In God we trust. All others must bring data. — W. Edwards DemingUsing word embeddings to recognize idioms

Jing Peng, Anna FeldmanDepartment of Computer Science

Department of LinguisticsMontclair State University

USApengj@mail.montclair.edu, feldmana@mail.montclair.edu

Abstract

Expressions, such as add fuel to the fire,can be interpreted literally or idiomaticallydepending on the context they occur in.Many Natural Language Processing appli-cations could improve their performance ifidiom recognition were improved. Our ap-proach is based on the idea that idioms vi-olate cohesive ties in local contexts, whileliteral expressions do not. We proposetwo approaches: 1) Compute inner prod-uct of context word vectors with the vec-tor representing a target expression. Sinceliteral vectors predict well local contexts,their inner product with contexts should belarger than idiomatic ones, thereby tellingapart literals from idioms; and (2) Com-pute literal and idiomatic scatter (covari-ance) matrices from local contexts in wordvector space. Since the scatter matri-ces represent context distributions, we canthen measure the difference between thedistributions using the Frobenius norm.For comparison, we implement Fazly et al.(2009)’s, Sporleder and Li (2009)’s, andLi and Sporleder (2010b)’s methods andapply them to our data. We provide ex-perimental results validating the proposedtechniques.

1 Introduction

Natural language is filled with emotion and im-plied intent, which are often not trivial to detect.One specific challenge are idioms. Figurative lan-guage draws off of prior references and is uniqueto each culture and sometimes what we don’t sayis even more important than what we do. This,naturally, presents a significant problem for manyNatural Language Processing (NLP) applicationsas well as for big data analytics.

Idioms are conventiolized expressions whosefigurative meanings cannot be derived from literalmeaning of the phrase. There is no single agreed-upon definition of idioms that covers all membersof this class (Glucksberg, 1993; Cacciari, 1993;Nunberg et al., 1994; Sag et al., 2002; Villavicen-cio et al., 2004; Fellbaum et al., 2006). At thesame time, idioms do not form a homogeneousclass that can be easily defined. Some examplesof idioms are I’ll eat my hat (I’m confident), Cutit out (Stop talking/doing something), a blessingin disguise (some bad luck or misfortune resultsin something positive), kick the bucket (die), ringa bell (sound familiar), keep your chin up (remaincheerful), piece of cake (easy task), miss the boat(miss out on something), (to be) on the ball (be at-tentive/competent), put one’s foot in one’s mouth(say something one regrets), rake someone overthe coals (to reprimand someone severely), underthe weather (sick), a hot potato (controversial is-sue), an arm and a leg (expensive), at the drop of ahat (without any hesitation), barking up the wrongtree (looking in the wrong place), beat around thebush (avoiding main topic).

It turns out that expressions are often ambigu-ous between an idiomatic and a literal interpreta-tion, as one can see in the examples below 1:

(A) After the last page was sent to the printer,an editor would ring a bell, walk toward the door,and holler ” Good night! ” (Literal) (B) Hisname never fails to ring a bell among local voters.Nearly 40 years ago, Carthan was elected mayorof Tchula. . . (Idiomatic)

(C) . . . that caused the reactor to literally blowits top. About 50 tons of nuclear fuel evapo-rated in the explosion. . . (Literal) (D) . . . He didn’tpound the table, he didn’t blow his top. He alwayskept his composure. (Idiomatic)

1These examples are extracted from the Corpus of Con-temporary American English (COCA) (http://corpus.byu.edu/coca/

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(E) . . . coming out of the fourth turn, slid downthe track, hit the inside wall and then hit the atten-uator at the start of pit road. (Literal) (F) . . . jobtraining, research and more have hit a Republicanwall. (Idiomatic)

Fazly et al. (2009)’s analysis of 60 idioms fromthe British National Corpus (BNC) has shownthat close to half of these also have a clear lit-eral meaning; and of those with a literal mean-ing, on average around 40% of their usages areliteral. Therefore, idioms present great challengesfor many Natural Language Processing (NLP) ap-plications. Most current translation systems relyon large repositories of idioms. Unfortunately,more frequently than not, MT systems are not ableto translate idiomatic expressions correctly.

In this paper we describe an algorithm for auto-matic classification of idiomatic and literal expres-sions. Similarly to Peng et al. (2014), we treat id-ioms as semantic outliers. Our assumption is thatthe context word distribution for a literal expres-sion will be different from the distribution for anidiomatic one. We capture the distribution in termsof covariance matrix in vector space.

