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# Potentially Problematic Word Usages and How to Detect Them: A Survey

Aina Garí Soler, Matthieu Labeau, Chloé Clavel

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## To cite this version:

Aina Garí Soler, Matthieu Labeau, Chloé Clavel. Potentially Problematic Word Usages and How to Detect Them: A Survey. 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025), Nov 2025, Suzhou, China. hal-05337734

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Distributed under a Creative Commons Attribution 4.0 International License
# Potentially Problematic Word Usages and How to Detect Them: A Survey

Aina Garí Soler<fnref n="1" /> Matthieu Labeau² Chloé Clavel<fnref n="1" />

¹INRIA, Paris, ²LTCI, Télécom-Paris, Institut Polytechnique de Paris, France
{aina.gari-soler,chloe.clavel}@inria.fr, matthieu.labeau@telecom-paris.fr

## Abstract

We introduce and frame 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.

## 1 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<fnref n="1" /> 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 Table 1). In practice, PPWUs should be determined with a specific target population in mind. In this paper, we discuss intrinsic factors of words and their contexts which are problematic both for specific communities but also for

the general public (e.g., words with a false friend in a language may only risk being misunderstood or misused by speakers of that language, but a word in an underconstrained context can be problematic for anyone). We say, of a specific word usage, that it is an (actual) problematic word usage (PWU) when there is evidence that it has been misunderstood or disagreed upon by someone (e.g., when someone has signalled it in an interaction, asking “what do you mean by ...?” or similar questions (Noble et al., 2021)).

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 PPWUs. 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 (Crockett et al., 2022). 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-

<footnote n="1">¹We consider open-class lexemes or lexical items in general, but refer to them as “words” for simplicity.
²We exclude cases of unclear referents of referring expressions such as pronouns or proper nouns.</footnote>
ization of PPWUs. We also present existing com-
putational 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.

## 2 Factors contributing to PPWUs

In this section we provide a non-exhaustive<fnref n="3" /> 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 (Table 1) and a diagram summarizing the content of this section (Figure 1).

## 2.1 Context-independent factors

Some words are inherently difficult to understand, regardless of the context they appear in. This is often referred to as **lexical complexity** (North et al., 2023). Here, we outline word characteristics that may be *reasons* for a word not to be understood or for its meaning to be disagreed upon.⁵ Note that the properties presented here are not absolute determinants of PPWUs; they often interact with each other as well as with other linguistic variables.⁶ While we have aimed to define distinct categories where possible, they are not mutually exclusive and can co-occur in real-world usage.

2.1.1 Word properties

**Lexical ambiguity.** Words that have multiple senses (homonyms and polysemous words) are more likely to be misunderstood, even if the context is enough for disambiguation, because the audience might not be familiar with all of their senses, especially if some of them are not very frequent. The number of polysemous words and unique senses in a text has been found to correlate with its readability (Danilov et al., 2023), and the readers’ knowledge of the multiple senses of a word correlates with reading comprehension (Kenneth Logan and Kieffer, 2017; Booton et al., 2022). Martínez Alonso et al. (2015) found that, in a sense annotation task, words with a higher number of senses and sense entropy tend to display higher disagreement. Words that have undergone lexical semantic changes (LSC) can be particularly difficult: readers or listeners are less likely to know novel senses of a word if these are recent, or, if reading a text from a different time period, a word may have changed in a way that the reader may not be aware of (e.g., *gay* used to mean *light-hearted, cheerful*). Approaches to quantifying a word’s number of senses (**polysemy detection**, Garí Soler and Apidianaki (2021a)) and whether, how and when a word has changed meaning (**LSC detection**, Schlechtweg et al. (2020); Montariol et al. (2021)) can be helpful in finding words that are more likely to be misunderstood.

**Word frequency** is one of the most useful features to estimate lexical complexity (Specia et al., 2012; Wilkens et al., 2014; Garí et al., 2018). This is not surprising, as rare words are less likely to be part of a speaker's vocabulary, because they may have had less or no exposure to them. Acronyms, if not commonly used and not introduced properly in a text, are also a common source of confusion. Numerous studies have shown the relationship between word frequency and reading comprehension (Marks et al., 1974; Freebody and Anderson, 1983; Nouri and Zerhouni, 2018), confirming that rarer words are more prone to limit comprehension than more frequent words. Word frequency lists exist for multiple languages (Speer, 2022). Their source is very important: the nature, register, variety and size of the corpus, among other factors, may determine the usefulness of word frequency estimations as features for lexical complexity prediction (Wilkens et al., 2014).

<footnote n="3">³Our goal is to propose a categorization of PPWU causes linked to relevant NLP tasks, but there are other idiosyncratic causes of PPWUs that are difficult to categorize.

⁴This distinction bears some parallels to the notions of “meaning potential” and “situated meaning” (Myrendal, 2019).

⁵Some of the notions presented are used as features in lexical complexity detection. We exclude factors that, while correlated with complexity, are not likely to cause miscommunication on their own (e.g., word length and syllable count (Desai et al., 2021)).

