Human-Attention-for-Text-Classification

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Human-Attention-for-Text-Classification

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A comparison of human attention with computational attention mechanisms

Human-Attention-for-Text-Classification 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Human-Attention-for-Text-Classification
A comparison of human attention with computational attention mechanisms

File format:
Input.label,    Input.text,    Answer.Q1Answer,    Answer.html_output

Input.label: Original Yelp label for the review, converted to 0 (negative) or 1 (positive). One and two-star reviews are mapped to 0, four and five-star reviews are converted to 1. 
Input.text: Original review from the Yelp dataset.
Answer.Q1Answer: Sentiment label collected from the annotator
Answer.html_output: Human attention map (HAM) collected from the annotator.

One exception is "ham_part7.csv" file contains 2 to 4 HAMs per review instead of standard 3 HAMs. 
This file will be re-uploaded after removing the reviews with 2 HAMs.

You can use the "visualize_single_map>empty-template.html" file for visualizing a single HAM. You should copy "Answer.html_output" field into the relevant part in this file.

You can use the "generate_ham>generate-human-attention.py" file for converting the html format HAM into a binary vector.

Human-Attention-for-Text-Classification

Human-Attention-for-Text-Classification 최신버젼 다운로드

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