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Sumit Neelam

dblp:149/5874 · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 since 2021Databases, data management, data science and information retrieval · 1

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
2 papers
Question answering and dialogue systems · 65% Information extraction and text analysis · 25% Knowledge representation and reasoning · 10%
Databases, data mining, and information retrieval
1 paper
Query processing and optimization · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Question answering and dialogue systems
knowledge base question answering
0.712023
Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction · EMNLP 2023
Natural language and speech › Question answering and dialogue systems › reasoning-based question answering
temporal question answering
0.712023
Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction · EMNLP 2023
Natural language and speech › Information extraction and text analysis
entity linking
0.512021
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking · ACL/IJCNLP (1) 2021
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition › knowledge extraction
fact extraction
0.212023
Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction · EMNLP 2023
Query processing and optimization › query optimization
robust query processing
0.212014
QUEST: An Exploratory Approach to Robust Query Processing · Proc. VLDB Endow. 2014

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

open-domain question answering · 0.7language model · 0.7neuro-symbolic reasoning · 0.5logic neural network · 0.5
YearPublicationVenuePosition
2023 Best of Both Worlds: Towards Improving Temporal Knowledge Base Question Answering via Targeted Fact Extraction
abstract
Temporal question answering (QA) is a special category of complex question answering task that requires reasoning over facts asserting time intervals of events.Previous works have predominately relied on Knowledge Base Question Answering (KBQA) for temporal QA.One of the major challenges faced by these systems is their inability to retrieve all relevant facts due to factors such as incomplete KB and entity/relation linking errors (Patidar et al., 2022).A failure to fetch even a single fact will block KBQA from computing the answer.Such cases of KB incompleteness are even more profound in the temporal context.To address this issue, we explore an interesting direction where a targeted temporal fact extraction technique is used to assist KBQA whenever it fails to retrieve temporal facts from the KB.We model the extraction problem as an open-domain question answering task using offthe-shelf language models.This way, we target to extract from textual resources those facts that failed to get retrieved from the KB.Experimental results on two temporal QA benchmarks show promising ~30% & ~10% relative improvements in answer accuracies without any additional training cost.
Nithish Kannen, Udit Sharma, Sumit Neelam, Dinesh Khandelwal, Shajith Ikbal, Hima P. Karanam, L. Venkata Subramaniam
EMNLP3
2021 LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
abstract
Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa 0001, Prithviraj Sen, Yunyao Li 0001, Alexander G. Gray
ACL/IJCNLP (1)4
2014 QUEST: An Exploratory Approach to Robust Query Processing
abstract
research-article Share on QUEST: an exploratory approach to robust query processing Authors: Anshuman Dutt Indian Institute of Science, Bangalore, INDIA Indian Institute of Science, Bangalore, INDIAView Profile , Sumit Neelam Indian Institute of Science, Bangalore, INDIA Indian Institute of Science, Bangalore, INDIAView Profile , Jayant R. Haritsa Indian Institute of Science, Bangalore, INDIA Indian Institute of Science, Bangalore, INDIAView Profile Authors Info & Claims Proceedings of the VLDB EndowmentVolume 7Issue 13August 2014 pp 1585–1588https://doi.org/10.14778/2733004.2733036Published:01 August 2014Publication History 3citation41DownloadsMetricsTotal Citations3Total Downloads41Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Anshuman Dutt, Sumit Neelam, Jayant R. Haritsa
Proc. VLDB Endow.2