2 Previous Work

Previous approaches to idiom detection can beclassified into two groups: 1) type-based extrac-tion, i.e., detecting idioms at the type level; 2)token-based detection, i.e., detecting idioms incontext. Type-based extraction is based on theidea that idiomatic expressions exhibit certain lin-guistic properties such as non-compositionalitythat can distinguish them from literal expressions(Sag et al., 2002; Fazly et al., 2009). Whilemany idioms do have these properties, many id-ioms fall on the continuum from being composi-tional to being partly unanalyzable to completelynon-compositional (Cook et al., 2007). Katz andGiesbrecht (2006), Birke and Sarkar (2006), Fa-zly et al. (2009), Li and Sporleder (2009), Li andSporleder (2010a), Sporleder and Li (2009), andLi and Sporleder (2010b), among others, noticethat type-based approaches do not work on expres-sions that can be interpreted idiomatically or lit-erally depending on the context and thus, an ap-proach that considers tokens in context is moreappropriate for idiom recognition.To address theseproblems, Peng et al. (2014) investigate the bag ofwords topic representation and incorporate an ad-ditional hypothesis–contexts in which idioms oc-

cur are more affective. Still, they treat idioms assemantic outliers.

3 Our Approach

We hypothesize that words in a given text seg-ment that are representatives of the local contextare likely to associate strongly with a literal ex-pression in the segment, in terms of projection (orinner product) of word vectors onto the vector rep-resenting the literal expression. We also hypoth-esize that the context word distribution for a lit-eral expression in word vector space will be dif-ferent from the distribution for an idiomatic one.This hypothesis also underlies the distributionalapproach to meaning (Firth, 1957; Katz and Gies-brecht, 2006).

3.1 Projection Based On Local ContextRepresentation

The local context of a literal target verb-noun con-struction (VNC) must be different from that of anidiomatic one. We propose to exploit recent ad-vances in vector space representation to capturethe difference between local contexts (Mikolov etal., 2013a; Mikolov et al., 2013b).

A word can be represented by a vector of fixeddimensionality q that best predicts its surroundingwords in a sentence or a document (Mikolov et al.,2013a; Mikolov et al., 2013b). Given such a vectorrepresentation, our first proposal is the following.Let v and n be the vectors corresponding to theverb and noun in a target verb-noun construction,as in blow whistle, where v 2 <q represents blowand n 2 <q represents whistle. Let �

vn

= v+n 2<q. Thus, �

vn

is the word vector that representsthe composition of verb v and noun n, and in ourexample, the composition of blow and whistle. Asindicated in Mikolov et al. (2013b), word vectorsobtained from deep learning neural net models ex-hibit linguistic regularities, such as additive com-positionality. Therefore, �

vn

is justified to pre-dict surrounding words of the composition of, say,blow and whistle. Our hypothesis is that on av-erage, inner product �

blowwhistle

· v, where vs arecontext words in a literal usage, should be greaterthan �

blowwhistle

·v, where vs are context words inan idiomatic usage.

For a given vocabulary of m words, representedby matrix V = [v1, v2, · · · , vm] 2 <q⇥m, we cal-culate the projection of each word v

i

in the vocab-

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ulary onto �vn

P = V t�vn

(1)

where P 2 <m, and t represents transpose. Herewe assume that �

vn

is normalized to have unitlength. Thus, P

i

= vti

�vn

indicates how stronglyword vector v

i

is associated with �vn

. This pro-jection, or inner product, forms the basis for ourproposed technique.

Let D = {d1, d2, · · · , dl

} be a set of l text seg-ments (local contexts), each containing a targetVNC (i.e., �

vn

). Instead of generating a term bydocument matrix, where each term is tf-idf (prod-uct of term frequency and inverse document fre-quency), we compute a term by document matrixM

D

2 <m⇥l, where each term in the matrix is

p · idf, (2)

the product of the projection of a word onto a tar-get VNC and inverse document frequency. Thatis, the term frequency (tf) of a word is replacedby the projection (inner product) of the word onto�vn

