A Distributional Semantics Model for Idiom Detection
The Case of English and Russian
Jing Peng
, Katsiaryna Aharodnik
and Anna Feldman
Montclair State University, Montclair, NJ, U.S.A.
CUNY Graduate Center, NY, U.S.A.
Idiom Recognition, Corpus Annotation, Distributional Semantics, English, Russian.
This paper describes experiments in English and Russian automatic idiom detection. Our algorithm is based
on the idea that literal and idiomatic expressions appear in different contexts. This difference is captured by
our distributional semantics model. We evaluate our model on both languages and compare its results. We
show that our model is language-independent. We also describe a new annotated resource we created for our
Idioms add color to language. Without idioms lan-
guage would be dull and unexciting. Idioms reflect
on our cultural values. Cross-linguistically, speak-
ers use different types of idioms to express similar
concepts. Thus for example, in American English,
one bites the bullet while in Russian, one squeezes
the teeth; in American English one puts a fly in the
ointment whereas in Russian one adds a spoon of tar
to the barrel of honey. Both Russians and Ameri-
cans shed crocodile tears. Many Natural Language
Processing applications, such as machine translation
(MT), natural language understanding (NLU), senti-
ment and emotion analysis could improve their per-
formance if idioms could be detected automatically
with good accuracy. It turns out that a large number
of expressions are ambiguous between their idiomatic
and literal interpretation and their status (idiomatic vs.
literal) can only be determined in context (e.g., sales
hit the roof vs. hit the roof of the car).
Several approaches have been explored in finding
a better solution to this problem (e.g.,(Katz and Gies-
brecht, 2006; Cook et al., 2007; Fazly et al., 2009;
Sporleder and Li, 2009; Li and Sporleder, 2010; Peng
et al., 2014a; Peng et al., 2015a; Peng and Feldman,
2016a; Peng and Feldman, 2016c; Pradhan et al.,
2017) among others). However, a number of ques-
tions about automatic processing of semantic relation-
ships specifically those that are not trivial to define
and disambiguate still remain unanswered.
The current paper addresses 1) the problem of de-
termining automatically whether an expression is lit-
eral or idiomatic in a specific context, and 2) whether
the same methodology can be generalized to other
languages besides English. In this paper, we only con-
sider those expressions that are ambiguous in nature
and can be interpreted either literally or figuratively
depending on the context they occur in. Below we
describe our approach.
Our approach is based on two hypotheses: (1) words
in a given text segment that are representatives of the
local context are likely to associate strongly with a lit-
eral expression in the segment, in terms of projection
of word vectors onto the vector representing the literal
expression; (2) the context word distribution for a lit-
eral expression in word vector space will be different
from the distribution for an idiomatic one (similarly
to (Firth, 1957; Katz and Giesbrecht, 2006)).
2.1 Projection based on Local Context
To address the first hypothesis, we propose to exploit
recent advances in vector space representation to cap-
ture the difference between local contexts (Mikolov
et al., 2013a; Mikolov et al., 2013b).
A word can be represented by a vector of fixed
dimensionality q that best predicts its surrounding
Peng, J., Aharodnik, K. and Feldman, A.
A Distributional Semantics Model for Idiom Detection - The Case of English and Russian.
DOI: 10.5220/0006733806750682
In Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) - Volume 2, pages 675-682
ISBN: 978-989-758-275-2
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
words in a sentence or a document (Mikolov et al.,
2013a; Mikolov et al., 2013b). Given such a vector
representation, our first proposal is the following. Let
v and n be the vectors corresponding to the verb and
noun in a target verb-noun construction, as in blow
whistle, where v
represents blow and n
represents whistle. Let
= v + n
Thus, σ
is the word vector that represents the com-
position of verb v and noun n, and in our example,
the composition of blow and whistle. As indicated in
(Mikolov et al., 2013b), word vectors obtained from
deep learning neural net models exhibit linguistic reg-
ularities, such as additive compositionality. There-
fore, σ
is justified to predict surrounding words of
the composition of, say, blow and whistle in a literal
context. Our hypothesis is that on average, the projec-
tion of v onto σ
, (i.e., v · σ
, assum-
ing that σ
has unit length), where vs are con-
text words in a literal usage, should be greater than
v · σ
, where vs are context words in an id-
iomatic usage.
