NLPExplorer

Shrikanth Narayanan

Number of Papers:- 35

Number of Citations:- 41

First ACL Paper:- 1998

Latest ACL Paper:- 2023

Venues:-

NUSE

NAACL

SIGDIAL

COLING

NLPMC

EMNLP

Findings

HLT

LREC

ACL

*SEMEVAL

Co-Authors:-

Abe Kazemzadeh

Abhinav Sethy

Alexandra Chronopoulou

Alexandros Potamianos

Angela Nazarian

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Domain Adaptation for Sentiment Analysis Using Robust Internal Representations F i n d i n g s - E M N L P

Mohammad Rostami | Digbalay Bose | Shrikanth Narayanan | Aram Galstyan |

Character Coreference Resolution in Movie Screenplays F i n d i n g s - A C L

Sabyasachee Baruah | Shrikanth Narayanan |

Clinical note section classification on doctor-patient conversations in low-resourced settings NLPMC WS

Zhuohao Chen | Jangwon Kim | Yang Liu | Shrikanth Narayanan |

Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios F i n d i n g s - E M N L P

Annotation and Evaluation of Coreference Resolution in Screenplays Findings

Sabyasachee Baruah | Sandeep Nallan Chakravarthula | Shrikanth Narayanan |

Towards end-2-end learning for predicting behavior codes from spoken utterances in psychotherapy conversations ACL

Karan Singla | Zhuohao Chen | David Atkins | Shrikanth Narayanan |

Screenplay Quality Assessment: Can We Predict Who Gets Nominated? ACL NUSE WS

Ming-Chang Chiu | Tiantian Feng | Xiang Ren | Shrikanth Narayanan |

Joint Estimation and Analysis of Risk Behavior Ratings in Movie Scripts EMNLP

Victor Martinez | Krishna Somandepalli | Yalda Tehranian-Uhls | Shrikanth Narayanan |

A Multi-task Approach to Learning Multilingual Representations ACL

Karan Singla | Dogan Can | Shrikanth Narayanan |

NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning *SEMEVAL

Linguistic analysis of differences in portrayal of movie characters ACL

Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter *SEMEVAL

Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation *SEMEVAL

A quantitative analysis of gender differences in movies using psycholinguistic normatives EMNLP

Anil Ramakrishna | Nikolaos Malandrakis | Elizabeth Staruk | Shrikanth Narayanan |

A Dynamic Programming Algorithm for Computing N-gram Posteriors from Lattices EMNLP

Doğan Can | Shrikanth Narayanan |

SAIL-GRS: Grammar Induction for Spoken Dialogue Systems using CF-IRF Rule Similarity *SEMEVAL

Kalliopi Zervanou | Nikolaos Malandrakis | Shrikanth Narayanan |

SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features *SEMEVAL

DeepPurple: Lexical, String and Affective Feature Fusion for Sentence-Level Semantic Similarity Estimation *SEMEVAL

SAIL: A hybrid approach to sentiment analysis *SEMEVAL

Nikolaos Malandrakis | Abe Kazemzadeh | Alexandros Potamianos | Shrikanth Narayanan |

Which ASR should I choose for my dialogue system? SIGDIAL WS

The Twins Corpus of Museum Visitor Questions LREC

A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle ACL

Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers NAACL

Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics NAACL