⁶For example, a low-frequency word that is morphologically transparent (*cardiomyopathy*) may be easier to understand than another rare word with an opaque morphology (*gybe*).</footnote>
**Neologisms** are newly created words recently introduced into a language, and which have a certain degree of acceptance in a linguistic community (e.g., *rizz*). As such, speakers (especially non-native ones (Charteris-Black, 1998)) are less likely to know them. An additional difficulty of neologisms is that they are often driven by technological advancements, so certain speakers may be unfamiliar not only with a new word form but also with the concept it expresses. At the same time, neologisms are often coined from existing morphemes (e.g., *mansplaining*) or from other languages, which may make their interpretation easier than that of neologisms created *ex nihilo* (e.g., *cromulent*) (Lehrer, 2003). Although less common, grammatical neologisms (existing words used with a new part of speech (PoS)) can also be problematic. Approaches to **neologism detection** (Janssen, 2012; Falk et al., 2014; Klosa and Lüngen, 2018) can be useful for detecting PPWUs. Finally, semantic neologisms (the appearance of new senses) are a form of lexical semantic change which can be addressed with the task of **novel sense detection** (Section 2.2).

**Lexical variation** refers to differences in lexical choice due to factors such as age, profession and social class (sociolects) or geographical location (dialectal variation). This includes slang, jargon and dialect-specific words which may only be produced and understood by certain communities of speakers. These differences can be particularly problematic in heterogeneous conversations (with people from, e.g., different age groups or cultures). Work on detecting **synchronic lexical differences** (Gonen et al., 2020; Yin et al., 2018; Schlechtweg et al., 2019; Garimella et al., 2016) and **dialect lexicon induction** (Scherrer, 2007; Artemova and Plank, 2023) could help identify words that are distinctive of certain groups of speakers.

**Idioms** are phrases with a non-compositional meaning; i.e., their meaning cannot be inferred from their parts (e.g., *kick the bucket*). As such, they need to be learned as autonomous lexical items. It is well known that idioms are particularly challenging for second language (L2) learners (Schraw et al., 1988; Alhaysony, 2017), except when a similar idiom exists in their native language (Irujo, 1986). Regardless of the frequency of an idiom, familiarity with the words that compose it can give a false perception of understanding. In fact, learners tend to overestimate their comprehension of idioms made up of high frequency words (Mar-

tinez and Murphy, 2011; Park and Chon, 2019); and when analyzing a sample of English idioms, Libben and Titone (2008) found that the frequency of verbs in idioms was negatively correlated with the predictability of their meaning (see Section 5 for more considerations on undetected misunderstanding). Relevant NLP tasks include **idiomatic expression identification** (Zeng and Bhat, 2021), the distinction between **literal and non-literal idiom usages** (Li and Sporleder, 2010), and **compositionality detection** (Cordeiro et al., 2019).

## 2.1.2 Concept-related properties

**Complex meaning.** Words designing complex concepts may be misunderstood because of a lack of or an incomplete knowledge of the reality being described (e.g., *enthalpy*). While estimating the complexity of a concept is a hard and subjective task, as it is not a well-established notion, **automatic term extraction** can be a good proxy to find words designating advanced or technical concepts (Hätty et al., 2020; Rigouts Terryn et al., 2020).

**Vagueness and generality.** Some words have an inherently vague meaning, i.e., a meaning that lacks precision (Van Rooij, 2011). This often concerns gradable adjectives, both relative (such as *big* or *tall*) and absolute (e.g., *bald*, *flat*), but it can also be found in words of other PoS (e.g., *heap*, *idiot*). These words describe qualities for which it is hard to draw a line and which can have multiple interpretations. Pezzelle and Fernández (2023) show that when faced when unclear gradable adjectives, speakers can increase their alignment with explicit interaction about word meaning. Adjectives may inherently be more problematic than other PoS because their semantic contribution to a noun is highly variable and combination-specific (Boleda et al., 2013).

Words that are very general, or high in a hypernymy hierarchy (e.g., *thing* or *do*), may not be specific enough and require clarification or more details. A mismatch between the provided level of specificity and the level required by the communicative situation, i.e., flouting Grice's maximum of quantity (Grice, 1975), can also generate confusion (Cruse, 1977).

**Scalar adjective identification** (Garí Soler and Apidianaki, 2021b) can be useful to identify potentially vague adjectives and distinguishing them from relational ones (e.g., *wooden*). The VAGO system (Icard et al., 2022) for measuring vague-
ness in texts relies on a database with lexical vagueness information (Atemezing et al., 2022). Lexicons and ontologies such as WordNet (Fellbaum, 1998) can serve as references for generality, and work on **semantic content quantification** (Herbelot and Ganesalingam, 2013; Santus et al., 2014) and more specifically on **hypernymy detection** (Shwartz et al., 2016; Cho et al., 2020) can help identify words with a general meaning.

**Connotatively loaded terms.** Words related to or referring to controversial topics are also prone to cause disagreement and misunderstanding: speakers may have different mental representations of these words, which are affected by their own opinions on the topic. For example, speakers may detect a disagreement on what constitutes or qualifies as sexism or abuse and signal this in a conversation (Myrendal, 2019). Work on **controversial topic detection** (Choi et al., 2010; Garimella et al., 2018) as well as on **lexico-semantic alignment** in, or outside of, conversations (Garí Soler et al., 2022, 2023) can potentially help detect this kind of words.

### 2.1.3 Cross-linguistic influence

In this section we describe cross-linguistic factors that may result in PPWUs when the situation involves L2 learners or bilingual individuals in a particular language pair.