For a given vocabulary of m words, represented by
V = [v
, v
, ··· , v
we calculate the projection of each word v
in the vo-
cabulary onto σ
P = V
where P
, and t represents transpose. Here we
assume that σ
is normalized to have unit length.
Thus, P
= v
indicates how strongly word vector
is associated with σ
. This projection forms the
basis for our proposed technique.
D = {d
, d
, ··· , d
be a set of l text segments (local contexts), each con-
taining a target VNC (i.e., σ
). Instead of generating
a term by document matrix, where each term is tf-
idf (product of term frequency and inverse document
frequency), we compute a term by document matrix
, where each term in the matrix is
p · id f . (2)
That is, the product of the projection of a word onto
a target VNC and inverse document frequency. That
is, the term frequency (tf) of a word is replaced by the
projection of the word onto σ
(1). Note that if seg-
ment d
does not contain word v
, M
(i, j) = 0, which
is similar to tf-idf estimation. The motivation is that
topical words are more likely to be well predicted by
a literal VNC than by an idiomatic one. The assump-
tion is that a word vector is learned in such a way
that it best predicts its surrounding words in a sen-
tence or a document (Mikolov et al., 2013a; Mikolov
et al., 2013b). As a result, the words associated with
a literal target will have larger projection onto a target
. On the other hand, the projections of words asso-
ciated with an idiomatic target VNC onto σ
have a smaller value.
We also propose a variant of p ·id f representation.
In this representation, each term is a product of p and
typical tf-idf. That is,
p ·t f · id f. (3)
2.2 Local Context Distributions
Our second hypothesis states that words in a local
context of a literal expression will have a different
distribution from those in the context of an idiomatic
one. We propose to capture local context distributions
in terms of scatter matrices in a space spanned by
word vectors (Mikolov et al., 2013a; Mikolov et al.,
d = (w
, w
··· , w
be a segment (document) of k words, where w
are represented by a vectors (Mikolov et al., 2013a;
Mikolov et al., 2013b). Assuming w
s have been cen-
tered, we compute the scatter matrix
Σ = d
d, (4)
where Σ represents the local context distribution for a
given target VNC.
Given two distributions represented by two scat-
ter matrices Σ
and Σ
, a number of measures can be
used to compute the distance between Σ
and Σ
, such
as Choernoff and Bhattacharyya distances (Fukunaga,
1990). Both measures require the knowledge of ma-
trix determinant. We propose to measure the differ-
ence between Σ
and Σ
using matrix norms. We have
experimented with the Frobenius norm and the spec-
tral norm. The Frobenius norm evaluates the differ-
ence between Σ
and Σ
when they act on a standard
basis. The spectral norm, on the other hand, evalu-
ates the difference when they act on the direction of
maximal variance over the whole space.
We carried out an empirical study evaluating the per-
formance of the proposed techniques. The following
methods are evaluated:
1. p · id f : compute term by document matrix from
training data with proposed p · id f weighting (2).
NLPinAI 2018 - Special Session on Natural Language Processing in Artificial Intelligence
2. p · t f · id f : compute term by document matrix
from training data with proposed p*tf-idf weight-
ing (3).
3. CoVAR
: proposed technique (4) described in
Section 2.2, the distance between two matrices is
computed using Frobenius norm.
4. CoVAR
: proposed technique similar to
. However, the distance between two
matrices is determined using the spectral norm.
For methods 3 and 4, we compute the literal and
idiomatic scatter matrices from training data (4). For
a test example, compute a scatter matrix according to
(4), and calculate the distance between the test scatter
matrix and training scatter matrices using the Frobe-
nius norm for method 3, and the spectral norm for
method 4.
