Corpus evidence for the role of world knowledge in ambiguity reduction:
Using high positive expectations to inform quantifier scope

Noa Attali, Lisa S. Pearl, & Gregory Scontras*

Abstract. Every-negation utterances (e.g., Every vote doesn’t count) are ambiguous
between a surface scope interpretation (e.g., No vote counts) and an inverse scope in-
terpretation (e.g., Not all votes count). Investigations into the interpretation of these
utterances have found variation: child and adult interpretations diverge (e.g., Musolino
1999) and adult interpretations of specific constructions show considerable disagree-
ment (Carden 1973, Heringer 1970, Attali et al. 2021). Can we concretely identify
factors to explain some of this variation and predict tendencies in individual interpre-
tations? Here we show that a type of expectation about the world (which we call a high
positive expectation), which can surface in the linguistic contexts of every-negation ut-
terances, predicts experimental preferences for the inverse scope interpretation of dif-
ferent every-negation utterances. These findings suggest that (1) world knowledge, as
set up in a linguistic context, helps to effectively reduce the ambiguity of potentially-
ambiguous utterances for listeners, and (2) given that high positive expectations are
a kind of affirmative context, negation use is felicitous in affirmative contexts (e.g.,
Wason 1961).
Keywords. scope ambiguity; universal quantifiers; negation; pragmatics; computa-
tional models; corpus linguistics; psycholinguistics

1. Introduction. It’s unclear how people prefer to interpret ambiguous every-negation utterances,
such as (1):

(1) Every vote doesn’t count.
a. No vote counts. Surface scope: ∀x[vote(x) →¬count(x)] (every > n’t)
b. Not all the votes count. Inverse scope: ¬∀x[vote(x) → count(x)] (n’t > every)

The utterance in (1) allows both a surface interpretation (1a) and an inverse one (1b), depending
on the logical scope of the quantifier relative to negation. Which interpretation would a listener or
reader believe is more likely to be intended by the speaker?

Previous studies show variation in interpretation behavior. On the one hand, children and
non-native speakers seem to disprefer the inverse scope interpretation of every-negation (Musolino
1999, Gualmini et al. 2008, Viau et al. 2010, Chung & Shin 2022); converging research on scope
ambiguity suggests that surface scope is easier to access, involving less representational complex-
ity or processing cost (Tunstall 1998, Pritchett & Whitman 1995, Anderson 2004, Lee et al. 2011).
On the other hand, experimental studies that directly measure interpretation preference by adult na-
tive English speakers find a preference for inverse scope interpretations of every- and all-negation
(Carden 1970, Heringer 1970, Carden 1973).

*We would very much like to thank the Quantitative Language Collective at UC Irvine for helpful comments and
feedback on this work. Authors: Noa Attali, UC Irvine (nattali@uci.edu), Lisa S. Pearl, UC Irvine (lpearl@uci.edu)
& Gregory Scontras, UC Irvine (g.scontras@uci.edu).

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Additionally, the above studies report preferences that are aggregated across different sen-
tences and contexts. When we look beyond average interpretations to behavior on individual sen-
tences, we find further variation. Changes in the immediate linguistic context – in fact, in the
sentence containing the quantifier-negation construction itself – can flip interpretation patterns.
Carden (1973) found that for (2), 82.5% of respondents said that only the inverse scope interpreta-
tion was possible, 7.5% said that both were possible but that they favored inverse scope, and none
reported accessing only surface scope. On the other hand, for (3), 100% said that only the surface
scope interpretation was possible.

(2) All the boys didn’t arrive, did they?

(3) All the boys didn’t leave until midnight.

This variation highlights how findings on interpretation patterns depend on the stimuli them-
selves. What then are the characteristics of the contexts that impact these interpretation preferences
for every-negation utterances? That is, when are particular scope interpretations preferred?

In their computational cognitive model of scope ambiguity, Scontras & Pearl (2021) demon-
strate that a kind of world knowledge we term a “high positive expectation” (HPE) can explain
some variation in interpretation preferences with every-negation utterances. To illustrate, an HPE
for Every vote doesn’t count is the prior belief that it’s highly likely that every vote does count.
That is, the HPE for this every-negation utterance is that the worlds consistent with the equivalent
positive utterance (Every vote does count) have a high probability. In general, an HPE is the prior
belief that the most likely true world states are those consistent with the every world state in the
universe of the utterance.

