Cross-Lingual Preposition Disambiguation for Machine Translation

M. Anand Kumar; S. Rajendran; K.P. Soman

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Procedía Computer Science 54 (2015) 291 - 300

Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015)

Cross-Lingual Preposition Disambiguation for Machine Translation

M. Anand Kumar*, S. Rajendran and K. P. Soman

Center for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore 641 112, India

Abstract

This paper presents a supervised prepositional ambiguity resolution method for machine translation models in which the target language is Tamil and source language is English. We restrict our transfer ambiguity resolution problem with few prepositions only. This resolution method is based on supervised models which exploit collocation occurrences and linguistic information as features. This attempt will rectify the challenges in handling prepositions in English to Tamil automatic translation system. The preliminary results obtained from the evaluation shows that the proposed method is suitable for preposition resolution problem. © 2015 TheAuthors.PublishedbyElsevier B.V.This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibilityof organizing committee of the Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015)

Keywords: Feature extraction; Machine learning; Machine translation; Natural language processing; Preposition disambiguation.

1. Introduction

Ambiguity is the major issue in Natural Language Processing. At every level of language processing ambiguity create difficulties and it became inevitable to resolve ambiguity at these levels. To understand the natural language, an automatic system has to be developed to handle these difficulties and combine the information from various levels into a meaningful representation free from ambiguity. There are number of researches oriented towards resolving lexical ambiguity and these researches are mostly focusing on ambiguity within the language. The proposed system tries to disambiguate at the transfer level across languages that too rarely addressed preposition translation. Prepositions are one of the word classes which are both frequent and extremely ambiguous. The preposition expresses different senses based on the noun which complement it and the interpreted sense is related to the semantic role of the governing prepositional phrase. Prepositions are not given the attention they deserve in earlier studies on the resolution of ambiguity. Even in lexicographic works including dictionaries, prepositions are not elaborately discussed explicating the ambiguity they carry along with them. Prepositions are not deeply studied in the corpus analysis unlike the other parts of speech. Though prepositions are only a closed set of words exhibiting certain grammatical functions, their polysemous nature is comparable to other parts of speech. Similar to the major parts of speech like noun and verb, preposition also creates a problem in their interpretation. The interpretation of prepositions becomes a challenge to the computational community who are involved in natural language processing. They are closely related to verbs as the indicators of their internal arguments.

* Corresponding author. Tel.: +919894753437. E-mail address: m_anandkumar@cb.amrita.edu

Peer-review under responsibility of organizing committee of the Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015) doi: 10.1016/j.procs.2015.06.034

Preposition is a term used in the grammatical classification of words referring to the set of items which typically precede NP (often single nouns or pronouns) to form a single constituent of structure. Prepositions normally precede nouns or pronouns. For example,

(1) The cow is grazing in the field.

The preposition 'in' shows the relationship between cow and field. In the above sentence the object of the preposition field comes after the preposition 'in'. Hence the noun or pronoun which is used with a preposition is called its object. In the example sentence (1), the noun field is in accusative case and is governed by the preposition in. A preposition may have two or more objects as in the following sentence.

(2) The road runs over hill and plain.

It has to be noted here that prepositions can also be an adverb and that is they can be used without an object. If personal pronouns I , we, he, she, they etc are used as the object of a preposition, then their objective form me, us, him, her, them, have to be used.

Tamil makes use of postpositions instead of prepositions; these postpositions could be suffixes or free forms or a combination of both.

(3) avan mEc-ai mEl puththakathth-ai vai-thth-An

He table on book-ACC keep-PAST-3SM He kept the book on the table

(ACC = accusative case marker; PAST = past tense suffix; 3SM = third person masculine singular).

Though we can draw one-to-one correspondence between English prepositions and Tamil postpositions in several instances, there are drastic differences between them in a few instances. This paper aims at unfolding the problem of transferring or translating prepositions in English into Tamil and resolving the problem of "transfer ambiguity" using machine learning approach. As an initial attempt, we are considering only few prepositions such as in, for, with, at and on to substantiate our arguments.

2. Ambiguity in Prepositions

Lexical ambiguity is the concern of machine translation. Lexical ambiguity can be classified at least into three kinds: categorical ambiguity (eg. Chair (noun) and chair (verb) and ambiguity due to homography (plant 'living plant' and plant 'industrial plant') and polysemy (play 'play musical instrument' and play 'play games'). In machine translation, there arise ambiguities popularly called "transfer ambiguities" (or translation ambiguities) in which a single source language word can have a number of different target words or expressions. The source language word may not be ambiguous or may not be perceived by the native speakers as ambiguous; it is ambiguous only when it is translated into another language. For example, if the verb 'hear' in English is translated into Tamil as kEL, the native speakers understand it in two different ways as the verb kEL in Tamil can mean 'hear' or 'ask'. Such cases are considered transfer ambiguities. Here in this paper we are concerned only with the transfer ambiguity of prepositions.

Different prepositions of English show different types of transfer ambiguity in Tamil. Let us examine in details the transfer of prepositions such as in, with, for, at and on into Tamil. The following examples are from the corpus created for our purpose.