4.1 English
We use BNC and a list of VNCs (Cook et al., 2008)
(described above) and labeled as L (Literal), I (Id-
ioms), or Q (Unknown). For our experiments we only
use VNCs that are annotated as I or L. We only ex-
perimented with idioms that can have both literal and
idiomatic interpretations. Each document contains
three paragraphs: a paragraph with a target VNC, the
preceding paragraph and following one. Our data is
summarized in Table 1.
Table 1: Datasets: Is = idioms; Ls = literals.
Expression Train Test
BlowWhistle 20 Is, 20 Ls 7 Is, 31 Ls
LoseHead 15 Is, 15 Ls 6 Is, 4 Ls
MakeScene 15 Is, 15 Ls 15 Is, 5 Ls
TakeHeart 15 Is, 15 Ls 46 Is, 5 Ls
BlowTop 20 Is, 20 Ls 8 Is, 13 Ls
GiveSack 20 Is, 20 Ls 26 Is, 36 Ls
HaveWord 30 Is, 30 Ls 37 Is, 40 Ls
HitRoof 50 Is, 50 Ls 42 is, 68 Ls
HitWall 90 Is, 90 Ls 87 is, 154 Ls
HoldFire 20 Is, 20 Ls 98 Is, 6 Ls
HoldHorse 80 Is, 80 Ls 162 Is, 79 Ls
Since BNC did not contain enough examples, we
extracted additional ones from COCA, COHA and
GloWbE (http://corpus.byu.edu/). Two human anno-
tators labeled this new dataset for idioms and liter-
als. The inter-annotator agreement was relatively low
(Cohen’s kappa = .58); therefore, we merged the re-
sults keeping only those entries on which the two an-
notators agreed.
Table 2: Russian idioms: Examples of different syntactic
Syntactic Construction Example Count
Adj(Poss. Pron) + Noun
cernyj voron 20
Prep+Noun bez golovy 17
Prep+Adj+Noun na moju golovu 3
Verb+(Prep)+Noun vtsepit’sja v glotku 50
Adv + Verb
zirno budet 2
Noun + Short Adj kontsert okon
cen 4
Prep+Noun+Verb kuda veter duet 4
Table 3: Russian examples: Is = idioms; Ls = literals.
Target Gloss Interpretation I L
s bleskom with flying colors brilliantly 222 38
na svoju golovu on your own head pain in the neck 119 39
na vysote at the height rise to the occasion 147 223
smotret’ v glaza look into the eyes face (challenges) 45 72
cerez golovu over the head go over someone’s head 58 224
na no
zax with the knives to be at daggers drawn 40 39
po barabanu on the drums couldn’t care less 64 19
vtoroj dom second home second home 13 33
se sebja above oneself beyond the possible 36 9
dlinnyj jazyk long tongue chatterbox 26 22
For our experiments reported here, we ob-
tained English word vectors using the word2vec tool
(Mikolov et al., 2013a; Mikolov et al., 2013b) and
the text8 corpus. The text8 corpus has more than 17
million words, which can be obtained from mattma-
honey.net/dc/text8.zip. The resulting vocabulary has
71,290 words, each of which is represented by a
q = 200 dimension vector. Thus, this 200 dimensional
vector space provides a basis for our experiments.
4.1.1 English Datasets
Table 1 describes the datasets we used to evaluate the
performance of the proposed technique. All these
verb-noun constructions are ambiguous between lit-
eral and idiomatic interpretations.
