Grounding Gradable Adjectives through Crowdsourcing

Abstract

In order to build technology that has the ability to answer questions relevant to national and global security, e.g., on food insecurity in certain parts of the world, one has to implement machine reading technology that extracts causal mechanisms from texts. Unfortunately, many of these texts describe these interactions using vague, high-level language. One particular example is the use of gradable adjectives, i.e., adjectives that can take a range of magnitudes such as small or slight. Here we propose a method for estimating specific concrete groundings for a set of such gradable adjectives. We use crowdsourcing to gather human language intuitions about the impact of each adjective, then fit a linear mixed effects model to this data. The resulting model is able to estimate the impact of novel instances of these adjectives found in text. We evaluate our model in terms of its ability to generalize to unseen data and find that it has a predictive R2 of 0.632 in general, and 0.677 on a subset of high-frequency adjectives.

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