Characterizing Idioms: Conventionality and Contingency
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Characterizing Idioms: Conventionality and Contingency
Michaela Socolof, Jackie Chi Kit Cheung, Michael Wagner, Timothy J. O’Donnell
McGill University
michaela.socolof@mail.mcgill.ca
jcheung@cs.mcgill.ca
{chael, timothy.odonnell}@mcgill.ca
Abstract of idiom in the literature. There is little consen-
sus about which phrases should be called idioms
Idioms are unlike other phrases in two im- beyond a small number of prototypical examples.
portant ways. First, the words in an id-
Some phrases that have proven challenging for
iom have unconventional meanings. Sec-
arXiv:2104.08664v1 [cs.CL] 17 Apr 2021
ond, the unconventional meaning of words definitions of idioms include light verb construc-
in an idiom are contingent on the presence tions (e.g., take a walk) and semantically transpar-
of the other words in the idiom. Linguistic ent collocations (e.g., on and off ). What seems
theories disagree about whether these two clear is that phrases typically called idioms involve
properties depend on one another, as well some level of non-conventional meaning and some
as whether special theoretical machinery is level of contingency between particular words.
needed to accommodate idioms. We define
two measures that correspond to these two This combination of non-conventionality and
properties, and we show that idioms fall at contingency between words distinguishes so-
the expected intersection of the two dimen- called idioms from other linguistic constructions
sions, but that the dimensions themselves and has led to a number of theories that treat id-
are not correlated. Our results suggest that ioms as exceptions to the mechanism that builds
idioms are no more anomalous than other phrases compositionally. These theories posit spe-
types of phrases, and that introducing spe-
cial machinery for handling idioms (e.g., Weinre-
cial machinery to handle idioms may not be
warranted. ich, 1969; Bobrow and Bell, 1973; Swinney and
Cutler, 1979). We propose an alternative ap-
proach, which views idioms not as exceptional,
1 Introduction but merely the result of the interaction of two
Idioms—expressions like spill the beans—bring independently motivated cognitive mechanisms.
together two phenomena which are of fundamen- The first mechanism stores and reuses linguistic
tal interest in understanding language. First, they structures that are useful, including form-meaning
exemplify non-conventional word meaning (Wein- mappings (e.g., Sciullo and Williams, 1987; Jack-
reich, 1969; Nunberg et al., 1994). The words endoff, 2002; O’Donnell, 2015). The second
spill and beans in this idiom seem to carry par- mechanism allows words to be interpreted in non-
ticular meanings—something like divulge and in- canonical ways depending on the context. This ap-
formation, respectively—which are different from proach predicts that neither mechanism should de-
the conventional meanings of these words in other pend on the presence of the other, and further, that
contexts. However, unlike other kinds of non- we should expect to find linguistic constructions
conventional word use such as novel metaphor, throughout the entire space where the two mecha-
there is a contingency relationship between words nisms interact.
in an idiom (Wood, 1986; Pulman, 1993). It is the This paper presents evidence that idioms oc-
specific combination of the words spill and beans cupy a particular region of the space of these two
that has come to carry the idiomatic meaning of mechanisms, but are not otherwise exceptional.
divulging information. One cannot dump out the We define two measures, conventionality—meant
legumes with the same non-conventional meaning. to measure the degree to which words are in-
Spill the beans is a prototypical example of an terpreted in a canonical way, and contingency—
idiom. However, a major difficulty in studying meant to measure the degree to which a combina-
idioms is that there is no agreed-upon definition tion of words is useful to store as a unit. We con-struct a novel corpus of idioms and non-idiom col- gree to which there is a statistical contingency—
locations, and show that phrases typically called the presence of one or more words strongly signals
idioms fall at the intersection of low convention- the presence of the others. This notion of contin-
ality and high contingency, but that the two mea- gency has also been argued to be a critical piece
sures are not correlated and there are no clear dis- of evidence used by language learners in decid-
continuities that separate idioms from other types ing which linguistic structures to store (e.g., Hay,
of phrases. 2003; O’Donnell, 2015).