4.2 Russian
4.2.1 Corpus Collection
For the list of idioms, a Russian-English dictionary
of idioms was used as a primary source (Lubensky,
2013). Initially, 150 idioms (target expressions) were
included in the list. The rationale for choosing a
certain target expression was that each expression
could be interpreted as either idiomatic or literal de-
pending on the context. For example, an expression
postavit’ to
cku (’put a stop’) can appear in a sentence
like U
citelnitsa napomnila Ma
cto nu
zno postavit’
cku v kontse predo
zenija (’The teacher reminded
Masha to put a period at the end of a sentence’) with
A Distributional Semantics Model for Idiom Detection - The Case of English and Russian
Table 4: Average accuracy of competing methods on 11 datasets: BlWh (BlowWhistle), LoHe (LoseHead), MaSe
(MakeScene), TaHe (TakeHeart), BlTo (BlowTop), GiSa (GiveSack), HaWo (HaveWord), HiRo (HitRoof), HiWa (HitWall),
HoFi (HoldFire), and HoHo (HoldHorse).
BlWh LoHe MaSe TaHe BlTo GiSa HaWo HiRo HiWa HoFi HoHo Ave
p · id f 0.29 0.49 0.82 0.9 0.59 0.55 0.52 0.54 0.55 0.97 0.86 0.64
p ·t f · id f 0.23 0.31 0.4 0.78 0.54 0.54 0.53 0.41 0.39 0.95 0.84 0.54
0.65 0.6 0.84 0.95 0.81 0.63 0.58 0.61 0.59 0.97 0.86 0.74
0.44 0.62 0.8 0.94 0.71 0.66 0.56 0.54 0.5 0.96 0.77 0.68
p · id f 0.82 0.27 0.48 0.43 0.58 0.47 0.53 0.84 0.92 0.83 0.81 0.63
p ·t f · id f 0.99 0.3 0.11 0.11 0.53 0.64 0.53 0.98 0.97 0.89 0.97 0.64
0.71 0.78 0.83 0.61 0.87 0.88 0.49 0.88 0.94 0.86 0.97 0.80
0.77 0.81 0.82 0.55 0.79 0.75 0.53 0.85 0.95 0.87 0.85 0.78
p · id f 0.6 0.48 0.53 0.44 0.68 0.62 0.54 0.66 0.7 0.81 0.78 0.62
p ·t f · id f 0.37 0.49 0.33 0.18 0.65 0.55 0.53 0.45 0.43 0.85 0.86 0.52
0.87 0.58 0.75 0.62 0.86 0.72 0.58 0.74 0.74 0.84 0.87 0.74
0.77 0.61 0.72 0.56 0.79 0.73 0.58 0.66 0.64 0.84 0.73 0.69
the literal interpretation and also in a sentence like
Ona re
sila effektno postavit’ to
cku v svoej kar’ere.
(’She decided to effectively put an end to her career’).
The list of idioms includes only multiword expres-
sions (MWE). Each target expression consists of more
than one word token, with their length ranging from
two, e.g., dlinnyj jazyk (long tongue), to four word to-
kens as in s penoj u rta (with frothing at the mouth).
Unlike for English, syntactically, target expressions
were not limited to a single structure. We collected
prepositional phrases, such as (bez golovy) (’without
head’), nouns with adjectival or possessive modifiers,
e.g., vtoroj dom (’second home’), verb phrases, e.g.,
plyt’ po te
ceniju (to go with the flow, Verb +PP),
and postavit’ to
cku (to put an end, Verb+NP). Table
2 provides a list of syntactic constructions with their
counts. The list included idioms in their dictionary
form, but each idiomatic expression was extracted
from the compiled corpora in any form it appeared
in files (conjugated forms for verbs or declined forms
for adjectives and nouns).
For the Russian experiments, we used pretrained
word vectors, trained on Wikipedia using fastText.
These vectors in dimension 300 were obtained using
the skip-gram model described in (Bojanowski et al.,
2016) with default parameters.
4.2.2 Extracting Target Expressions
A target token is defined as a multiword expression
that can be identified as either idiomatic or literal
within the text. Each target expression was extracted
with one preceding it and one following it paragraph
from a source text file. Thus, one entry is defined as
a three paragraph text in one file. Each target expres-
sion was extracted following the steps below:
1. Convert the online text file to html format. This
was done to preserve the html tags and use the
tags for paragraph extraction.