**False friends and partial cognates.** Two words in two different languages are said to be false friends if they sound or look similar but have different meanings (e.g., *embarrassed* and Spanish *embarazada*, which means *pregnant*). A related concept is that of partial cognates, where the two similar words share some, but not all, meanings (see Table 1). These words can easily give rise to confusion if they have not been learned properly. Work on **false friend** or **partial cognate detection** (Inkpen et al., 2005; Mitkov et al., 2007; Ljubešić and Fišer, 2013; Palmero Aprosio et al., 2020; Lefever et al., 2020; Kanojia et al., 2021) can help identify words that may be problematic, both in terms of production (Raušer, 2017) and comprehension (Mattheoudakis and Patsala, 2007), when a speaker of a particular language is involved.

**Cross-linguistic inequivalence.** Every language offers different conceptualizations of the world and maps words to referents in different ways (Pavlenko, 2009). There is rarely a 100% translation equivalence between words in two languages,

and often words that are almost equivalent differ in specific nuances that are hard to notice and to master, for language learners (Shalaby et al., 2009) but also bilingual speakers (Ameel et al., 2005). These differences can be quite notorious<fnref n="7" /> or very subtle, such as what is reflected in the categorization and naming of similar objects (Malt et al., 2003; Pavlenko and Malt, 2011). This is sometimes referred to as “cross-linguistic near-synonymy” (Gries et al., 2020) and is studied in the fields of contrastive lexicology and lexicosemantic typology (Schapper and Koptjevskaja-Tamm, 2022). In other cases, for cultural or historical reasons, some terms in a language may not exist at all in another language, because they designate realities that do not exist in the other culture (e.g., Russian **форточка** (fortochka), a specific kind of small window for ventilation). These differences can result in unintended cross-linguistic transfer during production and comprehension by multilingual individuals (Jarvis, 2011), which can lead to misunderstandings. An NLP task that could assist in automatically finding cross-linguistic near-synonyms is **bilingual lexicon induction** (Irvine and Callison-Burch, 2017).

**Words from another language.** Sometimes multilingual speakers interject words from other languages which may be unknown to their interlocutors or readers. **Code switching detection** (Samih et al., 2016; Kevers, 2022) and **language identification** algorithms can detect these usages, particularly when they are tailored to the word level (Solorio et al., 2014; Rijhwani et al., 2017; Ansari et al., 2021).

## 2.2 Context-dependent factors

A word usage may also be misunderstood or disagreed upon because of the characteristics of the context in which it is used, even if the word itself is not usually problematic.<fnref n="8" /> See Table 1 in the Appendix for examples.

**Contextual underspecification.** While many words have multiple senses, context is often enough for disambiguation, and in practice humans do not have a problem understanding many polysemous word usages (Piantadosi et al., 2012). When this

<footnote n="7">⁷meat is often translated as **мясо** (myaso) in Russian, but in its everyday use, **мясо** does not include poultry.</footnote>

<footnote n="8">⁸It is, however, not possible to fully disentangle context from word identity: whether a word's context is problematic strongly depends on the word itself.</footnote>
is not the case, however, ambiguity may require
clarification. This happens with underconstrained
contexts which do not provide enough information
to establish the sense of a word, and which are
a common reason of disagreement in word sense
annotation, especially when allowing only the an-
notation of a single sense (Jurgens, 2014). There
is not much work on identifying such ambiguities
at the lexical level (Liu et al., 2023). Quantifying
the **informativeness** of a context with respect to
a specific target word could be helpful (Montariol
et al., 2019; Schick and Schütze, 2019), but ex-
isting approaches assume that the target word is
unknown.

**Novel senses and metaphors.** A speaker may use a word in a new sense that it has only recently acquired, or propose a creative or figurative use of an existing word. The latter are called novel metaphors (i.e., metaphors that do not rely on established conceptual mappings such as LOVE IS A JOURNEY (Lakoff, 1993)), and are harder to understand than conventional metaphors (Lai et al., 2009; Horvat et al., 2022). There is work on **unknown or novel word sense detection** (Erk, 2006; Cook et al., 2014), as well as **novel metaphor detection** (Schulder and Hovy, 2014; Haagsma and Bjerva, 2016; Reimann and Scheffler, 2024). One challenge is distinguishing these creative uses of language from errors, discussed below.

**Lexical errors.** An existing word may be inap-
propriately used instead of a correct alternative for
multiple reasons. In text, a word can be confused
with a homophone or another similar-sounding
term, causing malapropisms (e.g., *insurance* for
*assurance*). Sometimes a speaker may mix words
that have a similar or related meaning (*broth* and
*stock* or *trip* and *journey*) (Shalaby et al., 2009),
or may use a different word because they can't
come up with the correct one. Lexical errors can
also be due to misspellings which result in another
existing word (so-called “real-word errors,” like
*angel* for *angle*) (Azmi et al., 2019), or be caused
by automatic correction tools. Of course, native
and non-native speakers may make different mis-
takes. There is abundant work studying the types
of lexical errors encountered in essays written by
non-native speakers (Hemchua et al., 2006; Saud,
2018), but not so much about the kinds of mistakes
made by native speakers other than malapropisms
(Hirst et al., 1998), presumably because they are
much rarer and harder to detect. Not all mistakes

are equally confusing, however. While exposed
to linguistic input, humans develop expectations
about what is going to be said, and surface form
similarity may facilitate understanding. There is
also evidence that native speakers adapt their expec-
tations when faced with non-native speech, and are
more likely to find interpretations for implausible
statements (Lev-Ari, 2015; Gibson et al., 2017).