(4) She sleeps in tents.

avaL kUtArangkaLil uRangkukiRAL. She tents-LOC sleeps-she

(5) Write in English Angkilattil ezuthu

English-LOC write

(6) Join in their celebrations. avarkaLin vizAkkaL-il kalawthuko

They-GEN celebrations-Loc participate

(7) I studied in Jaffna. wAn jApnAy-il patiththEn

I Jaffna-LOC studied-I

(8) I was born in 1998. wAn 1998-il piRawthEn.

1 1988-LOC was-born-I

(9) I married her in difficult situation.

wAn avaLai kaStamAna cUzalil maNawthEn

I she-ACC difficult situation-LOC married-I

(10) I paid my debts in installments

wAn en katangkaL-ai tavaNaimuRaiy-il ataithEn

I debts-ACC installments-LOC remitted-I

The preposition in has -il as its postpositional equivalent (i.e. the locative case suffix) in Tamil. So here, we are not concerned about the transfer of in in-phrases into Tamil. Actually prepositions themselves are ambiguous at the source language level. One can expect 'transfer ambiguity' at the transfer level when we move from English to Tamil. Take for example, the following English and Tamil sentences with the preposition for.

(11) He boarded the train for Jaipur. avan jaipUr-ukku rayil ERinAn

He Jaipur-DAT train boarded-he

(12) I waited for you.

wAn una-kk-Aka kaththiruwthEn

I you-DAT-BEN waited-I

(13) Ram has sympathy for the poor. rAmukku EzaikaL-itam irakkam irukkiRathu.

Ram-DAT poor-LOC sympathy is

(14) He studied Tamil for one year. Avan oru ANdu tamiz patiththAn

He one year-NUL Tamil studied-he

(15) Go for fish instead of chicken. kOzikkup patilAka mInai teriwtheTu

chicken-DAT instead-of fish-ACC choose

In the sentence (11), the prepositionfor is matched to dative suffix-ukku in Tamil; in the sentence (12) for is matched against the morphologically realized element -kku-Aka (a combination of dative case suffix -kku and the postposition Aka) which give benefactive sense and in the sentence (13) for is matched with the locative postposition itam. In the sentence (14) for is matched by a NUL element (i.e. absence of an equivalent morphologically realized element). Sometimes for is used along with certain verbs forming phrasal verbs which cannot be separated out as a preposition (eg. acquainted with 'know'). This five way distinction of for in Tamil exhibit the five-way ambiguity in the source language which is reflected at the transfer level. Because of the constraint on our data we have taken into consideration

only the ambiguities of the type shown in examples (11), (12), (13) and (15). Now let us look at the translation of sentences with the preposition with.

(16) Blend water with milk.

taNNIr-utan pAlaik kal

Water-with milk-ACC mix

(17) We walk with legs.

wAm kAlkaL-Al watakkinROm

we leg-PL-with walk-we

(18) He apologized with her.

avan avaL-itam mannippu kEttAn

He she-LOC pardon asked-he

(19) This is an ancient lake with a temple of Lord Mahadev near it.

itu makAthEv katavuLin kOvil-ai atan arukil koNta oru purAthana Eri Akum

this Mahadev lord-POS temple-ACC it's near have one old lake

(20) He is acquainted with difficulties

avan kaStangkaL-utan pazakkappattuvittAn

The difficulties-with acquainted-he

In the sentence (16), with is matched against the associative postposition utan and in the sentence (17), with is matched with the instrumental case suffix -Al 'by'; in the sentence (18), the preposition with is matched with the locative postposition itam in Tamil; in the sentence (19) with is matched with uLLa 'having'. Sometimes with is used along with certain verbs forming phrasal verbs which cannot be separated out as a preposition (eg. go for 'choose'). This four way distinction of "with" in Tamil exhibit five-way ambiguity at the source language which is reflected at the transfer level. But, because of the constraint on our data we have taken into consideration only the ambiguities of the type shown in examples (16) and (17).

Now let us look at the translation of sentences with preposition at.

(21) The stars shine at night.

watcaththirangkaL irav-il pirakAcikkum

stars night-LOC shine-PRES

(22) He is standing at the railway station

Avan thotarvaNti wilaiyaththil wiRkiRAn

He railway station-LOC stands-he

(23) She woke up at six O' clock.

AvaL ARu maNi-kku ezuwthAL

She six hour-DAT woke up-she

(24) I am getting angry at you.

enakku unn-itam kOpam varukinRath

I-DAT you-with angry come

(25) He was shocked at the news.

avan ceythiy-Al atircciyataiwthAn

He news-INST shocked-he

In the sentence (21) and (22), the preposition at is mapped against the locative case suffix -il in Tamil; in sentence (23), at is mapped against Tamil dative case suffix - kku; in sentence (24), at is mapped against Tamil receiver-marker itam; in sentence (25), at is mapped against the instrumental-case marker Al; This seven way distinction of at in

Table 1. An example English sentences with tags.