2. Save each file as a plain text document with pre-
served html tags.
3. Extract each target expression (token) from each
html document in a three paragraph format, with
the second paragraph containing a target expres-
4. Save each three paragraph entry in a separate text
Overall, 100 tokens/target expressions were used
to create the idiom-annotated corpus.
4.2.3 Annotation
Once the expressions were extracted, each file was
annotated manually by two Russian native speak-
ers. The overall inter-annotator agreement was high
(Kappa 0.81). Each target expression was assigned a
tag, Idiomatic (I) or Literal (L).
A list of 10 target expressions extracted for the
corpus is provided in Table 3. It also includes the
counts of idiomatic and literal interpretations for each
idiom. This paper is just a pilot study of the Russian
idioms, therefore, we only report the performance of
our system on three constructions, but in our future
work we will use the entire corpus to evaluate the sys-
NLPinAI 2018 - Special Session on Natural Language Processing in Artificial Intelligence
Table 5: Average performance of competing methods on Russian idioms.
na svoju golovu na vysote smotret’ v glaza
get into trouble to be at one’s best to face (a challenge) Ave
p · id f 0.75 0.49 0.40 0.55
p ·t f · id f 0.80 0.50 0.50 0.60
0.80 0.71 0.49 0.67
0.78 0.64 0.54 0.65
p · id f 0.73 0.83 0.40 0.65
p ·t f · id f 0.76 0.81 0.42 0.66
0.88 0.81 0.50 0.73
0.76 0.76 0.50 0.67
p · id f 0.63 0.64 0.57 0.61
p ·t f · id f 0.68 0.66 0.67 0.67
0.76 0.82 0.65 0.74
0.68 0.77 0.68 0.71
5.1 English
Table 4 shows the average precision, recall and accu-
racy of the competing methods on 11 datasets over 20
runs. (The average best performance is in bold face.
We calculate accuracy by adding true positives and
true negatives and normalizing the sum by the num-
ber of examples. The results show that the CoVAR
model outperforms the rest of the models overall.
Interestingly, the Frobenius norm outperforms the
spectral norm. One possible explanation is that the
spectral norm evaluates the difference when two ma-
trices act on the maximal variance direction, while the
Frobenius norm evaluates on a standard basis. That is,
Frobenius measures the difference along all basis vec-
tors. On the other hand, the spectral norm evaluates
changes in a particular direction. When the difference
is a result of all basis directions, the Frobenius norm
potentially provides a better measurement. The pro-
jection methods (p · id f and p · t f · id f ) outperform
t f · id f overall but not as pronounced as CoVAR.
Finally, we have noticed that even the best model
) does not perform as well on certain id-
iomatic expressions. We hypothesize that the model
works the best on highly idiomatic expressions.
5.2 Russian
The results of the experiments using the new Rus-
sian corpus are reported in Table 5. We evaluate our
models on three expressions. Right now, our prelim-
inary numbers indicate that the Russian model per-
forms similarly to English, even though Russian is a
more morphologically complex language and has free
word order.
We measure the correlation between the human
judgements and the competing algorithms in terms of
Pearson’s correlation coefficient. Figure 1 shows the
plots of the correlation matrices between the average
human judgements per idiom type shown in Table 6
and the judgements by the algorithms. The resulting
correlation matrices show that the performance of the
proposed algorithm CoVar
is highly correlated with
the human judgements, followed by CoVar
. This
once again demonstrates that CoVar
is capable of
exploiting context information.
