methods — techniques the papers use, named apart from their topics
learning curve prediction · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Abstract Syntax as Interlingua: Scaling Up the Grammatical Framework from Controlled Languages to Robust PipelinesabstractAbstract syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF. Aarne Ranta, Krasimir Angelov, Normunds Gruzitis, Prasanth Kolachina |
Comput. Linguistics | 4 |
| 2014 | Benchmarking of English-Hindi parallel corpora
Jayendra Rakesh Yeka, Prasanth Kolachina, Dipti Misra Sharma |
LREC | 2 |
| 2012 | Prediction of Learning Curves in Machine Translation
Prasanth Kolachina, Nicola Cancedda, Marc Dymetman, Sriram Venkatapathy |
ACL (1) | 1 |
| 2012 | Parsing Any Domain English text to CoNLL dependencies
Sudheer Kolachina, Prasanth Kolachina |
LREC | 2 |
| 2010 | Grammar Extraction from Treebanks for Hindi and Telugu
Prasanth Kolachina, Sudheer Kolachina, Anil Kumar Singh 0001, Samar Husain, Viswanatha Naidu, Rajeev Sangal, Akshar Bharati |
LREC | 1 |