AdjScales: Visualizing Differences between Adjectives for Language Learners

Abstract

SUMMARYIn this study we introduce AdjScales, a method for scaling similar adjectives by their strength. It combines existing Web-based computational linguistic techniques in order to automatically differentiate between similar adjectives that describe the same property by strength. Though this kind of information is rarely present in most of the lexical resources and dictionaries, it may be useful for language learners that try to distinguish between similar words. Additionally, learners might gain from a simple visualization of these differences using unidimensional scales. The method is evaluated by comparison with annotation on a subset of adjectives from WordNet by four native English speakers. It is also compared against two non-native speakers of English. The collected annotation is an interesting resource in its own right. This work is a first step toward automatic differentiation of meaning between similar words for language learners. AdjScales can be useful for lexical resource enhancement.

Learning Scalar Adjective Intensity from Paraphrases

“…Approaches to the task of ranking scalar adjectives by their intensity mostly fall under the paradigms of pattern-based or lexicon-based approaches. Pattern-based approaches work by extracting lexical (Sheinman and Tokunaga, 2009;de Melo and Bansal, 2013;Sheinman et al, 2013) or syntactic (Shivade et al, 2015) patterns indicative of an intensity relationship from large corpora. For example, the patterns "X, but not Y" and "not just X but Y" provide evidence that X is an adjective less intense than Y.…”

“…As a result, there have been efforts to automate the process of learning intensity relations (e.g. Sheinman and Tokunaga (2009), de Melo and Bansal (2013), Wilkinson (2017), etc.). Many existing approaches rely particularly pleased ↔ ecstatic quite limited ↔ restricted rather odd ↔ crazy so silly ↔ dumb completely mad ↔ crazy Figure 1: Examples of paraphrases from PPDB of the form RB JJ u ↔ JJ v which can be used to infer pairwise intensity relationships (JJ u < JJ v ).…”

Good, Great, Excellent: Global Inference of Semantic Intensities

“…Linguistic studies have found lexical patterns like ' but not ' (e.g. good but not great) to reveal order information between a pair of adjectives (Sheinman and Tokunaga, 2009). We assume that we have two sets of lexical patterns that allow us to infer the most likely ordering between two words when encountered in a corpus.…”

“…Previous work on information extraction from limited-sized raw text corpora revealed that coverage is often limited (Hearst, 1992;Hatzivassiloglou and McKeown, 1993). Some studies (Chklovski and Pantel, 2004;Sheinman and Tokunaga, 2009) used hit counts from an online search engine, but this is unstable and irreproducible (Kilgarriff, 2007). To avoid these issues, we use the largest available static corpus of counts, the Google n-grams corpus (Brants and Franz, 2006), which contains English n-grams (n = 1 to 5) and their observed frequency counts, generated from nearly 1 trillion word tokens and 95 billion sentences.…”

“…Previous work (Sheinman and Tokunaga, 2009;Schulam and Fellbaum, 2010;Sheinman et al, 2012) has also used lexico-semantic patterns to or-der adjectives. They mainly evaluate their algorithm on a set of pairwise decisions, but also present a partitioning approach that attempts to form scales by placing each adjective to the left or right of pivot words.…”