Our experiments also reveal hitherto unnoticed To aid in visualizing the space of phrase types
asymmetries in the behavior of head versus non- we expect to find in language, we place our two di-
head words of idioms. In phrases we would in- mensions on different axes of a 2x2 matrix, where
tuitively call idioms, the dependent word (e.g., each cell contains phrases that are either high or
beans in spill the beans) shows greater deviation low on the conventionality scale, and high or low
from its conventional meaning than the head word. on the contingency scale. The matrix is given in
We find that coordinate structures (e.g. X and Y) Figure 1, with the types of phrases we expect in
pattern consistently with other types of phrases in each cell.
this respect, as long as the first conjunct is consid-
ered the head. This may shed light on the long- Low High
standing debate over the headedness of coordinate conv. conv.
structures. High Idioms Common
cont. (e.g. raise hell) collocations
2 Background (e.g. in and out)
Low Novel Regular
There are various positions in the linguistics liter- cont. metaphors language use
ature about the relationship between composition- (e.g. eat peas)
ality and storage. On one hand, there are theories
predicting that the only elements that get stored Figure 1: Matrix of phrase types, organized by whether
they have high/low conventionality and high/low con-
are those that are non-compositional (e.g., Bloom-
tingency
field, 1933; Pinker and Prince, 1988). On the
other hand, some theories predict that storage can
We expect our measures to place idioms primar-
happen regardless of whether something is com-
ily in the top left corner of the space.At the same
positional (e.g., O’Donnell, 2015; Tremblay and
time, however, we do not expect to find evidence
Baayen, 2010).
that idioms are anomalous; thus we predict a lack
To explore the relationship between composi-
of correlation between the measures and a lack of
tionality and storage, we present measures that are
major discontinuities in the space.
intended to capture these two phenomena, and we
We find that our predictions are borne out. We
probe for evidence of a dependence between stor-
take this as support for theories that factorize out
age and non-compositionality.
the dimensions associated with compositionality
Our first measure, which we call conventional-
and storage, and we contend that this kind of fac-
ity, captures the extent to which the subparts of
torization provides a natural way of characteriz-
a phrase contribute their normal meaning to the
ing not just idioms, but also collocations and novel
phrase. Most of language is highly conventional;
metaphors, alongside regular language use.
we can combine a small set of units in novel ways,
precisely because we can trust that those units will 3 Methods
have similar meanings across contexts. At the
same time, the linguistic system allows structures In this section, we describe the creation of our cor-
such as idioms and metaphors, which use words in pus of idioms and define our measures of conven-
non-conventional ways. Our measure is intended tionality and contingency.
to distinguish between phrases based on how con-
ventional the meanings of their words are. 3.1 Dataset
Our second measure, contingency, captures how We built a corpus of sentences containing idioms
unexpectedly often a group of words occurs to- as well as non-idioms, all of which were gathered
gether in a phrase and, thus, measures the de- from the British National Corpus (BNC; Burnard,2000). Our corpus is made up of sentences con- Andrew, 2006) expressions were used to find in-
taining target phrases and matched phrases, which stances of each target phrase.
we detail below. Matched, non-idiomatic sentences were also
Given that definitions of idioms differ in which extracted. To obtain these matches, we used
phrases in our dataset count as idioms (some Tregex to find sentences that included a phrase
would include semantically transparent colloca- with the same syntactic structure as the target
tions, others would not), we do not want to commit phrase. Each target phrase was used to obtain two
to any particular definition a priori. Most impor- sets of matched phrases: one set where the head
tantly, given that we are investigating whether id- word remained constant and one set where the
ioms can be characterized on dimensions that do non-head word remained constant. For example,
not make reference to the notion of an idiom, we for the head word matches of the adjective noun
have tried to refrain from presupposing an a pri- combination sour grape, we found sentences that
ori category of “idiom” in our analysis, while still had the noun grape modified with an adjective
acknowledging that people share some weak but other than sour. Below is an example of a matched
broad intuitions about idiomaticity. sentence found by this method:
The target phrases in our corpus consist of 207
Not a special grape for winemaking, nor
English phrasal expressions, some of which are
a hidden architectural treasure, but hot
prototypical idioms (e.g. spill the beans) and some
steam gushing out of the earth.
of which are collocations (e.g. bits and pieces) that
are more marginally considered idioms. These ex- The number of instances of the matched phrases
pressions are divided into four categories based ranged from 29 (the number of verb object phrases
on their syntax: verb object (VO), adjective noun with the object logs and a verb other than saw) to
(AN), noun noun (NN), and binomial (B) expres- the tens of thousands (e.g. for verb object phrases
sions. Binomial expressions are fixed pairs of beginning with have), with the majority falling in
words joined by and or or (e.g., wear and tear). the range of a few hundred to a few thousand. Is-
The breakdown of phrases into these four cate- sues of sparsity were more pronounced among the
gories is as follows: 31 verb object pairs, 36 ad- target phrases, which ranged from one instance
jective noun pairs, 33 noun noun pairs, and 58 (word salad) to 2287 (up and down). Because of
binomials. The phrases were selected primarily this sparsity, some of the analyses described below
from lists of idioms published in linguistics papers focus on a subset of the phrases.
