A Unified Feature Representation for Lexical Connotations (2021)

Abstract: Ideological attitudes and stance are often expressed through subtle meanings of words and phrases. Understanding these connotations is critical to recognizing the cultural and emotional perspectives of the speaker. In this paper, we use distant labeling to create a new lexical resource representing connotation aspects for nouns and adjectives. Our analysis shows that it aligns well with human judgments. Additionally, we present a method for creating lexical representations that capture connotations within the embedding space and show that using the embeddings provides a statistically significant improvement on the task of stance detection when data is limited.

## introduction expressions of ideological attitudes are widespread in today's online world, influencing how we perceive and react to events and people on a daily basis. these attitudes are often expressed through subtle expressions or associations @xcite . for example, the sentence "the people opposed gun control" conveys no information about the author's opinion. however, by adding just one word, "the selfish people opposed gun control", the author can convey their stance on both gun control (against) and the people who support it (not valuable and disliked). discerning such subtle meaning is crucial for fully understanding and recognizing the hidden influences behind everyday content. recent studies in nlp have begun to examine these hidden influences through framing in social media and news @xcite @xcite and style detection in hyperpartisan news @xcite . lexical connotations provide a method to study these influences, including stance, in more detail. ## related work studies of connotation build upon the literature examining subtle language nuances, including good and bad effects of verbs @xcite , evoked sentiments and emotions @xcite @xcite , multi-dimensional sentiment @xcite @xcite , offensiveness @xcite , and psychosociological properties of words @xcite . work explicitly on connotations has focused primarily on detailed aspects for verbs @xcite @xcite or single polarities for many parts of speech @xcite @xcite . one exception is the work of @xcite , which extends limited detailed connotation dimensions from verbs to nouns within the context of certain verbs. our work is unique in directly defining detailed aspects for nouns and adjectives. early work on stance detection applied topicspecific models to various genres, including online debate forums @xcite @xcite @xcite and student essays @xcite . more recent studies have used a single model for many topics to predict stance in tweets @xcite @xcite and as part of the fact extraction and verification pipeline @xcite @xcite . @xcite explore the relationship between connotations and stance through verb frames. in contrast, our work studies stance using connotation representations from a learned joint embedding space for words from all parts of speech. recently, @xcite examine representations of political ideology as connotations and its use in information retrieval. representation learning has been used for stance detection of online debates by @xcite , who develop a joint representation of the text and the authors. our work, however, uses a representation of word connotations and does not use any author information (a strong feature in fullysupervised datasets but which may not be available in real-world settings). ## connotation lexicon we build a connotation lexicon for nouns and adjectives by defining six new aspects of connotation. we take inspiration from verb connotation frames and their extensions @xcite , which define aspects of connotation in terms of the agent and theme of transitive verbs. @xcite define six aspects of connotation for verbs (entities', writer's, and reader's perspectives, effect, value, and mental state) in connotation frames (e.g., "suffer" ; negative effect on the agent) and @xcite extend these aspects to include power and agency. we first define the six new aspects of connotation for nouns and adjectives ( §3.1) in our work, then we describe our distant labeling procedure ( §3.2) and human evaluation of the final lexicon ( §3.3). ## conclusion we create a new lexicon with six new connotation aspects for nouns and adjectives that aligns well with human judgments. we also show that the lexicon confirms hypotheses about semantic divergences between synonyms. we then use our lexicon to train a unified connotation representation for words from all parts of speech, yielding an embedding space that captures more connotative information than pre-trained word embeddings. we evaluate our connotation representations on stance detection. since the stance detection tasks encountered in real life concern a very large number of topics, zero-shot and few-shot stance detection are important subtasks. we show that models using our connotation representations are well suited for few-shot stance detection and may also generalize well to zero-shot settings. in future work, we plan to explore the relationships between connotations, context, and word sense, as well as adapting our methods to learn multi-lingual connotation representations that accurately capture cultural and linguistic variations. chang xu, cécile paris, surya nepal, and ross sparks.