Potentially Problematic Word Usages and How to Detect Them: A Survey (2025)

Abstract: We introduce and explore the concept of potentially problematic word usages (PPWUs): word occurrences that are likely to cause communication breakdowns of a semantic nature. While much research has been devoted to lexical complexity, ambiguity, vagueness and related issues, no work has attempted to fully capture the intricate nature of PPWUs. We review linguistic factors, datasets and metrics that can be helpful for PPWU detection. We also discuss challenges to their study, such as their complexity and subjectivity, and highlight the need for future work on this phenomenon.

## introduction language is a powerful communication tool allowing us to exchange complex messages, but information is not always conveyed successfully. miscommunication can be due to multiple factors, both linguistic and non-linguistic (e.g., environmental or psychological causes, such as a noisy background or the listener's lack of attention). in this paper, we focus on cases where a specific use of a word can give rise to a misunderstanding or an objection related to its meaning. we define a potentially problematic word usage (ppwu) as an instance of a word foot_0 in context which is likely to cause some type of miscommunication (misunderstanding or non-understanding) or disagreement of a semantic nature. 2 for example, in "i enjoy working on my car", it is not clear whether working means polishing it as a hobby or repairing it (see the detection of ppwus has several applications. it can be useful for text simplification and readability assessment and can also have uses in applications related to language learning in general, such as aiding in choosing the right learning materials to adapt them to a student's level, or designing exercises that target specific types of pp-wus. if integrated with a conversational system, ppwus could either be actively avoided in production, or trigger directed clarification requests to the user. ensuring clear language use contributes to decrease misunderstandings, which can have negative psychological and physiological effects @xcite . as part of a writing assistance tool, it could help identify words to be replaced to improve clarity. it could also be used to detect lexical errors in translated text. numerous studies focus on specific types or causes of ppwus, but a unifying perspective encompassing them in their full complexity is missing. in this paper, we introduce and frame the notion of ppwu bringing together insights and points of view from research on different domains (e.g, psycholinguistics, computational linguistics, cognitive science, nlp) and with different goals, providing a foundation and framework for future research on ppwu detection. to this end, we compile and outline multiple reasons why a specific word or word usage is likely to cause miscommunication or be disagreed upon, thereby proposing a first character-ization of ppwus. we also present existing computational methods, resources and datasets that can be helpful for the study and detection of ppwus, identifying areas where more work and knowledge is necessary; and include a discussion on various considerations to make when conducting research on ppwus. to compile the bibliography for this survey, we searched for relevant literature on google scholar using keywords such as "polysemy detection," "false friends," "lexical complexity," "text readability," or "lexical errors;" and expanded the search by examining papers citing, or cited by, the returned papers. ## factors contributing to ppwus in this section we provide a non-exhaustive foot_2 list of linguistic properties and phenomena linked to ppwus. we distinguish between factors tied exclusively to word identity (section 2.1) and factors linked to the linguistic context where a word is used (section 2.2). 4 in the appendix, we provide examples ( ## data in this section we describe the kinds of data that can be used to investigate ppwus or to train systems to automatically detect them. our focus is on datasets of actual pwus where there is evidence of speaker differences in word meaning. for datasets dedicated to the linguistic factors described in section 2, refer to non-dialogical types of data exposing word usage or comprehension differences between speakers are presented in sections 3.2 and 3.3. ## methods we present here metrics, tools, features and methods that can be useful for ppwu prediction, for example because they have been shown to correlate with, or help predict, factors linked to ppwus. we do not discuss supervised models tailored to the specific nlp tasks. ## open directions and challenges as we have shown, there is a substantial amount of work on detecting individual phenomena or word characteristics associated with ppwus. for the study of communicative success and failure, as well as for the outlined applications of ppwu detection, we argue that it is worth aspiring to address ppwus in their full diversity, encompassing the multiple factors presented, but no such approach currently exists. 10 the present survey aims to provide an initial overarching perspective that sets the ground for future work. one key reason for the lack of a comprehensive approach is the complexity and the varied nature of ppwus. the most effective solution may involve a combination of multiple approaches tailored to specific purposes, audiences, situations, or ppwu types. a big gap preventing progress is the scarcity of large, good-quality datasets annotated with real pwus. annotation of wmns is helpful but costly; recent efforts striving toward their semi-automatic annotation @xcite will contribute to a better understanding of the characteristics of pwus and their rate of occurrence, and will provide more data that could eventually serve to train models for (p)pwu detection. existing metrics and methodology (section 4) typically address a particular phenomenon, but it is not always clear what kinds of ppwus they may detect, which ones they may fail to capture, and what kind of false positives they may propose. for example, lm-derived measures can find unexpected usages of words, which is practical for, e.g., lexical error detection, but would probably not be useful to identify contextual underspecification problems. another difficulty lies in the subjectivity of pp-wus. as mentioned in section 1, what constitutes an actual pwu depends on various characteristics of both the speaker and the listener, such as their age, language proficiency, and any potential language impairments or pathologies. it is therefore important to take annotator characteristics into account when collecting data for ppwu detection, and more data and studies are needed to determine the types of usages that are problematic for each type of audience. differences are not necessarily restricted to specific groups -individual variation can occur even within a community, such as among language learners @xcite . while the individual component should not be neglected, and it may be desirable to restrict the scope to usages problematic for a specific population, it is also possible to identify usages that are problematic across communities. such usages may involve errors, underspecification, or jokes; where the issue comes from the production side and depends less on the listener's characteristics. unsignaled ppwus present an additional challenge. not all ppwus result in communication breakdowns: many misunderstandings and disagreements are not signaled, either because they go undetected or because they do not pose a real problem to continue communication. a consequence of this is that when collecting evidence of ppwus in conversation, an undetermined number of ppwus are prone to be overlooked. identifying keywords within a dialog may help determine the words that are critical for the conversation to move forward. in a conversational system, this could help restrict the words for which clarification is needed. it is possible that ppwus worthy of being signaled are more likely to be located in the parts of utterances that introduce new information (i.e., the comment, rather than the topic or theme, in information structure @xcite ). although the perspective proposed here is intended to be language-independent, there may also be language-and culture-specific causes of pp-wus, and ways of adapting metrics and tools to specific languages. whether a ppwu is signaled in a conversation may have a cultural component and be influenced by different politeness norms, potentially causing differences in the frequency with which they are observed across languages. ## conclusion we have introduced the notion of potentially problematic word usages (ppwus) and reviewed work from various disciplines to provide a comprehensive perspective of related linguistic factors, methods for their detection, and the kinds of data that can be used for their study. we have also discussed challenges to take into account when working with ppwus, and identified areas for future work. ppwus are a complex and understudied phenomenon. they are affected by multiple factors, both linguistic (word and contextual properties) and non-linguistic (e.g., interlocutor characteristics and differences between them). more work is needed to broaden our understanding of ppwus, their underlying causes and their prevalence, but datasets containing evidence of miscommunication due to specific word usages are scarce. one of our long-term goals is to develop methods aimed at capturing ppwus in their many forms, rather than focusing on just one subtype. with this work, we have laid out a framework organizing existing knowledge and methods, pointing out the diversity of problems that must be considered when tackling ppwus, in order to serve as a stepping stone for future research on this multifaceted phenomenon.