Studies targeting anomalies at the lexical level
aim at detecting text obfuscation (deliberate word
substitutions to encrypt a message (Fong et al.,
2008), for example to bypass censorship (Ji and
Knight, 2018)); at identifying **real-word mis-
spellings** (Samanta and Chaudhuri, 2013; Bravo-
Candel et al., 2021) or detecting **miscollocations**
(Wanner et al., 2013).

**Rare senses.** As discussed in Section 2.1.1, pol-
ysemous words used in a rare sense may be prob-
lematic. Precisely due to their low frequency, Word Sense Disambiguation (WSD) and Neural Machine Translation (NMT) systems also strug-
gle with them (Campolungo et al., 2022a). Efforts
toward **identifying rare word senses** (McCarthy
et al., 2004) and correctly **disambiguating** them
in context (Barba et al., 2021; Hangya et al., 2021;
Campolungo et al., 2022b) can be helpful to find
this kind of PPWUs.

**Conversational Maxims Flouting.** Going
against conversational maxims (Grice, 1975) can
cause confusion for the listener, especially the
maxim of quality, with contradictory statements,
jokes, information that goes against common
sense, or **vandalism** in, for example, collaborative
text editing (Adler et al., 2011). Approaches for
**semantic plausibility** (Ko et al., 2019) and **joke
detection** (Baranov et al., 2023) can be relevant
for these usages, but they are typically designed to
work at the sentence or text level.

3 Data

In this section we describe the kinds of data that
can be used to investigate PPWUs or to train sys-
tems 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 Sec-
tion 2, refer to Table 2 in the Appendix. The most
obvious clues come from real, spontaneous interac-
tions where a speaker explicitly signals a problem
with a word used in a conversation (Section 3.1).
Non-dialogical types of data exposing word usage or comprehension differences between speakers are presented in Sections 3.2 and 3.3.

## 3.1 Dialog

When miscommunication happens in dialog, if it is detected, it can be addressed by means of repair strategies, such as rephrasing or asking for clarification (Purver et al., 2003; Pickering and Garrod, 2004). While some datasets on clarification questions (Xu et al., 2019; Aliannejadi et al., 2019; Kumar and Black, 2020) and repair strategies exist (Rasenberg et al., 2022), they are rarely dedicated to problems with word meaning.

The most relevant work is on **word meaning negotiation** (WMN) in dialog, both in language learning settings (Varonis and Gass, 1985) as well as in online discussions (Myrendal, 2019). WMN can be understood as a case of conversational repair targeting the meaning of a specific word occurrence. WMN instances typically have three parts: a trigger (the PWU), an indicator (the turn signaling a clarification request or an objection), and the meaning negotiation, which is a metalinguistic discussion where speakers explicitly discuss the meaning of the trigger word (Varonis and Gass, 1985; Myrendal, 2015). Myrendal (2015) distinguishes two types of WMN: those which arise from an incomplete understanding and those caused by a disagreement about how someone has used a word. Currently, only one dataset with WMN annotations exists: the NeWMe corpus (Garí Soler et al., 2025a). NeWMe contains over 600 WMNs and related phenomena, coming from both oral and online conversations and involving a PWU. This kind of data can shed light on more aspects and characteristics of (P)PWUs. For example, in an analysis of triggers in NeWMe, Noble et al. (2025) found that disagreement problems tend to involve abstract terms, while understanding issues are more often linked to concrete terms. However, the data remains scarce; given the variety of PPWUs, more annotations covering a wider range of conversational situations are needed for their study.

## 3.2 Monolog

In monolog-like text, due to the absence of feedback, it is typically harder to anticipate what parts of the discourse may be unclear. In fact, speakers tend to overestimate their interlocutor's understanding (Keysar and Henly, 2002). Collaborative writing environments like Wikipedia, where texts

are revised and edited by multiple authors, provide however a useful testbed to investigate problematic language use, by highlighting what can be improved in the original text. The wikiHow-ToImprove dataset (Anthonio et al., 2020) contains 2.7 million sentences with their revisions. The advantage of wikiHow compared to other sources of **revision histories** like Wikipedia (Faruqui et al., 2018) or news (Spangher et al., 2022) is that, in the former, modifications are more likely to be linguistically motivated instead of updating factual knowledge or providing additional information.

In this kind of interactive setting, PPWUs can be studied through word replacements. For example, Anthonio and Roth (2020) investigate noun substitutions in wikiHowToImprove. The fact that a word is replaced, however, is not evidence of it being a PWU: modifications are not always of a semantic nature (e.g., misspelling corrections), and only about 70% of revised versions in the dataset were judged to be improvements. Indeed, revisions may introduce vandalism, serve to just improve general textual coherence, or simply reflect the editor's personal lexical choice preferences (e.g., replacing *start* with *begin*). A dataset of word replacements annotated to indicate whether they constitute a semantic improvement is currently missing. Other kinds of modifications that can provide interesting data for the study of PPWUs are specific kinds of word insertions that clarify a word usage, such as completions of underspecified noun phrases (*tank → goldfish tank*) (Roth et al., 2022).