An HPE could contribute to the felicity of using every-negation with an inverse scope not all
interpretation (e.g., Not all the votes count), thereby reducing the ambiguity of the utterance for
listeners. This ambiguity reduction occurs because the not all interpretation is highly informative
about the expectation that all is true (by indicating that this expectation isn’t correct). We might
put it this way: in a context with this kind of expectation (e.g., All votes count), the inverse scope
interpretation of every-negation (e.g., Not all votes count) is felicitous as an emphatic message that
the salient expectation (e.g., that all votes do in fact count) is false. As Scontras & Pearl (2021)
suggest, this world knowledge factor is one way to quantitatively specify a pragmatic factor that
explains prior behavioral results (see Scontras & Pearl 2021 for more details).

Scontras & Pearl’s model implements a cooperative, efficient speaker – cooperative in wanting
to say something true, and efficient in wanting to be informative. For example, returning to the
counting votes case, the model predicts that when the context holds the expectation that all votes
count, speakers would more likely agree that Every vote doesn’t count could be used to mean Not
all votes count. Given this HPE context, the inverse scope interpretation of Every vote doesn’t
count felicitously – i.e., cooperatively and efficiently – conveys that some votes do not, in fact,
count. In general, the model predicts that speakers tend to endorse every-negation (e.g., Every vote
doesn’t count) as a true description of a scenario consistent with the inverse scope interpretation
(e.g., Not all votes count) when every-negation conveys that an HPE (e.g., All votes count) is false
(e.g., It’s false that all votes count).

The model’s predictions for the role of an HPE extend well to prior findings. Attali et al. (2021)

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find that Scontras & Pearl’s model successfully predicts listener interpretations of every-negation
when given an HPE. Specifically, the modeled listener shows a close qualitative and quantitative
match to the average preference for the inverse scope interpretation of an out-of-context sentence
Every marble isn’t red. The inverse scope not all interpretation is preferred over surface scope
none when given an HPE because (1) there are more ways for the not all interpretation to be true
compared with the none interpretation, and so a cooperative speaker intends that interpretation,
and (2) it’s highly informative to update a strongly biased, salient belief such as an HPE (e.g., that
every vote does count), and so an efficient speaker intends that interpretation; for more details see
Attali et al. 2021).

Because HPEs can explain interpretations for every-negation utterances in prior experimental
and computational work, we ask how well HPEs account for interpretations of every-negation
utterances in naturalistic contexts. For instance, do HPEs surface in the contexts of spontaneously
produced every-negation? Specifically, when a local linguistic context seems to express an HPE,
is an inverse scope interpretation more likely than a surface scope interpretation?

We first describe how we developed a corpus of naturally-occurring every-negation uses in
context via a behavioral study, including the preferred interpretation of each use. We then describe
how we identified HPEs in the corpus contexts. We present our analysis for the connection between
the presence of an HPE and an item’s preferred interpretation, finding that inverse scope interpre-
tations are indeed more preferred following an HPE. Our results suggest that the world knowledge
implemented as an HPE in the local linguistic context can help effectively reduce the ambiguity of
every-negation utterances for listeners, and that negation use is felicitous in affirmative contexts,
like the kind that HPEs encode.

2. Corpus data and behavioral experiment. To assess the role of HPEs in the interpretation of
naturally-occurring every-negation utterances, we need (1) a corpus of naturally-occurring every-
negation utterances in context, (2) a measure of these utterances’ preferred interpretations, and (3)
an estimate of the extent to which a context contains an HPE. In this section, we describe how we
created a corpus (goal 1) and measured interpretation preferences in context (goal 2). Section 3
discusses how we identified HPEs in context (goal 3) to see whether their presence predicts inverse
scope interpretations.

