Anoop Kunchukuttan

47 papers receiving 651 citations

Peers

Anoop Kunchukuttan
Comparison fields: 5 of 34
  • Artificial Intelligence 694
  • Computer Vision and Pattern Recognition 181
  • Language and Linguistics 49
  • Information Systems 42
  • Signal Processing 27
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Countries citing papers authored by Anoop Kunchukuttan

Since Specialization
Citations

This map shows the geographic impact of Anoop Kunchukuttan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Anoop Kunchukuttan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anoop Kunchukuttan more than expected).

Fields of papers citing papers by Anoop Kunchukuttan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anoop Kunchukuttan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anoop Kunchukuttan. The network helps show where Anoop Kunchukuttan may publish in the future.

Co-authorship network of co-authors of Anoop Kunchukuttan

This figure shows the co-authorship network connecting the top 25 collaborators of Anoop Kunchukuttan. A scholar is included among the top collaborators of Anoop Kunchukuttan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Anoop Kunchukuttan. Anoop Kunchukuttan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 5
2 1
3 0
4 0
5 2
6 8
7 32
8 7
9 14
10 39
11
Multilingual Indian Language Translation System at WAT 2018: Many-to-one Phrase-based SMT
4
12
Utilizing Lexical Similarity between Related, Low-resource Languages for Pivot-based SMT
1
13
IIT Bombay’s English-Indonesian submission at WAT: Integrating Neural Language Models with SMT
2
14
Augmenting Pivot based SMT with word segmentation
2
15
Addressing Class Imbalance in Grammatical Error Detection with Evaluation Metric Optimization
1
16
Shata-Anuvadak: Tackling Multiway Translation of Indian Languages
19
17
When Transliteration Met Crowdsourcing : An Empirical Study of Transliteration via Crowdsourcing using Efficient, Non-redundant and Fair Quality Control
12
18
Supertag Based Pre-ordering in Machine Translation.
1
19
IITB System for CoNLL 2013 Shared Task: A Hybrid Approach to Grammatical Error Correction
6
20
Partially modelling word reordering as a sequence labelling problem
2

About Anoop Kunchukuttan

Anoop Kunchukuttan is a scholar working on Artificial Intelligence, Language and Linguistics and Computer Vision and Pattern Recognition, having authored 52 papers that have together received 718 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (48 papers), Topic Modeling (43 papers) and Translation Studies and Practices (8 papers). The work is most often cited by research in Artificial Intelligence (694 citations), Computer Vision and Pattern Recognition (181 citations) and Language and Linguistics (49 citations). Anoop Kunchukuttan has collaborated with scholars based in India, Japan and United Kingdom. Frequent co-authors include Mitesh M. Khapra, Pushpak Bhattacharyya, Pratyush Kumar, Divyanshu Kakwani, Satish Golla, Raj Dabre, Sumanth Doddapaneni, Bamdev Mishra, Ritesh Shah and G. Ramesh. Their work appears in journals such as ACM Computing Surveys, Language Resources and Evaluation and Transactions of the Association for Computational Linguistics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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