(1). Note that if segment dj

does not containword v

i

, MD

(i, j) = 0, which is similar to tf-idfestimation. The motivation is that topical wordsare more likely to be well predicted by a literalVNC than by an idiomatic one. The assumption isthat a word vector is learned in such a way that itbest predicts its surrounding words in a sentenceor a document (Mikolov et al., 2013a; Mikolovet al., 2013b). As a result, the words associatedwith a literal target will have larger projection ontoa target �

vn

. On the other hand, the projectionsof words associated with an idiomatic target VNConto �

vn

should have a smaller value.We also propose a variant of p · idf representa-

tion. In this representation, each term is a productof p and typical tf-idf. That is,

p · tf · idf. (3)

3.2 Local Context DistributionsOur second hypothesis states that words in a localcontext of a literal expression will have a differ-ent distribution from those in the context of an id-iomatic one. We propose to capture local contextdistributions in terms of scatter matrices in a spacespanned by word vectors (Mikolov et al., 2013a;Mikolov et al., 2013b).

Let d = (w1, w2 · · · , wk

) 2 <q⇥k be a seg-ment (document) of k words, where w

i

2 <q are

represented by a vectors (Mikolov et al., 2013a;Mikolov et al., 2013b). Assuming w

i

s have beencentered, we compute the scatter matrix

⌃ = dtd, (4)

where ⌃ represents the local context distributionfor a given target VNC.

Given two distributions represented by two scat-ter matrices ⌃1 and ⌃2, a number of measurescan be used to compute the distance between ⌃1

and ⌃2, such as Choernoff and Bhattacharyya dis-tances (Fukunaga, 1990). Both measures requirethe knowledge of matrix determinant. In our case,this can be problematic, because ⌃ (4) is mostlikely to be singular, which would result in a de-terminant to be zero.

We propose to measure the difference between⌃1 and ⌃2 using matrix norms. We have experi-mented with the Frobenius norm and the spectralnorm. The Frobenius norm evaluates the differ-ence between ⌃1 and ⌃2 when they act on a stan-dard basis. The spectral norm, on the other hand,evaluates the difference when they act on the di-rection of maximal variance over the whole space.

4 ExperimentsWe have carried out an empirical study evaluatingthe performance of the proposed techniques. Thegoal is to predict the idiomatic usage of VNCs.

4.1 MethodsFor comparison, the following methods are evalu-ated.

1. tf · idf : compute term by document matrixfrom training data with tf · idf weighting.

2. p · idf : compute term by document ma-trix from training data with proposed p · idfweighting (2).

3. p · tf · idf : compute term by document ma-trix from training data with proposed p*tf-idfweighting (3).

4. CoVARFro : proposed technique (4) de-scribed in Section 3.2, the distance betweentwo matrices is computed using Frobeniusnorm.

5. CoVARSp : proposed technique similar toCoVARFro . However, the distance betweentwo matrices is determined using the spectralnorm.

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6. Context+ (CTX+): supervised version of theCONTEXT technique described in Fazly etal. (2009) (see below).

For methods from 1 to 3, we compute a latentspace from a term by document matrix obtainedfrom the training data that captures 80% variance.To classify a test example, we compute cosinesimilarity between the test example and the train-ing data in the latent space to make a decision.

For methods 4 and 5, we compute literal and id-iomatic scatter matrices from training data (4). Fora test example, compute a scatter matrix accordingto (4), and calculate the distance between the testscatter matrix and training scatter matrices usingthe Frobenius norm for method 4, and the spectralnorm for method 5.

Method 6 corresponds to a supervised versionof CONTEXT described in Fazly et al. (2009).CONTEXT is unsupervised because it does notrely on manually annotated training data, ratherit uses knowledge about automatically acquiredcanonical forms (C-forms). C-forms are fixedforms corresponding to the syntactic patterns inwhich the idiom normally occurs. Thus, the gold-standard is “noisy” in CONTEXT. Here we pro-vide manually annotated training data. That is, thegold-standard is “clean.” Therefore, CONTEXT+is a supervised version of CONTEXT. We imple-mented this approach from scratch since we hadno access to the code and the tools used in the orig-inal article and applied this method to our datasetand the performance results are reported in Table2.