(Riehemann, 2001; Stone, 2016; Bruening et al., The syntactic consistency between the target
2018; Bruening, 2019; Titone et al., 2019). The and matched phrases is an important feature of
full list of target phrases is given in Appendix A. our corpus, as it allows us to control for syntac-
The numerical distribution of phrases is given in tic structure in our experiments.
Table 1. 3.2 Conventionality measure
Phrase Number of Example Our measure of conventionality is built on the idea
type phrases that a word being used in a compositional way
Verb 31 jump the gun should keep roughly the same meaning across con-
Object texts, whereas a non-conventional word meaning
Noun 36 word salad should be idiosyncratic to particular contexts. In
Noun the case of idioms, we expect that the difference
Adj. 33 red tape between a word’s meaning in an idiom and the
Noun word’s conventional meaning should be greater
Binom. 58 fast and loose than the difference between the word’s meaning in
a non-idiom and the word’s conventional meaning.
Our measure makes use of the language model
Table 1: Types, counts, and examples of target phrases BERT (Devlin et al., 2018) to obtain contextu-
in our idiom corpus, with head words bolded alized embeddings for the words in our dataset.
For each of our phrases, we compute the conven-
The BNC was parsed using the Stanford Parser tionality measure separately for the head and non-
(Manning et al., 2014), then Tregex (Levy and head words. For each case (head and non-head),we first take the average embedding for the word To estimate the contingency of particular
across sentences not containing the phrase. That phrases, we make use of word probabilities given
is, for spill in spill the beans, we get the embed- by XLNet (Yang et al., 2019), an auto-regressive
dings for the word spill in sentences where it does language model that gives estimates for the con-
not occur with the direct object beans. Let O be ditional probabilities of words given their context.
a set of instances w1 , w2 , ..., wn of a particular To estimate the joint probability of the words in
word used in contexts other than the context of spill the beans in some particular context (the nu-
the target phrase. Each instance has an embedding merator of the expression above), we use XLNet to
uw1 , uw2 , ..., uwn . The average embedding for the obtain the product of the conditional probabilities
word among these sentences is: in the chain rule decomposition of the joint. We
get the relevant marginal probabilities by using at-
n
1X tention masks over particular words, as shown be-
µO = uwi . (1)
n low, where c refers to the context—that is, the rest
i=1
of the words in the sentence containing spill the
We take this quantity to be a proxy for the proto- beans.
typical meaning of the word (i.e., its conventional
meaning). The conventionality score is the nega-
tive of the average distance between uO and the Pr(beans | spill the, c) = ..spill the beans...
embeddings for uses of the word within the phrase Pr(the | spill, c) = ...spill the [___]...
in question. We compute this as follows: Pr(spill | c) = ...spill [___] [___]...
m The denominator is the product of the probabil-
1 X Ti − µO
conv(phrase) = − , (2) ities of each individual word in the phrase, with
m σO 2
i=1 both of the other words masked out:
where T is the embedding corresponding to a par-
ticular use of the word in the target phrase, and σO Pr(beans | c) = ...[___] [___] beans...
is the component-wise standard deviation of the Pr(the | c) = ...[___] the [___]...
set of embeddings uwi . Pr(spill | c) = ...spill [___] [___]...
3.3 Contingency measure The conditional probabilities were computed
Our second measure, which we have termed con- right to left, and with a paragraph of padding text
tingency, refers to whether a particular set of that came before the sentence containing the rel-
words appears within the same phrase at an un- evant phrase. Note that this measure calculates
expectedly high rate. The measure is based on the XLNet-based generalized PMI for the entire
the notion of pointwise mutual information (PMI), string bounded by the two words of the idiom –
which is a measure of the strength of association this means, for example, that the phrase spill the
between two events. We estimate a generalization exciting beans will return the PMI score for the
of PMI that extends it to sets of more than two entire phrase, adjective included.
events, allowing us to capture the association be-
4 Related work
tween phrases that contain more than two words.