Finally, another useful kind of data could be obtained by directly asking annotators to mark, in a text, the word usages that they do not understand.

## 3.3 Signals from annotator disagreement

We can also find evidence of PWUs in cases of low inter-annotator agreement in semantic annotations at the word level. For example, in datasets where multiple people annotated senses (Jurgens and Klapaftis, 2013) or word usage similarity (Erk et al., 2009, 2013; Schlechtweg et al., 2021, 2025). Disagreement can be due to multiple reasons, such as inadequacy of the labels, but it can also point to the difficulty and subjectivity of the task (Plank, 2022), unveiling ambiguous, unclear or underspecified word usages. A tendency for a word to have lower levels of agreement can indicate a vaguer meaning (McCarthy et al., 2016) or other word characteristics that hinder its comprehension.

Low *intra*-annotator (Abercrombie et al., 2023)
agreement is also worth exploring. Schober (2005) found evidence of “conceptual misalignment” (i.e., speakers having different mental representations of words) in surveys: Respondents taking the same survey twice were more likely to change their answers when provided with clarifications such as word definitions in the second iteration. This shows that there had been a misunderstanding the first time around. However, this kind of data is expensive to obtain and it requires formulating hypotheses about the words that are likely to be misunderstood.

# 4 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.

## 4.1 Language Model-derived measures

Language models (LMs) are trained with large amounts of text and can provide a measure of the likelihood of sentences or of words in context. Different LM-derived measures can be used to estimate the predictability of a word.

One commonly used measure is word **surprisal**, calculated as the negative log probability of a word occurrence. Surprisal theory (Hale, 2001) states that the cognitive effort required to process a word is proportional to its surprisal. This relationship has been largely studied, and surprisal has been found to be a generally good predictor of words' reading times (RT) (Smith and Levy, 2013; Goodkind and Bicknell, 2018); although it seems to better reflect RTs of lower verbal intelligence profiles (Haller et al., 2024). The choice of LM, however, is an important one. Surprisal values from large LMs with lower perplexities provide worse RT estimations than smaller models (Oh and Schuler, 2023).<fnref n="9" />

Another LM-derived measure is **entropy**. While surprisal is a measure of how unexpected a given word is in its context, entropy is calculated on the probability distribution over the vocabulary, elicited by the preceding context of a specific word position. It can be interpreted as the difficulty of predicting the continuation of a context. Pimentel et al. (2023) use entropy as an operationalization of

anticipation and find that it can be a better predictor of RTs than surprisal.

One limitation of these measures for PPWU detection is that they are not only sensitive to (some) semantic anomalies but also to misspellings and syntactic deviations. One promising first step toward solving this problem, so far at the text level, is the contrastive perplexity score (Todd et al., 2020). The difference in perplexity between two LMs (one of which has been fine-tuned on a specific domain) gives information about the nature of the anomaly found (i.e., semantic vs non-semantic).

## 4.2 Word vector representations

Vector representations of words have been used for a long time in NLP. Static representations from Vector Space Models or word2vec (Mikolov et al., 2013), where a word is assigned a unique vector, have been shown to reflect multiple aspects of words' semantics. For instance, they can be used for identifying synchronic and diachronic lexical variation (Hamilton et al., 2016; Schlechtweg et al., 2019; Gonen et al., 2020), idioms (Peng and Feldman, 2017), hypernyms (Santus et al., 2014) and, in their multilingual form, false friends (Palmero Aprosio et al., 2020) (Section 2.1).

Contextualized word embeddings from Transformer-based language models like BERT (Devlin et al., 2019) additionally allow to represent word semantics at the token level and obtain good results on context-sensitive tasks (Section 2.2) such as novel metaphor detection (Pedinotti et al., 2021) and WSD (Wiedemann et al., 2019). The similarity between a target word and its context has been used to predict eye-tracking features (Salicchi et al., 2023). At the same time, these representations have been shown to encode rich out-of-context lexico-semantic information, such as a word's polysemy level, intensity, complexity and figurativeness (Garí Soler and Apidianaki, 2020; Xypolopoulos et al., 2021; Lyu et al., 2023).

## 4.3 Cognitive and neurolinguistic measures

Although costly to obtain, psycholinguistic and neurolinguistic data at the word level, as measured through **eye-tracking** and electroencephalography (EEG), can also help identify PPWUs. See Table 2 for datasets annotated with related metrics.

Longer eye fixations and reading times are typically interpreted as an indication of higher cognitive load (Just and Carpenter, 1980; Kintsch et al., 1975). Some eye tracking-derived measures, such

<footnote n="9">⁹The authors hypothesize that this is due to the vastly larger amount of training data compared to what humans are exposed to.</footnote>
as gaze duration and first fixation duration, are affected by word frequency and predictability (Inhoff, 1984). Ambiguity also plays a role: Eye fixation times are longer on ambiguous words which have equally likely meanings (Rayner and Duffy, 1986) or for which previous context instantiated a less frequent meaning (Sereno et al., 1992).