2.1. CORPUS SEARCH FOR every-NEGATION UTTERANCES. To achieve goal 1, we identified
uses of every-negation in a corpus of spontaneous speech. We extracted the every-negation occur-
rences in the speech section of the Corpus of Contemporary American English (COCA; Davies
2015), which is made up of transcripts of spoken conversations from American radio and TV pro-
grams from 1990 to 2012 (≈9 million clauses, or ≈95 million words). We defined every-negation
occurrences as those where quantified subjects precede and c-command sentential negation (with
not or contracted n’t). To develop the automated search, we randomly selected a year of COCA
transcripts and manually searched it for uses of every-negation. We then wrote a search that yielded
a 100% recall rate, that is, returning each of the occurrences in this development set. We applied
this search to the rest of the COCA speech section. We found that every-negation uses are highly
infrequent in English conversation; in total, we identified 390 cases.

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2.2. CORPUS ANNOTATION. To achieve goal 2, following Degen (2015), we crowd-sourced
interpretation preferences of these uses in their immediate contexts (three preceding sentences
and one following sentence). For each item, participants (N = 208) completed a paraphrase-
endorsement task (Scontras & Goodman 2017), choosing on a sliding scale between none and
not all paraphrases of the potentially-ambiguous clause.

2.2.1. PARTICIPANTS. We recruited 390 participants with U.S. IP addresses through Amazon.com’s
Mechanical Turk (MTurk) crowd-sourcing service. Of these 390, we assessed data from 208 par-
ticipants (35% female; mean age: 41 y/o) who passed control trials (discussed further in Section
2.2.4) and indicated that English was their only native language. Each received $2.00.

2.2.2. STIMULI. For each of the 390 every-negation uses, we created excerpts consisting of the
three preceding context sentences, the bolded potentially-ambiguous clause, and one following
context sentence (see Figure 1). We also created paraphrases of the surface and inverse scope
interpretations. As part of a pilot experiment (N=94), we checked that the paraphrase wording was
correctly understood, so that the surface scope interpretation paraphrase was always understood
to be consistent with a none situation (e.g., that None are red describes three blue marbles rather
than two red and one blue marble) and the inverse scope paraphrase was always understood to be
consistent with a some but not all situation (e.g., that Not all are red preferentially describes two
red and one blue marble rather than three blue marbles).

Because the ambiguous clauses took the form quantified noun phrase–verb–negation–remainder
(e.g., Everybody is not doing that: quantified noun phrase = Everybody; verb = is; negation = not;
remainder = doing that), surface scope paraphrases took the form none/no one/nobody/nothing–
verb–remainder (e.g., nobody is doing that) and inverse scope paraphrases took the form not all/not
all things are–remainder (e.g., not all are doing that).

Figure 1: Sample paraphrase-endorsement trial from the corpus analysis of every-negation.

2.2.3. DESIGN. The initial instructions asked to “choose the best paraphrase for the bolded part”
for fifteen randomly-selected conversation excerpts; at each trial, participants were again asked
“What did the speaker mean in the bolded part?” (see Figure 1). Beneath the excerpt, participants
rated the best paraphrase as a judgment on a sliding scale between the surface and inverse scope
interpretations. The two scope interpretations were randomly assigned for each item in left-right
or right-left order.

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2.2.4. CONTROLS. To check that participants were reading and understanding the contexts of the
items – and also as a way to suggest that context is useful – two control trials were constructed
to imitate the items from the corpus. These control trials contained clearly disambiguating infor-
mation about the intended scope interpretation in the surrounding context. The disambiguating
information always appeared as a restatement of the speaker’s meaning. These two controls ap-
peared in random order as the first two trials for each participant.

The surface scope-disambiguating control item is in (4), and the inverse scope-disambiguating
control item is in (5). For clarity, the disambiguating information is italicized, though it was not
italicized in the experiment. Participants were considered to pass the surface control by placing the
slider closer to the none paraphrase than to the not all paraphrase; they passed the inverse control
by placing the slider closer to the not all paraphrase than to the nobody paraphrase.

(4) TONHAUSER: The ten board members voted last night. I was really surprised—I thought at
least some of them would like Proposition 23. But all ten of them voted against it. Basically,
every board member didn’t like Proposition 23. Not even a single one of them liked it.

