Recognition of Mental Adjectives in An Efficient and Automatic Style
Fei Yang
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.