The N400 is an event-related potential signal in the brain consisting of a negative peak occurring about 400 milliseconds after a stimulus. It has been observed as a reaction to all sorts of semantically surprising input (Kutas and Hillyard, 1980; Kutas and Federmeier, 2011). Marked N400 waves have also been found in the processing of metaphorical language (Coulson, 2008). N400 has been shown to reflect word predictability modeled as LM surprisal (Michaelov et al., 2024), and evidence from joke processing shows that it is also sensitive to semantic plausibility operationalized as the similarity of a word with its preceding context (Xu et al., 2024). However, thematic role violations (e.g., between a verb and its argument: “Every morning at breakfast the eggs would eat...”) seem to elicit instead a P600 effect, typically linked with syntactic violations and ambiguities, but no N400 effect (Kuperberg, 2007).

## 4.4 Linguistic features

The earliest approaches to solving the NLP tasks presented in Section 2 often relied on different kinds and combinations of carefully selected linguistic features. For example, frequency measures (raw, comparative, or diachronic) can be useful for neologism detection (Garcia-Fernandez et al., 2011) and terminology extraction (Rigouts Terryn et al., 2020). PoS and syntactic features have been used to predict disagreement in sense annotations (Martínez Alonso et al., 2015). In lexical complexity prediction, multiple statistical, formal and psycholinguistic features (e.g., word length and creteness) are also often used (Desai et al., 2021). Ngram information can be employed for neologism (Falk et al., 2014) and code-switching detection (Kevers, 2022), and word co-occurrence information for semantic content quantification (Herbelot and Ganesalingam, 2013). Finally, information on words’ selectional preferences has been helpful for metaphor detection (Haagsma and Bjerva, 2016).

## 4.5 Other approaches

While it is not possible to present all existing methods here, there are other, less widespread approaches that are worth mentioning. For example, the uncertainty of automatic sense annotations from WSD models (Liu and Liu, 2023) can be used to find ambiguous word usages as well as to identify words with a tendency to present disambiguation difficulties. Anomaly or outlier detection methods, although they are most often applied to texts and not at the word level (Ruff et al., 2019; Arora et al., 2021), have been used to detect novel as well as figurative and metaphoric word usages (Sasaki and Shinnou, 2012; Bejan et al., 2023) and idioms (Feldman and Peng, 2013). Topic modeling has been used for novel word sense detection (Lau et al., 2014) and idiom detection (Peng et al., 2014); and sentiment analysis can be useful for controversial topic detection (Choi et al., 2010). There has also been work evaluating the ability of generative Large LMs to detect ambiguities, not restricted to lexical ones (Liu et al., 2023); but LLMs' effectiveness to detect different kinds of PPWUs remains unexplored.

# 5 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.<fnref n="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 (Garí Soler et al., 2025b) will contribute to a better understanding of the characteristics of PWUs and their rate of

<footnote n="10">¹⁰Lexical complexity prediction is closely related, but it has a narrower scope. It typically focuses on concept-related difficulties that can cause non-understanding. Our definition of PPWUs is broader and more context-dependent, including usages that cause confusion and disagreement, and considers phenomena such as humor, ambiguity, and lexical errors.</footnote>
occurrence, and will provide more data that could
eventually serve to train models for (P)PWU detec-
tion. 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 un-
expected 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 (Degraeuwe and Goethals, 2024). 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 chal-
lenge. Not all PPWUs result in communication
breakdowns: Many misunderstandings and dis-
agreements are not signaled, either because they go
undetected or because they do not pose a real prob-
lem 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 struc-
ture (Lambrecht, 1994)).

Although the perspective proposed here is in-
tended 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, po-
tentially causing differences in the frequency with
which they are observed across languages.

6 Conclusion

We have introduced the notion of Potentially Prob-
lematic Word Usages (PPWUs) and reviewed work
from various disciplines to provide a comprehen-
sive perspective of related linguistic factors, meth-
ods 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 meth-
ods 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 phe-
nomenon.

Limitations

While we have made an effort to provide a com-
prehensive and cross-disciplinary overview of phe-
nomena, datasets and approaches relevant to the
study of problematic word usages, some relevant
works may have been missed. Even regardless of
the methodology for bibliography search, however,
this survey cannot be fully exhaustive. For instance,
we have identified and presented discrete factors
associated with PPWUs, but word usages can be
problematic due to other idiosyncratic reasons that
may be hard to classify and detect (see last row
of Table 1 for an example). Moreover, it remains
unclear how often each of the presented factors actually leads to a PWU.

In this paper, partly due to space constraints, we have chosen to give only a high-level overview of the different research directions that need to be explored for studying PPWUs. Our primary goal was to establish a conceptual and empirical foundation for the study of PPWUs, which required an extensive synthesis of prior work. While we point to several avenues for future research, taking the first concrete steps, such as carrying out corpus-based studies that could propose a complete typology of PPWUs, is left for future work.

Finally, we acknowledge that, if taken naively, the definition of PPWUs is very broad and many words in a text could be considered to be “potentially problematic.” However, this is precisely why we argue that PPWU identification must be audience- and context-sensitive. Bringing these factors together under a single perspective is crucial, because in real-life interaction, problems may arise from different sources, and effectively determining the underlying cause requires considering them jointly.

## Acknowledgments

We thank all anonymous reviewers for their feedback. This research was partially funded by the Agence Nationale de la Recherche SINNet project (ANR-23-CE23-0033-01). Additional support was provided by the ANR under the France 2030 program PRAIRIE (ANR-23-IACL-0008).