(5) SIDNER: Look, we completely fixed the issue. Indicators have improved across the board.
Everybody’s happy.
GROSZ: (VOICEOVER) No, everybody isn’t happy. Some are happy but others are deeply
dissatisfied with what they call a ‘band aid solution.’

We restricted analysis to those participants who passed both controls and indicated that English
was their only native language. The rate of passing both controls was 53%. This relatively high
failure rate may have been due to low English reading proficiency. Though we restricted MTurk
participation to US IP addresses and to those MTurk workers who have completed at least 1,000
tasks in the past and we also only analyzed data from self-reported native English speakers, it’s
possible that some participants didn’t fluently read English well enough. Another factor may have
been attention and motivation: participants in an online platform may be disengaged with the
experiment. A third factor is task difficulty: perhaps the paraphrase endorsement task could be
seen as a complex reading comprehension and logical inference task, because these sentences have
multiple logical operators.

2.3. RESULTS. Each item was judged by at least 2 and at most 14 different participants. Although
the surface scope paraphrases randomly appeared on the left or right of the sliders, we transformed
and report responses on sliders as though the surface scope paraphrases always appeared on the
left. This allows the final response measure to vary from 0 (maximum endorsement of the surface
scope interpretation) to 1 (maximum endorsement of the inverse scope interpretation).

In the COCA transcripts, we found both a general preference for inverse scope interpreta-
tions as well as a high degree of interpretation variation for the every-negation utterances. Figure
2, which shows judgment-by-judgment interpretations, suggests that many of these utterances in
context seem unambiguous: 29% of individual scores were below 0.25 (indicating a strongly sur-
face scope interpretation) while 53% of individual scores were above 0.75 (indicating a strongly
inverse scope interpretation). Figure 3 aggregates ratings by items. For some items, strong intu-
itions are reliable across different participants’ judgments: 12% of mean scores were below 0.25,
and 38% of mean scores were above 0.75. Examples (6) and (7) are items that elicited a strong

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surface scope preference ((6): mean response ≈ 0.13) or a strong inverse scope preference ((7):
mean response ≈ 0.98).

Figure 2: Individual scope interpretations from the every-negation corpus analysis.

Figure 3: Mean interpretations per item from the every-negation corpus analysis.

(6) @!WERTHEIMER: So what about New Jersey? Can New Jersey get over the hump?
@!RAPOPORT: Well, [transcript cuts out] first team from the Eastern Division to return to
the finals since the Bulls were winning all their championships. They’re a little nervous about
that in New Jersey, Linda, that every team that made it to the finals from the East in the
last couple of years hasn’t been able to repeat; but again, they’re strong competition. The
Pistons have been impressive this year.
a. No team (that made it to the finals from the East in the last couple of years) has been

able to repeat. (every > n’t)
b. Not all teams (that made it to the finals from the East in the last couple of years) have

been able to repeat. (n’t > every)

(7) @!CALLER Hi. My question for Mr. Eisner was, MGM is one of my favorite places in
Disneyworld and one of my favorite attractions there is the animation studios, and now the
studio, the animation studio there is closed, and everything has moved to California, and I

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wanted to know how you justified doing that.
@!EISNER Well, everything has not moved to California. We will still be demonstrating
animation in Florida.
a. Nothing has moved to California. Surface scope (every > not)
b. Not all things have moved to California. Inverse scope (not > every)

2.4. DISCUSSION. The results of the paraphrase endorsement study with the corpus-mined stim-
uli show variation and a weak inverse scope preference in adult native English speakers’ interpre-
tations for every-negation utterances. Although these results agree with the larger picture painted
by previous studies on every-negation, to our knowledge this is the first larger-scale investigation
of naturalistic stimuli in context.

In the following section, we ask whether HPEs account for this variation and inverse scope
interpretation preference. More specifically, when an inverse scope interpretation is preferred for
an every-negation utterance (e.g., Not all votes count for Every vote didn’t count), was that use of
every-negation in fact an emphatic message that a salient HPE (e.g., All votes count) is false?

