Search Results for author: Suraj Maharjan

Found 16 papers, 3 papers with code

Scalable Prompt Generation for Semi-supervised Learning with Language Models

no code implementations18 Feb 2023 YuHang Zhou, Suraj Maharjan, Beiye Liu

In this paper, we propose two methods to automatically design multiple prompts and integrate automatic verbalizer in SSL settings without sacrificing performance.

Few-Shot Learning Natural Language Understanding

Folksonomication: Predicting Tags for Movies from Plot Synopses Using Emotion Flow Encoded Neural Network

no code implementations COLING 2018 Sudipta Kar, Suraj Maharjan, Thamar Solorio

Folksonomy of movies covers a wide range of heterogeneous information about movies, like the genre, plot structure, visual experiences, soundtracks, metadata, and emotional experiences from watching a movie.


Language Identification and Analysis of Code-Switched Social Media Text

no code implementations WS 2018 Deepthi Mave, Suraj Maharjan, Thamar Solorio

In this paper, we detail our work on comparing different word-level language identification systems for code-switched Hindi-English data and a standard Spanish-English dataset.

Language Identification Machine Translation

RiTUAL-UH at SemEval-2017 Task 5: Sentiment Analysis on Financial Data Using Neural Networks

no code implementations SEMEVAL 2017 Sudipta Kar, Suraj Maharjan, Thamar Solorio

In this paper, we present our systems for the {``}SemEval-2017 Task-5 on Fine-Grained Sentiment Analysis on Financial Microblogs and News{''}.

Sentiment Analysis

Detecting Nastiness in Social Media

no code implementations WS 2017 Niloofar Safi Samghabadi, Suraj Maharjan, Alan Sprague, Raquel Diaz-Sprague, Thamar Solorio

Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others.

CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings

no code implementations WS 2016 Mohammed Attia, Suraj Maharjan, Younes Samih, Laura Kallmeyer, Thamar Solorio

The evaluation results of our system on the test set is 88. 1{\%} (79. 0{\%} for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76. 0{\%} (42. 3{\%} when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations.

Binary Classification General Classification +7

Cannot find the paper you are looking for? You can Submit a new open access paper.