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## A Appendix

## B PPWU contributing factors: Diagram
<table><caption>Table 1: Examples of PPWUs.</caption><thead><tr><th>Cause</th><th>Example</th><th>Source</th></tr></thead><tbody><tr><td>Low frequency</td><td>Yes, I am a <b>byword</b> to them.</td><td>Shardlow et al. (2020)</td></tr><tr><td>Neologism</td><td>– I, yeah, I-m, yeah, I do aerobics, uh, <b>step classes</b> and, uh<br/>– Step classes?<br/>– toning classes, yes.<br/>(At the time of speaking, 1992, stepping classes were a new concept)</td><td>Godfrey et al. (1992);<br/>Garí Soler et al. (2025a)</td></tr><tr><td>Lexical variation</td><td><i>chips</i> denotes different things in UK and American English, and <i>lorry</i> is mainly exclusively used in the UK.</td><td></td></tr><tr><td>Complex meaning</td><td>It is unlikely that morphological changes during development led to impairment of spatial learning and motor coordination, and morphological alterations in the cytoarchitecture of the <b>hippocampus</b> and cerebellum were not observed (...)</td><td>Shardlow et al. (2020)</td></tr><tr><td>Vagueness</td><td>– Do you genuinely believe that the less palatable parts of TRP [The Red Pill] are less <b>effective</b> than the positive parts?<br/>– You'll have to define "effective" for me and give me a specific example or two (...). Many forms of abuse, manipulation, and deception might be described as "effective", depending on what your goal is and whether or not you're a psychopath.</td><td>Tan et al. (2016);<br/>Garí Soler et al. (2025a)</td></tr><tr><td>Generality</td><td>– What do you mean by care? I was brought medicine and received help with mixing the nutritional drink which was replacing food during the time my intestines were resting. When I started feeling better and could move around I had to fix that myself.</td><td>Myrendal (2015)</td></tr><tr><td>Connotatively loaded term</td><td>– (...) <b>agnosticism</b> postulates that the existence or nonexistence of god is beyond our knowledge or ability to gather it. (...)<br/>– Not necessarily, or in most cases. Agnosticism argues that it is not possible at a given moment in time to know absolutely, but then we don't know anything absolutely. Moreover, that that's ok, we'll work with what we have. (...)<br/>– (...) That's kind of a flimsy sort of agnosticism (...)</td><td>Tan et al. (2016);<br/>Garí Soler et al. (2025a)</td></tr><tr><td>False friend</td><td>We usually go to a <b>magazine</b> to buy milk.<br/>(Russian <b>магазин</b> (magazin) or French <b>magasin</b> mean shop, store)</td><td>Yaylaci and Argynbayev (2014)</td></tr><tr><td>Partial cognate</td><td>English <b>blank</b> and its equivalent in several Romance languages (ES: <b>blanco</b>, FR: <b>blanc</b>, PT: <b>branco</b>, IT: <b>bianco</b>, CA: <b>blanc</b>). They can both mean <b>empty</b> but the Romance versions also mean <b>white</b>.</td><td>Domínguez and Nerlich (2002)</td></tr><tr><td>Cross-linguistic inequivalence</td><td>Russian <b>форточка</b> (fortochka), a specific kind of small window for ventilation common in post-Soviet states.</td><td>Pavlenko (2009)</td></tr><tr><td>Word from another language</td><td>– (...) the truth is that I don't know what the <b>gringo</b> fandom is like (...)<br/>– (...) Not sure what you mean by <b>gringo</b> (...)<br/>– Oh JAJFWJF SORRY- "gringo" is a way that people who speak Spanish refer to people who speak English (...)</td><td>Noble et al. (2021)</td></tr><tr><td>Ambiguity or contextual underspecification</td><td>Rooms are classically decorated and <b>warm</b></td><td>Jurgens (2014)</td></tr><tr><td>Ambiguity or contextual underspecification</td><td>– (...) I enjoy <b>working</b> on my car (...)<br/>– (...)<br/>– Oh I thought you meant "working on my car" as in polishing it and keeping it in super condition as a hobby, not as in "occasional repairs".</td><td>Tan et al. (2016);<br/>Garí Soler et al. (2025a)</td></tr><tr><td>Novel sense</td><td>The use of <b>bet</b> to mean yes</td><td></td></tr><tr><td>Metaphor</td><td>Westerns have a <b>gladiatorial</b>, timeless quality.</td><td>Do Dinh et al. (2018)</td></tr><tr><td>Lexical error</td><td>Choose which <b>charter</b> you want to be. (correction: character)<br/>– Anyone who has a link to a <b>dirty</b> Win7 download?<br/>– What do you mean by <b>dirty</b>?