6.1 Related Work
Previous approaches to idiom detection can be classi-
fied into two groups: 1) type-based extraction, i.e., de-
tecting idioms at the type level, e.g., (Sag et al., 2002;
Fazly et al., 2009; Widdows and Dorow, 2005; Hearst,
1992); 2) token-based detection, i.e., detecting id-
ioms in context. Type-based extraction is based on the
idea that idiomatic expressions exhibit certain linguis-
tic properties such as non-compositionality that can
distinguish them from literal expressions (Sag et al.,
A Distributional Semantics Model for Idiom Detection - The Case of English and Russian
Correlation Matrix
0 0.2 0.4 0.6 0.8
2 2.5 3
Correlation Matrix
0 0.5 1
2 2.5 3
Correlation Matrix
0.6 0.8
2 2.5 3
Correlation Matrix
0.6 0.8
2 2.5 3
Figure 1: Pairwise Pearson’s correlation matrix between the human judgements and the competing algorithms. Top row:
p · id f and p · t f · id f . Middle row: CoVar
and CoVar
2002; Fazly et al., 2009). While many idioms do have
these properties, many idioms fall on the continuum
from being compositional to being partly unanalyz-
able to completely non-compositional (Cook et al.,
2007). (Katz and Giesbrecht, 2006; Birke and Sarkar,
2006; Fazly et al., 2009; Sporleder and Li, 2009; Li
and Sporleder, 2010; Bu et al., 2010; Boukobza and
Rappoport, 2009; Reddy et al., 2011), among oth-
ers, notice that type-based approaches do not work
on expressions that can be interpreted idiomatically
or literally depending on the context and thus, an ap-
proach that considers tokens in context is more ap-
propriate for idiom recognition. To address these
problems, (Peng et al., 2014b) investigate the bag of
words topic representation and incorporate an addi-
tional hypothesis–contexts in which idioms occur are
more affective. Still, they treat idioms as semantic
outliers. (Yazdani et al., 2015; Salehi et al., 2015;
Peng et al., 2015b; Salton et al., 2016; Peng and Feld-
man, 2016b; Cordeiro et al., 2016) explore a range of
distributional vector-space models for semantic com-
In this paper we described a distributional approach to
idiom detection and tested it on English and Russian
data. Our results suggest that the proposed approach
is applicable to languages other than English, with
more complex morphology and more flexible word
order compared to English.
We also reported the results of an experiment in
which human annotators ranked English idiomatic ex-
pressions in context on a scale from 1 (literal) to 4
(highly idiomatic). Our experiment supports the hy-
pothesis that idioms fall on a continuum and that one
might differentiate between highly idiomatic, mildly
idiomatic and weakly idiomatic expressions. In addi-
tion, we measured the relative idiomaticity of 11 id-
iomatic types and computed the correlation between
the relative idiomaticity of an expression and the per-
formance of various automatic models for idiom de-
Our best performing Russian idioms syntactically
represent prepositional phrases (PPs): na (Prep) svoyu
(Attribute) golovu (Noun); na (Prep) vysote (Noun),
thus suggesting that our model is able to perform well
not just on verb-noun constructions reported for En-
glish. Noticeably, the two best performing expres-
sions, when idiomatic, are highly idiomatic (accord-
ing to our annotators) and we think that the average
idiomaticity correlates with the model’s performance,
similarly to the English case. Like in English, the best
performing idioms are those that are highly idiomatic
in certain contexts and unambiguously non-idiomatic
in others. For instance, it can be seen from the cor-
pus that na svoju golovu is associated with certain
verbs that appear with literal but not idiomatic inter-
pretations. In addition, those idioms that are harder
to disambiguate by human annotators (e.g., smotret’ v
glaza) are also harder to disambiguate automatically.
In our current work we are running experiments
on a larger Russian dataset, exploring a variety of syn-
tactic constructions, but experiments described in this
paper suggest that we are moving in the right direction
toward automatic idiom detection.
NLPinAI 2018 - Special Session on Natural Language Processing in Artificial Intelligence
Table 6: Average human rankings of 11 idiom types.
hold fire 3.28
hold horse 3.37
blow whistle 3.16
have word 2.29
give sack 3.33
take hear 3.30
lose head 3.35
make scene 3.02
hit wall 3.19
hit roof 3.34
blow top 3.44
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