Table 1: Datasets: Is = idioms; Ls = literalsExpression Train TestBlowWhistle 20 Is, 20 Ls 7 Is, 31 LsLoseHead 15 Is, 15 Ls 6 Is, 4 LsMakeScene 15 Is, 15 Ls 15 Is, 5 LsTakeHeart 15 Is, 15 Ls 46 Is, 5 LsBlowTop 20 Is, 20 Ls 8 Is, 13 LsBlowTrumpet 50 Is, 50 Ls 61 Is, 186 LsGiveSack 20 Is, 20 Ls 26 Is, 36 LsHaveWord 30 Is, 30 Ls 37 Is, 40 LsHitRoof 50 Is, 50 Ls 42 is, 68 LsHitWall 90 Is, 90 Ls 87 is, 154 LsHoldFire 20 Is, 20 Ls 98 Is, 6 LsHoldHorse 80 Is, 80 Ls 162 Is, 79 Ls

4.2 Data PreprocessingWe use BNC (Burnard, 2000) and a list of verb-noun constructions (VNCs) extracted from BNCby Fazly et al. (2009) and Cook et al. (2008)

and labeled as L (Literal), I (Idioms), or Q (Un-known). The list contains only those VNCs whosefrequency was greater than 20 and that occurredat least in one of two idiom dictionaries (Cowieet al., 1983; Seaton and Macaulay, 2002). Thedataset consists of 2,984 VNC tokens. For our ex-periments we only use VNCs that are annotated asI or L. We only experimented with idioms that canhave both literal and idiomatic interpretations. Weshould mention that our approach can be appliedto any syntactic construction. We decided to useVNCs only because this dataset was available andfor fair comparison – most work on idiom recog-nition relies on this dataset.

We use the original SGML annotation to extractparagraphs from BNC. Each document containsthree paragraphs: a paragraph with a target VNC,the preceding paragraph and following one.

Since BNC did not contain enough examples,we extracted additional ones from COCA, COHAand GloWbE (http://corpus.byu.edu/). Two hu-man annotators labeled this new dataset for idiomsand literals. The inter-annotator agreement wasrelatively low (Cohen’s kappa = .58); therefore,we merged the results keeping only those entrieson which the two annotators agreed.

4.3 Word VectorsFor our experiments reported here, we obtainedword vectors using the word2vec tool (Mikolovet al., 2013a; Mikolov et al., 2013b) and thetext8 corpus. The text8 corpus has more than17 million words, which can be obtained frommattmahoney.net/dc/text8.zip. Theresulting vocabulary has 71,290 words, each ofwhich is represented by a q = 200 dimension vec-tor. Thus, this 200 dimensional vector space pro-vides a basis for our experiments.

4.4 DatasetsTable 1 describes the datasets we used to evaluatethe performance of the proposed technique. Allthese verb-noun constructions are ambiguous be-tween literal and idiomatic interpretations. Theexamples below (from the corpora we used) showhow these expressions can be used literally.BlowWhistle: we can immediately turn towards ahigh-pitched sound such as whistle being blown.The ability to accurately locate a noise · · · Lose-Head: This looks as eye-like to the predator asthe real eye and gives the prey a fifty-fifty chanceof losing its head. That was a very nice bull I shot,

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Table 2: Average precision, recall, and accuracy by each method on 12 datasets.

Method BlowWhistle LoseHead MakeScene TakeHeartPre Rec Acc Pre Rec Acc Pre Rec Acc Pre Rec Acc

tf · idf 0.23 0.75 0.42 0.27 0.21 0.49 0.41 0.13 0.33 0.65 0.02 0.11p · idf 0.29 0.82 0.60 0.49 0.27 0.48 0.82 0.48 0.53 0.90 0.43 0.44p · tf · idf 0.23 0.99 0.37 0.31 0.30 0.49 0.40 0.11 0.33 0.78 0.11 0.18CoVARFro 0.65 0.71 0.87 0.60 0.78 0.58 0.84 0.83 0.75 0.95 0.61 0.62CoVARsp 0.44 0.77 0.77 0.62 0.81 0.61 0.80 0.82 0.72 0.94 0.55 0.56CTX+ 0.17 0.56 0.40 0.55 0.52 0.46 0.78 0.0.37 0.45 0.92 0.66 0.64