3. Identifying high positive expectations in linguistic contexts. An HPE represents a prior be-
lief, and one way to measure for its presence is by its overt linguistic expression in an item’s
preceding context. For example, for Every vote doesn’t count, an HPE is the prior belief that every
vote does count – that is, that the worlds consistent with the non-negated utterance (Every vote does
count) are highly probable – and one measure of this HPE’s presence is the non-negated utterance
itself: Every vote does count.

As a preliminary measure, the first author hand-coded categorically for the presence/absence
of an overt HPE expression in each preceding context. We found that 59/390 (15%) of the items
contained such an expression.

For an automatic and more objective measure of the HPE expression – that is, a method that
could scale to large amounts of data and would capture the intended linguistic phenomenon while
minimizing experimenter bias – we calculated the degree of lexical overlap between the preced-
ing linguistic context and a string representing the positive expectation (pos exp). For each item
(e.g., Every vote doesn’t count), we first coded pos exp as the potentially-ambiguous clause with-
out negation (e.g., Every vote does count). We then coded for the extent to which the pos exp
appeared in the preceding context as the longest common substring similarity (LCS; Needleman
& Wunsch 1970) between each preceding context string c and pos exp pair, calculated using the R
stringdist package (Loo 2014).

Each LCS was equal to the longest sequence formed by pairing words from the preceding
context string c and pos exp, while keeping their order intact; the dissimilarity dlcs(c, pos exp)
was then the number of unpaired words left over in both strings. dlcs(c, pos exp) can be defined
recursively as in (8) for different relative lengths of the two strings to be matched against:

(i) It is trivially 0 for empty strings (line 1: c = pos exp = ϵ).
(ii) It is based on pairing each word from both strings if the two strings have equal length (line

2: |c| = |pos exp|). For example, suppose the preceding context is Every vote does count for an
utterance with the pos exp Every vote does count. Then, dlcs= 0. However, if the preceding context
was What is going on?, dlcs= -8 because all eight words in the two strings would be unpaired.

(iii) It is based on the minimum lcs-distance that can be obtained from pairing all the words

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from the shorter string to an equal number of words from the longer string (line 3: otherwise). For
example, suppose the preceding context was I believe every vote does count for an utterance with
the pos exp Every vote does count. Then, dlcs= -2 because all four words in pos exp would pair to
every vote does count in the context, and leave unpaired the two words I believe.

Thus, dissimilarity ranges from 0 (completely similar) to the total words W in both strings
combined (completely dissimilar), where W = (|c| + |pos exp|). We calculate LCS similarity as
negative dissimilarity: −dlcs(c, pos exp). Thus, LCS similarity ranges from 0 to -W , with values
closer to zero indicating more lexical overlap. In particular, values closer to zero indicate a greater
similarity between the context and the HPE linguistic string, and so represent a higher probability
that the context contained a linguistic string transparently encoding an HPE.

(8) dlcs(c, pos exp) =





0, if c = pos exp = ε

dlcs(c1:|c|−1, pos exp1:|pos exp|−1), if |c| = |pos exp|
1 +min{dlcs(c1:|c|−1, pos exp),

dlcs(c, pos exp1:|pos exp|−1)}, otherwise.

4. Results.

4.1. HAND-CODED HPE RESULTS. Using the preliminary categorical hand-coding where we
found that 59/390 of the utterances had HPEs, we first looked at p(inverse|HPE): how often an
inverse scope interpretation was preferred when an HPE occurred. We found that 50/59 (85%) of
utterances with HPEs were on average better paraphrased by the inverse scope paraphrase not all
than the surface scope paraphrase none.

We also looked at p(HPE|inverse) vs. p(HPE|surface): how often items where the inverse
interpretation was strongly preferred had an HPE compared with items where the surface interpre-
tation was strongly preferred. We found that 22% of highly inverse-preferred items (those with
responses greater than 0.75) had HPEs, as opposed to 6% of highly surface scope-preferred items
(those with responses less than 0.25).

These results suggest that the hand-coded HPEs do tend to co-occur with an inverse scope
interpretation in our sample. However, the automatic measure of an HPE’s presence that we de-
scribed above allows us to to measure the continuous relationship between the extent of HPE
expression and the strength of inverse scope preference, as shown below. In addition, this measure
can be used in future work to analyze larger samples.