The specific generalization of PMI that we use The measures described above have been instan-
has at various times been called total correla- tiated in various ways in previous work on idiom
tion (Watanabe, 1960), multi-information (Stu- detection.
dený and Vejnarová, 1998), and specific correla- Many idiom detection models build on insights
tion (Van de Cruys, 2011). about unconventional meaning in metaphor. A
number of these approaches use distributional
p(x1 , x2 , ..., xn ) models, such as Kintsch (2000), Utsumi (2011),
cont(x1 , x2 , ..., xn ) = log Qn . (3)
i=1 p(xi ) Sa-Pereira (2016), and Shutova et al. (2012), the
latter of which was one of the first to implement
For the case of three variables, we get:
a fully unsupervised approach that aims to en-
p(x, y, z) code relationships between words, their contexts,
cont(x, y, z) = log . (4) and their dependencies. A related line of work
p(x)p(y)p(z)has taken on the task of automatically determin- two measures together, we expect idioms to fall
ing whether potentially idiomatic expressions are at the intersection of low conventionality and high
being used idiomatically or literally in individual contingency, but not to show major discontinuities
sentences, based on contextual information (Katz that qualitatively distinguish them from phrases
and Giesbrecht, 2006; Fazly et al., 2009; Sporleder that fall at other areas of intersection.
and Li, 2009, 2014).
Our measure of conventionality is inspired by 5.1 Analysis 1: conventionality measure
the insights of these previous models. As de- In order to validate that our conventionality mea-
scribed in Section 3.2, our measure uses differ- sure corresponds to an intuitive notion of un-
ences in embeddings across contexts. usual word meaning, we carried out an online
Meanwhile, approaches to collocation detection experiment to see whether human judgments of
have taken a probabilistic or information-theoretic conventionality correlated with our automatically-
approach that seeks to identify collocations us- computed conventionality scores. The experimen-
ing word combination probabilities. A frequently- tal design and results are described below.
used quantity for measuring co-occurrence prob-
5.1.1 Human rating experiment
abilities is pointwise mutual information (PMI;
Fano, 1961; Church and Hanks, 1990). Other The experiment asked participants to rate the liter-
implementations include selectional association alness of a word or phrase in context. We focused
(Resnik, 1996), symmetric conditional probabil- the experiment on twenty-two verb object target
ity (Ferreira and Pereira Lopes, 1999), and log- phrases and their corresponding matched phrases.
likelihood (Dunning, 1993; Daille, 1996). To im- For each target phrase (i.e. rock the boat), there
plement our notion of contingency, we use a gen- were ten items, each of which consisted of the
eralized PMI measure that is in line with the mea- target phrase used in the context of a (different)
sures in previous work. sentence. Each sentence was presented with the
While much of the literature in NLP recognizes preceding sentence and the following sentence as
that idioms are characterized by several properties context, which is the same amount of context that
which must be measured separately (e.g., Fazly the automatic measure was given. In each item,
and Stevenson, 2006; Fazly et al., 2009), our ap- a word or phrase was highlighted, and the partici-
proach is novel in attempting to characterize id- pant was asked to rate the literalness of the high-
ioms along two orthogonal dimensions that cor- lighted element. We obtained judgments of the lit-
relate with human intuitions about word meaning eralness of the head word, non-head word, and en-
and usage. tire phrase for ten different sentences containing
each target phrase.
5 Analyses We also obtained literalness judgments of the
head word and the entire phrase for phrases
In this section we first present analyses of our two matched on the head of the idiom (e.g., verb object
measures individually, showing that they capture phrases with rock as the verb and some noun other
the properties they were intended to capture, fol- than boat as the object). Similarly, we obtained lit-
lowed by an exploration of the interaction between eralness judgments of the non-head word and the
the measures. Section 5.3 evaluates our central entire phrase for phrases matched on the non-head
predictions, showing results that are inconsistent word of the idiom (e.g., verb object phrases with
with a special mechanism theory of idioms (under boat as the object and some verb other than rock).
