Search Results for author: Animesh Prasad

Found 10 papers, 4 papers with code

WING-NUS at SemEval-2017 Task 10: Keyphrase Extraction and Classification as Joint Sequence Labeling

no code implementations SEMEVAL 2017 Animesh Prasad, Min-Yen Kan

We describe an end-to-end pipeline processing approach for SemEval 2017{'}s Task 10 to extract keyphrases and their relations from scientific publications.

Clustering General Classification +2

Bench-Marking Information Extraction in Semi-Structured Historical Handwritten Records

no code implementations17 Jul 2018 Animesh Prasad, Hervé Déjean, Jean-Luc Meunier, Max Weidemann, Johannes Michael, Gundram Leifert

In this report, we present our findings from benchmarking experiments for information extraction on historical handwritten marriage records Esposalles from IEHHR - ICDAR 2017 robust reading competition.

Benchmarking Handwritten Text Recognition +3

Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction

no code implementations NAACL 2019 Animesh Prasad, Min-Yen Kan

Graph Convolutional Networks (GCNs) are a class of spectral clustering techniques that leverage localized convolution filters to perform supervised classification directly on graphical structures.

Clustering Keyphrase Extraction

Distribution augmentation for low-resource expressive text-to-speech

no code implementations13 Feb 2022 Mateusz Lajszczak, Animesh Prasad, Arent van Korlaar, Bajibabu Bollepalli, Antonio Bonafonte, Arnaud Joly, Marco Nicolis, Alexis Moinet, Thomas Drugman, Trevor Wood, Elena Sokolova

This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data.

Data Augmentation

Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues

1 code implementation26 May 2023 Yue Feng, Yunlong Jiao, Animesh Prasad, Nikolaos Aletras, Emine Yilmaz, Gabriella Kazai

Further, it employs a fulfillment representation layer for learning how many task attributes have been fulfilled in the dialogue, an importance predictor component for calculating the importance of task attributes.

Attribute Language Modelling +1

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