Search Results for author: Luis Gravano

Found 7 papers, 2 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

Pragmatic Evaluation of Clarifying Questions with Fact-Level Masking

no code implementations17 Oct 2023 Matthew Toles, Yukun Huang, Zhou Yu, Luis Gravano

Here we present a definition and framework for natural language pragmatic asking of clarifying questions (PACQ), the problem of generating questions that result in answers useful for a reasoning task.

Chatbot Question Answering +2

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

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