<br/>– What I meant by 'dirty' was an illegal copy of the operating system.</td><td>Anthonio et al. (2020)</td></tr><tr><td>Vandalism</td><td>First, make sure your hamster is familiar with your scent and your <b>poop</b>. (original word: "voice")</td><td>Anthonio and Roth (2020)</td></tr><tr><td>Semantic plausibility / contradiction</td><td>i understand. i am not sure if i can afford a babysitter, i am a <b>millionaire</b></td><td>Ko et al. (2019)</td></tr><tr><td>Unclassified</td><td>– (...) True <b>waffles</b> are crisp on the outside and fluffy on the inside (...)<br/>– So the definition of waffle changes based on how long you cook it? I happen to enjoy my waffles slightly undercooked, does that mean that they are not waffles?</td><td>Tan et al. (2016);<br/>Garí Soler et al. (2025a)</td></tr></tbody></table>
<table><caption>Table 2: Selection of datasets annotated with linguistic information relevant for PPWUs.</caption><thead><tr><th>Task/Annotation</th><th>Reference</th><th>Language</th><th>Resource name (if existing)</th></tr></thead><tbody><tr><td rowspan="6">Lexical Semantic Change</td><td>Schlechtweg et al. (2018)</td><td>DE</td><td>DURel</td></tr><tr><td>Schlechtweg et al. (2020)</td><td>EN, DE, SV, LA</td><td>SemEval 2020 Task 1</td></tr><tr><td>Basile et al. (2020)</td><td>IT</td><td>DIACR-Ita</td></tr><tr><td>Rodina and Kutuzov (2020)</td><td>RU</td><td>RuSemShift</td></tr><tr><td>Kutuzov et al. (2022)</td><td>NO</td><td>NorDiaChange</td></tr><tr><td>Chen et al. (2023)</td><td>ZH</td><td>ChiWUG</td></tr><tr><td rowspan="7">Complex Word Identification</td><td>Horn et al. (2014)</td><td>EN</td><td></td></tr><tr><td>Paetzold and Specia (2016)</td><td>EN</td><td>CWI-2016</td></tr><tr><td>Yimam et al. (2018)</td><td>EN, DE, ES</td><td>CWI-2018</td></tr><tr><td>Maddela and Xu (2018)</td><td>EN</td><td>Word Complexity Lexicon</td></tr><tr><td>Lee and Yeung (2018)</td><td>EN</td><td></td></tr><tr><td>Ortiz-Zambranoa and Montejo-Ráezb (2020)</td><td>ES</td><td>ALexS</td></tr><tr><td>Nishihara and Kajiwara (2020)</td><td>JA</td><td></td></tr><tr><td rowspan="5">Idiomatic Expressions</td><td>Shardlow et al. (2021)</td><td>EN</td><td>CompLex</td></tr><tr><td>Haagsma et al. (2020)</td><td>EN</td><td>MAGPIE</td></tr><tr><td>Korkontzelos et al. (2013)</td><td>EN</td><td>SemEval-2013 Task 5</td></tr><tr><td>Cook et al. (2008)</td><td>EN</td><td>VNC-Tokens</td></tr><tr><td>Sporleder et al. (2010)</td><td>EN</td><td>IDIX</td></tr><tr><td>Term Extraction</td><td>Rigouts Terryn et al. (2020)</td><td>EN, FR, NL</td><td>ACTER</td></tr><tr><td>Scalar Adjective Identification</td><td>Garí Soler and Apidianaki (2021b)</td><td>EN</td><td>SCAL-REL</td></tr><tr><td>Vague terms</td><td>Atemezing et al. (2022)</td><td>EN, FR</td><td></td></tr><tr><td>False friends and cognates</td><td>Palmero Aprosio et al. (2020)</td><td>IT-{EN,FR,DE,ES}</td><td></td></tr><tr><td>Bilingual Lexicon Induction</td><td>Pavlick et al. (2014)</td><td>100 languages</td><td></td></tr><tr><td rowspan="5">Code-switching</td><td>Solorio et al. (2014)</td><td>MSA-DA, EN-ES, EN-ZH, EN-NE</td><td></td></tr><tr><td>Molina et al. (2016)</td><td>MSA-DA, EN-ES</td><td></td></tr><tr><td>Kevers (2022)</td><td>CO-FR</td><td>BDLC</td></tr><tr><td>Nayak and Joshi (2022)</td><td>HI-EN</td><td>L3Cube-HingCorpus</td></tr><tr><td>Cook et al. (2014)</td><td>EN</td><td>BNC-ukWaC & SiBol/Port</td></tr><tr><td>Novel Word Sense Detection</td><td>Cook et al. (2014)</td><td>EN</td><td>BNC-ukWaC & SiBol/Port</td></tr><tr><td>Metaphor Detection</td><td>Steen et al. (2010)</td><td>EN</td><td>VUAMC</td></tr><tr><td>Novel Metaphor Detection</td><td>Do Dinh et al. (2018)</td><td>EN</td><td></td></tr><tr><td>Self-paced reading times</td><td>Smith and Levy (2013)</td><td>EN</td><td>Brown Corpus</td></tr><tr><td>Self-paced reading times</td><td>Futrell et al. (2021)</td><td>EN</td><td>Natural Stories Corpus</td></tr><tr><td>Word-level eye-tracking</td><td>Kennedy et al. (2003)</td><td>EN</td><td>Dundee Corpus</td></tr><tr><td>Word-level eye-tracking</td><td>Luke and Christianson (2018)</td><td>EN</td><td>Provo Corpus</td></tr><tr><td>N400</td><td>Toffolo et al. (2022)</td><td>EN</td><td></td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>Figure 1: Factors associated with PPWUs and related NLP tasks, with examples.</figcaption></figure>