Distinguishing Address vs. Reference Mentions of Personal Names in Text (2023)

Abstract: Detecting named entities in text has long been a core NLP task. However, not much work has gone into distinguishing whether an entity mention is addressing the entity vs. referring to the entity; e.g., John, would you turn the light off? vs. John turned the light off . While this distinction is marked by a vocative case marker in some languages, many modern Indo-European languages such as English do not use such explicit vocative markers, and the distinction is left to be interpreted in context. In this paper, we present a new annotated dataset that captures the address vs. reference distinction in English, an automatic tagger that performs at 85% accuracy in making this distinction, and demonstrate how this distinction is important in NLP and computational social science applications in English language.

## introduction named entity recognition (ner) in text has long been a core task in the nlp community @xcite . however, not much work has looked into distinguishing whether an entity mention is an instance of addressing the entity or referring to them: the address usage is also called a vocative phrase: "a noun phrase which does not belong to the thematic grid of a predicate and is used to attract someone's attention" @xcite . many languages have explicit morphological vocative case markers: e.g., in "et tu, brute?", brute marks the vocative case of the nominative brutus. however, many 1 https://stavatir.com/s/address-vs-reference.xlsx modern indo-european languages, including english, do not have vocative case markers, and the distinction is left to be interpreted based on context. distinguishing vocative phrases is important in many nlp tasks, such as sentiment analysis @xcite , offensiveness detection @xcite and information extraction @xcite . for instance, @xcite point out the difference in interpretations between "let's eat, grandma" and "let's eat grandma". the vocative distinction is also important for nlp-aided computational social sciences, since the pragmatics and the patterns of usage vary between these two types of name mentions @xcite , and since name mentions capture various societal biases @xcite . this aspect is especially crucial in studies analyzing political discourse, with the goal of understanding the rhetoric by and about political personalities @xcite . despite the prevalence of ner as a useful task in various nlp applications @xcite , efforts to make this distinction have largely been limited to languages that have explicit vocative case markers such as portuguese @xcite , hebrew @xcite , @xcite @xcite , and not much work has looked into detecting vocative name mentions in english. in this paper, we present a dataset of social media text in the political domain in english language, with person mentions annotated with the address vs. reference distinction. we then build a tagger that is able to make this distinction automatically, with an accuracy of 85%. we use this tagger to demonstrate the importance of this distinction in two largescale computational socio-linguistic analysis. first, we demonstrate that female personalities are more likely to be mentioned in the addressing context than male personalities, across three different social medial corpora, which has implications for nlp research on gender bias in data and models. second, we demonstrate that sentences with address mentions are significantly more likely to be toxic than those with reference mentions. this finding has important implications for the active area of nlp research on detecting online abuse. ## address vs. reference mentions how a person is addressed or referenced in language, and its associated pragmatics has long been of interest in sociolinguistics @xcite . while most of this research focused on the different address pronouns and the t/v distinction, much less work has looked into the difference in the social meaning of a mention when used as an address vs. when used as a reference @xcite . while this distinction is not limited to persons (for instance, organizations may also be mentioned in an addressing context, as in hey doordash, where is my food?), person name mentions add additional nuance owing to the social relations. for instance, @xcite show that the words used to address a person by a speaker may differ from the words used to refer to them depending on the social power relations between the speaker, the referent, and the addressee. forms of address has been studied in nlp-aided computational sociolinguistics, for instance, in the context of how they relate to social power relations @xcite . the address vs. references distinction has also been shown to be of value in nlp tasks, for instance, @xcite extracts arabic tweets with the vocative particle "ya" as it indicates directing speech to a person or a group, increasing the likelihood of offensiveness. however nlp work on making this distinction is largely limited to languages that have explicit vocative case markers. in the absence of any vocative markers, as in english, this becomes a task that relies on the syntactic context. in this paper, we build resources to perform and evaluate this distinction, and demonstrate its utility in nlp applications. there is related work in nlp on detecting addressees in