which a high level of word co-occurrence is pack- Participants were asked to rate literalness on a
aged together with non-canonical interpretation). scale from 1 (‘Not literal at all’) to 6 (‘Completely
We predict that the target phrases will score literal’). Items were presented using a Latin square
lower on conventionality than the matched design.
phrases, since we expect these phrases to contain Participants were adult native English speakers
words with (often highly) unconventional mean- who provided written informed consent to partici-
ings. We further predict that the target phrases will pate in the experiment. The experiment took about
have higher contingency scores than the matched 10 minutes to complete. The data were recorded
phrases, due to all of the target phrases being ex- using anonymized participant codes, and none of
pressions that are frequently reused. Putting the the results included any identifying information.There were 150 participants total. The data from phrases, with a difference in contingency value of
10 of those participants were excluded due to fail- 2.25 (t(159) = 8.807, p < 0.001).
ure to follow the instructions (assessed with catch
AN B
trials). 12.5
10.0 ●
5.1.2 Results 7.5
●
●
Contingency score
To explore whether our conventionality measure 5.0
●
correlates with human judgments of literalness, 2.5
NN VO
we compare the scores to the results from the hu- 12.5
man rating experiment. Ratings were between 1 10.0
and 6, with 6 representing the highest level of con- 7.5
ventionality. 5.0 ●
We predicted that the conventionality scores 2.5
●
Match Target Match Target
should increase as literalness ratings increased. To Phrase type (target vs. matched)
assess whether our prediction was borne out, a lin-
ear model was fit using the lmerTest (Kuznetsova Figure 2: Contingency of target and matched phrases,
et al., 2017) package in R (Team, 2017), with hu- for phrases with at least 30 instances
man rating average and highlighted word (head
versus non-head) as predictors, plus random ef- Figure 2 shows boxplots of the average contin-
fects of participant and item. Results of fitting gency score of each phrase’s average contingency
the model are shown in Table 2. The results con- score. Given that many of the target phrases only
firm our prediction that words that receive higher occurred in a handful of sentences, we have ex-
scores on the conventionality metric are rated as cluded phrases for which the target or matched
highly literal by humans (β̂ = 0.181, SE(β̂) = sets contain less than 30 sentences. This thresh-
0.024, p < 0.001). old was chosen to strike a balance between having
We carried out a nested model comparison to enough instances contributing to the average score
see whether including the BERT conventionality for each datapoint, and having a large enough sam-
score as a predictor significantly improved the ple of phrases. We considered thresholds at every
model. A likelihood ratio test with the above multiple of 10 until we reached one that left at least
model and one that differed in excluding the BERT 100 datapoints remaining. Within each structural
conventionality score as a predictor yielded a type of phrase (adjective noun, binomial, noun
higher log likelihood for the model with the BERT noun, verb object), the target phrases are on the
score as a predictor (χ2 = 42.784, p < 0.001). right and the non-idiomatic, matched phrases are
on the left. It should be noted that for the most
part, there were fewer sentences containing the tar-
Table 2: Model results table with human literalness rat-
get phrase than there were sentences containing
ing as the dependent variable
only the head word in the relevant structural posi-
Coefficient β̂ SE(β̂) t p tion. This likely explains the greater spread among
Intercept 0.033 0.026 1.258 0.104 the target phrases—namely, the averages are based
ConvScore 0.181 0.024 7.644 < 0.001 on fewer data points.
Word:Nonhead 0.024 0.020 1.220 0.111 For all four types of phrases, the median con-
tingency score was higher for the target phrases
n = 2117 than for their matched phrases. The greatest differ-
ences were observed for verb object and binomial
phrases.
5.2 Analysis 2: contingency measure
To evaluate the role of contingency in distinguish- 5.3 Analysis 3: interaction and correlation of
ing phrases that are often assumed to be stored (id- measures
ioms and collocations), we compare the scores of Here we show that idioms fall in the expected area
the target and matched phrases. of our two-dimensional space with no sharp dis-
We find that, overall, the target phrases had continuities or correlation between the measures,
higher contingency scores than the matched thus confirming our predictions.Recall the 2x2 matrix of contingency versus
conventionality (Figure 1), where idioms were ex-
10
pected to be in the top left quadrant. Plotting our
Contingency
measures against each other gives us the results
in Figure 3. Since the conventionality scores were
5
for individual words, we averaged the scores of the
head word and the primary non-head word (i.e.,
the verb and the object for verb object phrases, the 0
adjective and the noun for adjective noun phrases, −40 −35 −30 −25
the two nouns in noun noun phrases, and the two Conventionality
words of the same category in binomial phrases). Phrase type Target phrases Matched phrases
The plot shows the average values of the target and
matched phrases only if the phrase and matched Figure 4: Contingency versus conventionality of tar-
structure both occurred in at least 30 sentences in get and matched phrases (for target phrases selected as
the BNC. examples of idioms). Large points represent average
values.