4.2. AUTOMATIC HPE RESULTS. We used the continuous LCS-based measure −dlcs to assess if
an HPE predicts an inverse scope preference per item, and ran a linear mixed effects model pre-
dicting logit-transformed mean item responses by −dlcs (representing LCS similarity) with random
intercepts for participants (see Figure 4). To determine whether an HPE captures individual judg-
ment variation above and beyond mean item-level variation, we predicted logit-transformed item
responses by LCS similarity, with random intercepts for participants and items. Both models found
that LCS similarity was a significant predictor of an inverse scope preference preference (p < .001
in both). That being said, although LCS similarity is a significant predictor of inverse scope pref-
erence, the relationship is noisy, as Figure 4 shows, with a marginal R2 = 0.024.

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Figure 4: Preceding HPE and average inverse scope item preference.

Interestingly, only preceding, and not following, expressions of HPEs predict an inverse scope
preference, as Figure 5 shows: a version of both models that calculated LCS similarity using
overlap with the following – rather than preceding – context, found LCS similarity of the following
context not to be a significant predictor of either item-level or judgment-level interpretations.

Figure 5: Following HPE and average inverse scope item preference.

5. Discussion. Our corpus analysis suggests that a high positive expectation (HPE) expressed
directly in the preceding linguistic context can affect scope interpretation preferences for every-
negation utterances. In particular, HPEs expressed this way correlate with stronger preferences for
the inverse interpretation. These results align with previous modeling results (Scontras & Pearl
2021) and pragmatically-oriented proposals from truth-value judgment studies for supporting the

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felicity of every-negation in context (Gualmini et al. 2008). In particular, an HPE provides a
context that makes an inverse scope interpretation more felicitous because several things hold: (1)
the listener assumes that speakers are truthful and informative, (2) an HPE represents a strong
belief about the world, (3) finding out an HPE is false (that not all is true) is very informative, and
(4) the inverse scope not all interpretation is one way to find this out. We speculate that perhaps in
comparison with alternative constructions such as Not every (e.g., Not every vote counts for Every
vote doesn’t count), the every-negation construction highlights that a positive expectation is false,
and might even be preferred as more informative (in such a context) than its not every alternative.

We note that our LCS similarity measure for HPEs is a first-pass one (the first anyone has
tried to our knowledge), and likely underestimates HPE presence. In particular, this measure looks
for transparent linguistic encodings of an HPE; but of course world expectations do not have to
be encoded linguistically, or encoded nearby even if they are linguistically encoded. Even given
our restriction to overtly expressed world knowledge in the preceding three sentences, our specific
measurement of LCS similarity has a noisy potential to underestimate the presence of an HPE
for several reasons. First, it is affected by context length, such that LCS similarity is lower for
longer contexts even if those contexts contain a clear HPE expression. For instance, returning
to the vote-counting case, LCS similarity would be -2 for the context I believe every vote does
count but it would be 0 the context Every vote does count. Second, this LCS similarity measure
looks for an HPE based on the exact lexical items in the every-negation utterance. For instance,
it would identify the HPE in the context Every vote does count for the every-negation utterance
Every vote doesn’t count; yet, this measure misses the HPE in the context All votes should matter
because the individual lexical items differ (every vs. all, count vs. matter). This rigidity of LCS
similarity as a measure of context-sentence overlap could be a source of the noisiness evidenced
in Figure 4. Other potential sources of noise include additional factors that may help disambiguate
scope interpretations, such as prior expectations about questions under discussion and grammatical
scope accessibility (e.g., as found by Scontras & Pearl 2021).

Still, the advantage of LCS similarity is that it provides an automatic continuous measure
to improve our analysis of larger-scale data. Here, it allowed us to consider the potential linear
relationship between the extent of HPE expression and the extent of an inverse scope preference.
Future work could replace LCS similarity with a measure that considers a vectorized semantic
representation of meaning rather than lexical overlap between the context and a string representing
the HPE. A vectorized semantic measure would allow for the flexibility to recognize degrees of
semantic similarity rather than categorical lexical equivalence. For example, such an approach
would allow us to count All votes should matter as a context expressing an HPE for Every vote
doesn’t count (recognizing that all is similar to every, and count similar to matter, in this context).

