Search Results for author: Giannis Karamanolakis

Found 13 papers, 5 papers with code

Quantifying the Effects of COVID-19 on Restaurant Reviews

no code implementations NAACL (SocialNLP) 2021 Ivy Cao, Zizhou Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano

As of now, however, it is not clear how and to what extent the pandemic has affected restaurant reviews, an analysis of which could potentially inform policies for addressing this ongoing situation.

Time Series Analysis

WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding

no code implementations NAACL 2022 Guoqing Zheng, Giannis Karamanolakis, Kai Shu, Ahmed Hassan Awadallah

In this paper, we propose such a benchmark, named WALNUT (semi-WeAkly supervised Learning for Natural language Understanding Testbed), to advocate and facilitate research on weak supervision for NLU.

Natural Language Understanding Weakly-supervised Learning

Self-Training with Weak Supervision

1 code implementation NAACL 2021 Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng, Ahmed Hassan Awadallah

In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task.

text-classification Text Classification

Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only

no code implementations EMNLP (Louhi) 2020 Ziyi Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano

To improve performance without extra annotations, we create artificial training documents in the target language through machine translation and train mBERT jointly for the source (English) and target language.

Machine Translation text-classification +1

Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher

1 code implementation Findings of the Association for Computational Linguistics 2020 Giannis Karamanolakis, Daniel Hsu, Luis Gravano

In this work, we propose a cross-lingual teacher-student method, CLTS, that generates "weak" supervision in the target language using minimal cross-lingual resources, in the form of a small number of word translations.

General Classification Representation Learning +2

Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health

no code implementations WS 2019 Giannis Karamanolakis, Daniel Hsu, Luis Gravano

In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e. g., sentences) of a review may focus on different aspects of the entity in question.

Classification General Classification +4

Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training

1 code implementation IJCNLP 2019 Giannis Karamanolakis, Daniel Hsu, Luis Gravano

In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i. e., weakly positive indicators) for the aspects of interest.

Aspect Category Detection Opinion Mining +2

Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only

no code implementations ICLR Workshop LLD 2019 Giannis Karamanolakis, Daniel Hsu, Luis Gravano

In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i. e., keywords that describe an aspect, are available.

Aspect Extraction Descriptive +3

Item Recommendation with Variational Autoencoders and Heterogenous Priors

no code implementations17 Jul 2018 Giannis Karamanolakis, Kevin Raji Cherian, Ananth Ravi Narayan, Jie Yuan, Da Tang, Tony Jebara

In recent years, Variational Autoencoders (VAEs) have been shown to be highly effective in both standard collaborative filtering applications and extensions such as incorporation of implicit feedback.

Collaborative Filtering

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