15 tion. There are, of course, intermediate areas that
phrases can fall into.
Contingency
10 We also found no evidence of correlation be-
tween contingency and conventionality values
5 among the entire set of phrases, target and
matched (r(312) = -0.037, p = 0.518).
0
−45 −40 −35 −30 −25 6 Asymmetries between heads and
Conventionality dependents
Phrase type Target phrases Matched phrases
Our experiments revealed an unexpected but in-
Figure 3: Contingency versus conventionality of target teresting asymmetry between heads and their de-
and matched phrases. Large points represent average pendents. We found that the head word of the
values. target phrases was more conventional on average
than the non-head content word. A two-sample
As discussed above, the target phrases con- t-test revealed that this difference was significant
tained a mix of phrases typically considered id- (t = 3.029, df = 252.45, p = 0.0027). This effect
ioms, as well as (seemingly) compositional collo- was much more robust for the target phrases (made
cations. Therefore, we predicted that they would up of idioms and collocations) than it was for the
be distributed between the top two quadrants, with matched phrases. The matched phrases did not
idioms on the top left and collocations on the top show a significant difference between heads and
right. For the matched phrases, we assumed that non-heads (t = 1.506, df = 277.42, p = 0.1332).
the majority were instances of regular language Figure 5 presents the data in a slightly different
use, so we predicted that they should be clustered way. The plots here include all of the target and
in the bottom right quadrant. The horizontal and matched phrase, and show that at lower values of
vertical black lines on the plot were placed at the overall phrase conventionality, the unusual mean-
mean values for each measure. ing comes disproportionately from the non-head
Figure 4 shows only the target phrases that re- word. We have shown that idioms fall at the low
ceived a human annotation of 1 or 2 for head end of the conventionality spectrum, so the data in
word literality—that is, the phrases judged to be Figure 5 indicate that the unconventional meaning
most non-compositional. As expected, the average of idioms comes largely from the dependent word.
score for the target phrases moved more solidly The effect is largest for verb object phrases.
into the idiom quadrant. Note that, given the con- One possibility we raise for the difference
tinuous nature of the measures, the division of lan- in effect sizes between the phrase types is that
guage space into four quadrants is a simplifica- there could be an additive effect of linear order,with conventionality decreasing from left to right about language, and we showed that these mea-
through the phrase. For verb object phrases, the sures concentrate idioms in the predicted region of
two effects go in the same direction, whereas for the space. To compare the behavior of our conven-
adjective noun and noun noun phrases, the linear tionality measure with human behavior, we carried
order effect would go in the opposite direction as out an online rating experiment in which people
the headedness effect. rated the literalness of words and phrases in con-
There is disagreement in the literature about text. As predicted, higher literalness ratings corre-
whether binomial phrases (which are coordinate lated with higher conventionality scores given by
structures) contain a head at all. Some proposals our measure. As for the contingency measure, we
treat the first conjunct as the head (e.g., Rothstein, separated the phrases in our corpus into idioms
1991; Kayne, 1994; Gazdar, 1981), while others and collocations, on one hand, and non-idiom,
treat the conjunction as the head or claim that there non-collocations on the other. Again, as predicted,
is no head (e.g., Bloomfield, 1933). We note that the idioms and collocations scored higher on our
in the binomial case, the first conjunct seems to contingency measure.
pattern like the heads of the other phrase types, When we plotted the conventionality and con-
lending support to the first-conjunct-as-head the- tingency scores against one another, we found that
ory. idioms fell, on average, in the area of low con-
ventionality and high contingency, as expected,
AN B
3
while regular, non-idiomatic phrases fell in the
high conventionality, low contingency area, also
0 as expected. The lack of a correlation between the
two measures provides support for theories that di-
Word conventionality (rescaled)
−3
vorce the notions of compositionality and storage.
