Understanding Interpersonal Variations in Word Meanings via Review Target Identification

Daisuke Oba, Shoetsu Sato, Naoki Yoshinaga, Satoshi Akasaki, Masashi Toyoda

Abstract: When people verbalize what they felt with various sensory functions, they could represent different meanings with the same words or the same meaning with different words; we might mean a different degree of coldness when we say ‘this beer is icy cold,’ while we could use different words such as “yellow” and “golden” to describe the appearance of the same beer. These interpersonal variations in word meanings not only prevent us from smoothly communicating with each other, but also cause troubles when we perform natural language processing tasks with computers. This study proposes a method of capturing interpersonal variations of word meanings by using personalized word embeddings acquired through a task of estimating the target (item) of a given reviews. Specifically, we adopt three methods for effective training of the item classifier; (1) modeling reviewer-specific parameters in a residual network, (2) fine-tuning of reviewer-specific parameters and (3) multi-task learning that estimates various metadata of the target item described in given reviews written by various reviewers. Experimental results with review datasets obtained from ratebeer.com and yelp.com confirmed that the proposed method is effective for estimating the target items. Looking into the acquired personalized word embeddings, we analyzed in detail which words have a strong semantic variation and revealed some trends in semantic variations of the word meanings.