Recognition of Mental Adjectives in An Efficient and Automatic Style

TL;DR

This paper introduces a new lexical inference task called Mental and Physical Classification (MPC) to improve commonsense reasoning by categorizing words into mental and physical groups using a fine-tuned BERT model with active learning, achieving high accuracy with minimal labeled data.

Contribution

It proposes the MPC task for better commonsense reasoning and demonstrates an efficient training approach with active learning and BERT, reducing annotation effort.

Findings

The ENTROPY strategy achieves high accuracy with only 300 labeled words.

MPC differs from sentiment analysis in classifying mental and physical concepts.

The approach effectively distinguishes mental from physical words in reasoning tasks.

Abstract

In recent years, commonsense reasoning has received more and more attention from academic community. We propose a new lexical inference task, Mental and Physical Classification (MPC), to handle commonsense reasoning in a reasoning graph. Mental words relate to mental activities, which fall into six categories: Emotion, Need, Perceiving, Reasoning, Planning and Personality. Physical words describe physical attributes of an object, like color, hardness, speed and malleability. A BERT model is fine-tuned for this task and active learning algorithm is adopted in the training framework to reduce the required annotation resources. The model using ENTROPY strategy achieves satisfactory accuracy and requires only about 300 labeled words. We also compare our result with SentiWordNet to check the difference between MPC and subjectivity classification task in sentiment analysis.