BlowTop BlowTrumpet GiveSack HaveWordPre Rec Acc Pre Rec Acc Pre Rec Acc Pre Rec Acc

tf · idf 0.55 0.93 0.65 0.26 0.85 0.36 0.61 0.63 0.55 0.52 0.33 0.52p · idf 0.59 0.58 0.68 0.44 0.85 0.69 0.55 0.47 0.62 0.52 0.53 0.54p · tf · idf 0.54 0.53 0.65 0.33 0.93 0.51 0.54 0.64 0.55 0.53 0.53 0.53CoVARFro 0.81 0.87 0.86 0.45 0.94 0.70 0.63 0.88 0.72 0.58 0.49 0.58CoVARsp 0.71 0.79 0.79 0.39 0.89 0.62 0.66 0.75 0.73 0.56 0.53 0.58CTX+ 0.66 0.70 0.75 0.59 0.81 0.81 0.67 0.83 0.76 0.53 0.85 0.57

HitRoof HitWall HoldFire HoldHorsePre Rec Acc Pre Rec Acc Pre Rec Acc Pre Rec Acc

tf · idf 0.42 0.70 0.52 0.37 0.99 0.39 0.91 0.57 0.57 0.79 0.98 0.80p · idf 0.54 0.84 0.66 0.55 0.92 0.70 0.97 0.83 0.81 0.86 0.81 0.78p · tf · idf 0.41 0.98 0.45 0.39 0.97 0.43 0.95 0.89 0.85 0.84 0.97 0.86CoVARFro 0.61 0.88 0.74 0.59 0.94 0.74 0.97 0.86 0.84 0.86 0.97 0.87CoVARsp 0.54 0.85 0.66 0.50 0.95 0.64 0.96 0.87 0.84 0.77 0.85 0.73CTX+ 0.55 0.82 0.67 0.92 0.57 0.71 0.97 0.64 0.64 0.93 0.89 0.88

but I lost his head. MakeScene: · · · in which themany episodes of life were originally isolated andthere was no relationship between the parts, butat last we must make a unified scene of our wholelife. TakeHeart: · · · cutting off one of the forelegsat the shoulder so the heart can be taken out stillpumping and offered to the god on a plate. Blow-Top: Yellowstone has no large sources of water tocreate the amount of steam to blow its top as inprevious eruptions.

5 Results

Table 2 shows the average precision, recall and ac-curacy of the competing methods on 12 datasetsover 20 runs. The best performance is in bold face.The best model is identified by considering preci-sion, recall, and accuracy together for each model.We calculate accuracy by adding true positives (id-ioms) and true negatives (literals) and normalizingthe sum by the number of examples.

Interestingly, the Frobenius norm outperformsthe spectral norm. One possible explanation is thatthe spectral norm evaluates the difference when

two matrices act on the maximal variance direc-tion, while the Frobenius norm evaluates on a stan-dard basis. That is, Frobenius measures the differ-ence along all basis vectors. On the other hand,the spectral norm evaluates changes in a particulardirection. When the difference is a result of all ba-sis directions, the Frobenius norm potentially pro-vides a better measurement. The projection meth-ods (p · idf and p · tf · idf ) outperform tf · idfoverall but not as pronounced as CoVAR.CTX+ demonstrates a very competitive per-

formance. Since CTX+ is a supervised versionof CONTEXT, we expect our proposed algorithmsto outperform Fazly’s CONTEXT method.

6 Conclusions

In this paper we described an original algorithmfor automatic classification of idiomatic and lit-eral expressions. We also compared our algo-rithm against several competing idiom detectionalgorithms discussed in the literature. The per-formance results show that our algorithm gener-ally outperforms Fazly et al. (2009)’s model. Note

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that CTX+ is a supervised version of Fazly et al.(2009)’s, in that the training data here is the true“gold-standard,” while in (Fazly et al., 2009) isnoisy. A research direction is to incorporate af-fect into our model. Idioms are typically used toimply a certain evaluation or affective stance to-ward the things they denote (Nunberg et al., 1994;Sag et al., 2002). We usually do not use idioms todescribe neutral situations, such as buying ticketsor reading a book. Similarly to Peng et al. (2014)we are exploring ways to incorporate affect intoour idiom detection algorithm. Even though ourmethod was tested on verb-noun constructions, itis independent of syntactic structure and can beapplied to any idiom type. Unlike Fazly et al.(2009)’s approach, for example, our algorithm islanguage-independent and does not rely on POStaggers and syntactic parsers, which are often un-available for resource-poor languages. Our nextstep is to expand this method and use it for idiomdiscovery. The move will imply an extra step –extracting multiword expressions first and then de-termining their status as literal or idiomatic.

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