multi-party dialog (op den akker and op den @xcite @xcite , which is a substantially different task from ours. first, addressee detection in multi-party dialog takes into account the larger dialog/content context (e.g., prior utterances). for instance, ouchi and tsuboi (2016) jointly captures "who is talking about what at each time step" in order to determine the addressee. ours is a simple linguistic task that relies on the local syntactic context of named mentions, making it applicable in broader contexts. second, the above work crucially looks into the implicit cues about addressees. in contrast, our work focuses only on explicit mentions, primarily motivated by the computational social science analyses anchored on them. ## gender effects in addressing we first look into the rtgender dataset @xcite built to study differential responses to gender. they found that responses to female posters or speakers were more likely to be about the individuals (e.g., their appearance) rather than about the content they posted or talked about. as a complementary analysis, we analyze whether these responses were addressed to the speaker or poster, or referring to them. we apply the tagger to 5k comments each, chosen at random, from three different sub-corpora in the rtgender corpus: comments in response to (1) facebook posts by politicians (fb congress), (2) facebook posts by celebrities (fb wiki), and (3) ted talk videos (ted talks). we ensured that the tagger does not introduce systematic gender bias; t-test revealed no association between gender and error (@xmath0.166). across board, mentions of female personalities were more likely to be in the address rather than reference contexts (figure 1 ). this difference was statistically significant in all three cases: t(4999) = 3.51, @xmath1.001 (fb congress); t(4999) = 3.87, @xmath2.001 (fb wiki); and t(4999) = 4.41, @xmath3.001 (ted talks). for the congress dataset, we also have access to the political party they belong to; we added it as a control causing the effect size to decrease (2.72) suggesting that political party affiliation plays an important role. in fact, figure 2 shows that the gender disparity is present only for the republican party politicians. addressing someone directly could be an expression of friendliness or familiarity, and its prevalence in comments directed at female personalities is notable. these insights enable adding nuance to many nlp-aided studies of gender and power. moreover, this finding adds to research on gender influences on communication with and about professionals @xcite . ## address vs. reference and toxicity we now turn to online abuse detection, an nlp task where address vs. reference distinction is important. prior work has shown that 2nd person pronouns are spuriously associated with toxic comments @xcite . in languages such as arabic that has explicit vocative markers, researchers have used vocative markers to curate comments with higher likelihood of offensiveness @xcite . in this section, we use our tagger to analyze the toxicity dataset annotated by jigsaw @xcite to see if this pattern holds true. in the jigsaw dataset, we do not have access to the mentions of people in text. hence, we created a tagger for the jigsaw dataset by first using the spacy python package to detect person mentions, then used the bert slotfilling (s3) tagger to detect whether each person is addressed or referenced in the message. we find significant difference in address vs. reference in toxic vs. non-toxic tweets. the average toxicity score of sentences with address mentions were 0.088, compared to 0.070 for those without; this difference is statistically significant using the standard student's t-test (@xmath4.001) and a permutation test (@xmath5.001). figure 3 shows differences in the ratios of address to reference mentions in toxic and non-toxic texts. this finding is important for nlp-aided content moderation, especially in detecting targets of abuse. ## discussion/conclusion in this paper, we introduced the basic nlp task of distinguishing a name mention to be address or reference, annotated a new dataset for it in the english language, and presented a simple tagger using contextual word embeddings. our annotation and tagging experiments reveal this to be a relatively easy task, however our accuracy being only at 85% suggests room to improve. we also demonstrate the utility of this capability in computational social science work anchored on name mentions through two analyses: first, on gender bias in mention patterns, and second, in toxic comments online. this capability is important, but often ignored, for tasks that assume entity mentions to be part of the expressed propositional meaning; e.g., belief modeling @xcite , and social relation extraction @xcite . it will also aid in tasks that model relationships between interactants, such as power (prabhakaran and rambow, 2014) and influence @xcite . the vocative usage is arguably already being implicitly modeled in tasks such as dialog act tagging. however, it may be important to model it explicitly in certain cases, e.g., our work could contribute to ongoing efforts in detecting addressees in multi-party dialog @xcite . future work should look into these applications, and more advanced modeling techniques such as few-shot training for this task.