−6 Specifically, this lack of correlation indicates that
storage of a form is not dependent on the extent to
−9 which the form is compositional.
NN VO
3
We have thus provided evidence that idioms
trend toward one end of the intersection between
0 compositionality and storage, but are not other-
wise exceptional, contrary to theories that posit
−3
special machinery to handle idioms. Idioms, then,
−6 represent just one of the ways that composition-
ality and storage can interact, and are no more
−9 special than collocations or novel metaphors. We
−4 −2 0 2 −4 −2 0 2
Phrase conventionality (rescaled) have also presented the novel finding that the locus
Word type Head Non−head of non-compositionality in idioms resides primar-
ily in the dependent, rather than the head, of the
Figure 5: Change in head versus non-head convention- phrase, a result that merits further study.
ality scores as phrase conventionality increases, for all
phrases (target and matched) separated by phrase type
(adjective noun, binomial, noun noun, and verb object). References
Leonard Bloomfield. 1933. Language. Henry
7 Discussion & Conclusion Holt, New York.
We investigated whether idioms could be charac- Samuel Bobrow and Susan Bell. 1973. On catch-
terized as occupying the intersection between stor- ing on to idiomatic expressions. Memory &
age and compositionality of a form, without need- Cognition, 1:343–346.
ing to appeal to special machinery to handle these
phrases, as has been proposed at various times in Benjamin Bruening. 2019. Idioms, collocations,
the literature. and structure: Syntactic constraints on conven-
We validated that our conventionality and tionalized expressions. Natural Language and
contingency measures corresponded to intuitions Linguistic Theory, 70:491–538.Benjamin Bruening, Xuyen Dinh, and Lan Kim. Jennifer Hay. 2003. Causes and Consequences of
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pretraining for language understanding.
On the following page is a list of the target phrases
in our corpus.Target phrase Type Target phrase Type Target phrase Type Target phrase Type
deliver the goods VO swimming pool NN cold feet AN by and large B
run the show VO cash cow NN green light AN more or less B
rock the boat VO foot soldier NN red tape AN bits and pieces B
call the shots VO attorney general NN black box AN up and down B
talk turkey VO hit list NN blue sky AN rise and fall B
cut corners VO soup kitchen NN bright future AN sooner or later B
jump the gun VO bull market NN sour grape AN rough and ready B
have a ball VO boot camp NN green room AN far and wide B
foot the bill VO message board NN easy money AN give and take B
break the mold VO gold mine NN last minute AN time and effort B
pull strings VO report card NN hard heart AN pro and con B
mean business VO comfort food NN hot dog AN sick and tired B
raise hell VO pork barrel NN raw talent AN back and forth B
close ranks VO flower girl NN hard labor AN day and night B
strike a chord VO hit man NN broken home AN wear and tear B
cry wolf VO blood money NN fat chance AN nut and bolt B
lose ground VO cottage industry NN dirty joke AN tooth and nail B
make waves VO board game NN happy hour AN on and off B
clear the air VO death wish NN high time AN win or lose B
pay the piper VO word salad NN rich history AN food and shelter B
spill the beans VO altar boy NN clean slate AN odds and ends B
bite the dust VO bench warrant NN stiff competition AN in and out B
saw logs VO time travel NN maiden voyage AN sticks and stones B
lead the field VO love language NN cold shoulder AN make or break B
take the powder VO night owl NN clean energy AN part and parcel B
buy the farm VO life blood NN hard sell AN loud and clear B
turn tail VO road rage NN back pay AN cops and robbers B
get the sack VO light house NN deep pockets AN short and sweet B
hit the sack VO bid price NN broken promise AN safe and sound B
kick the bucket VO carrot cake NN dead silence AN black and blue B
shoot the bull VO command line NN blind faith AN toss and turn B
stag night NN tight schedule AN fair and square B
husband material NN brutal honesty AN heads or tails B
bright idea AN hearts and flowers B
kind soul AN rest and relaxation B
bruised ego AN flesh and bone B
life and limb B
checks and balances B
fast and loose B
high and dry B
pots and pans B
now or never B
hugs and kisses B
bread and butter B
risk and reward B
cloak and dagger B
pins and needles B nickel and dime B
sugar and spice B rhyme or reason B
neat and tidy B leaps and bounds B
step by step B live and learn B
lost and found B peace and quiet B
old and grey B song and dance BYou can also read