More generally, our results suggest that listeners can rely on world knowledge and proper-
ties of the immediately surrounding contexts of an ambiguous utterance, like an every-negation
utterance, to interpret it. Since a context containing an HPE is a kind of affirmative context, these
findings support the broader theory that negation use is more felicitous in affirmative contexts (e.g.,
Wason 1961). One way that listeners understand every-negation in context is as a kind of emphatic
frame for the message that an HPE is false.

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References

Anderson, Catherine. 2004. The structure and real-time comprehension of quantifier scope ambi-
guity: Northwestern University Evanston, IL dissertation.

Attali, Noa, Gregory Scontras & Lisa S Pearl. 2021. Pragmatic factors can explain variation
in interpretation preferences for quantifier-negation utterances: A computational approach.
Proceedings of the Annual Meeting of the Cognitive Science Society 43(43).

Carden, Guy. 1970. A note on conflicting idiolects. Linguistic Inquiry 1(3). 281–290.
Carden, Guy. 1973. Disambiguation, favored readings, and variable rules. New Ways of Analyzing

Variation in English 171–82.
Chung, Eun Seon & Jeong-Ah Shin. 2022. Native and second language processing of quantifier

scope ambiguity. Second Language Research 1–26.
Davies, Mark. 2015. Corpus of Contemporary American English (COCA) https://doi.org/

10.7910/DVN/AMUDUW.
Degen, Judith. 2015. Investigating the distribution of some (but not all) implicatures using corpora

and web-based methods. Semantics and Pragmatics 8. 11–1.
Gualmini, Andrea, Sarah Hulsey, Valentine Hacquard & Danny Fox. 2008. The question–answer

requirement for scope assignment. Natural Language Semantics 16(3). 205.
Heringer, James T. 1970. Research on quantifier-negative idiolects. Chicago Linguistic Society 6.

95.
Lee, Miseon, Hye-Young Kwak, Sunyoung Lee & William O’Grady. 2011. Processing, pragmat-

ics, and scope in korean and english. Japanese/Korean Linguistics 19. 297–311.
Loo, Mark P.J. van der. 2014. The stringdist Package for Approximate String Matching. The

R Journal 6(1). 111–122. 10.32614/RJ-2014-011. https://doi.org/10.32614/
RJ-2014-011.

Musolino, Julien. 1999. Universal grammar and the acquisition of semantic knowledge: An exper-
imental investigation into the acquisition of quantifier-negation interaction in english: Uni-
versity of Maryland, College Park dissertation.

Needleman, Saul B & Christian D Wunsch. 1970. A general method applicable to the search for
similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48(3).
443–453.

Pritchett, Bradley & John Whitman. 1995. Syntactic representation and interpretive preference.
Japanese Sentence Processing 65–76.

Scontras, Gregory & Noah D Goodman. 2017. Resolving uncertainty in plural predication. Cog-
nition 168. 294–311.

Scontras, Gregory & Lisa S Pearl. 2021. When pragmatics matters more for truth-value judgments:
An investigation of quantifier scope ambiguity. Glossa: a journal of general linguistics 6(1).

Tunstall, Susanne Lynn. 1998. The interpretation of quantifiers: semantics & processing: Univer-
sity of Massachusetts at Amherst dissertation.

Viau, Joshua, Jeffrey Lidz & Julien Musolino. 2010. Priming of abstract logical representations in
4-year-olds. Language Acquisition 17(1-2). 26–50.

Wason, Peter C. 1961. Response to affirmative and negative binary statements. British Journal of
Psychology 52(2). 133–142.

Proceedings of ELM 2: 13-23, 2023

Noa Attali, Lisa S. Pearl and Gregory Scontras:
Corpus evidence for the role of world knowledge in ambiguity reduction. 23

https://doi.org/10.3765/elm
https://www.elm-conference.net/