An example of English sentences Preposition tag

Dussehra is celebrated for ten days. NU

I have played outside fo an hour. ku-Aka

She does not bring water fo me. ku

I had headache fo two or three days. Aka

His body is covered with hair. Al

Peter has fallen out with his boss. utan

I have lived with my parents for over 10 years. utan

She did not come with me. utan

Table 2. Possible tamil postpositions.

English prepositions Possible tamil postpositional equivalents

fo ku,k-Aka, NUL, Aka, Phrase, ku-Ana

at LOC, ku, Phrase.itam,Al

with utan,Al,itam, koNta

on mEl, il, Phrase,ku, paRRi

Tamil exhibit seven-ways ambiguity at the source language which is reflected at the transfer level. But Because of the constraint on our data we have taken into consideration only the ambiguities of the type shown in examples (21) and (23).

We can generalize certain fact by looking at the corpus. Tamil does not distinguish between in and at and mostly replace them with locative - il. We can infer that locative sense is always mapped against locative - il in Tamil. The temporal elements such as, hour governed by at in English2 is governed by dative ku which in directional in Tamil. As 'hour' is a dynamic entity, Tamil prefers directional-dative ku instead of locative il. 'Angry' also is assumed as directed to a person rather than located to a person in Tamil; so the person to whom anger is directed is marked for the receiver maker itam in Tamil. In the case of verbs such as shock the relation between the agent and the sufferer is marked by the preposition at in English; Tamil considers it as a causer relation and realizes the relation by instrumental case marker- Al. Not all occurrence of at is a preposition. It can be a unit of prepositional-verbs such as look at or an idiom or multiword expression.

Example English sentences with Tamil postposition tag are shown in the Table 1. Table 2 illustrates the possible Tamil equivalents for English prepositions. We are taken into consideration only most frequently occurring equivalents in the training corpus. Here, the annotation 'Phrase' covers up prepositional verbs, multi-words and idioms.

3. Related Works

A number of researchers including Alam1, Harabagiu8, O'Hara and Wiebe17, Sopena23, Loberas and Moliner, Sablayrolles24, Saint-Dizier22 and Vazquez, Litkowski15, and Boonthum4, Toida and Levinstein have recently studied disambiguation of the preposition. Alam1 studied the disambiguation of the preposition 'over'. Harabagiu8 made use of WordNet to disambiguate prepositional phrase attachment. A special issue of Computational linguistics (Baldwin et al.)2 was devoted to discuss about the issues on preposition. Preposition sense disambiguation was one of the SemEval 2007 tasks (Litkowski and Hargraves)15, and was explored in a number of papers using supervised approaches. O'Hara and Wiebe19 presented a semantic roles based supervised preposition sense disambiguation. Tratz and Hovy25, and Hovy9 et al. make explicit use of the arguments for preposition sense disambiguation. Rudzicz21 and Mokhov and 'Hara and Wiebe17 have studied the constraints of prepositional constructions to annotate the semantic role of complete prepositional phrases. The present study is much rarer of the kind of studies mentioned above as it aims to resolve the prepositional ambiguity at the transfer level. Sudip and Sivaji16 presented the study for handling of preposition in English-Bengali Machine Translation system. Husain10 et al. proposed an explicit model of preposition correspondence on the basis for preposition selection in English to Indian language machine translation. Parameswarappa20 et al. introduced a scalable algorithm to disambiguate sense of the preposition during English to Kannada Machine Translation. Jayan11 et al. proposed a rule based method for disambiguating the prepositions in English-Malayalam MT system.

Fig. 1. Framework for English preposition disambiguation.

<wf cmd=are lemma=be |«jc=V pos=VBP deplab=cop hword=you hpos=PRP hlab= <wf cmd=you leroma=you wc=PN pos=ERP deplab=ROOT hwocd=ElOOT hpoa=r hi a

<wi emd=Myfchili lemma=MyChili we=H pos=NWP deplab=n3ubj hword=expeeti <wf cmd=has lemma=have wc=V pos=VBZ deplab=aux hword=expecting hpos=V <wf cmd=been lenune=be wc=V pos=VBN deplab=aux hword=expecting hpos=VB' <wf cmd=expecting lemma=expect wc=V poa=VB«3 d epl afo=P.OOT hword=ROOT hp <-wf cmd=£or l«ntn»=for w<:=P p«>s=IN dtplib=pïep hwoid^xp&cting hpos=VB' <wi erad=three leirnna=,three we=NUM pos=CD deplab=num hword=months hpos— <wf cmd=motiths leraifta=month wc=W pos=NNS deplab=pobj hword=for hpos=IN ; <pur»c>. </punc>

Fig. 2. Snippet of features extracted from sentences.

4. Methodology

Figure 1, illustrates the frame work for English preposition disambiguation system. English sentences with the prepositions "for", "with", "at" and "on" have been collected from the EILMT Tourism-2 parallel corpus. These sentences are manually tagged with equivalent postpositions in Tamil. The essential linguistic information for each English sentence is extracted using the freely available (Dan Klein and Christopher D. Manning, 2003)6 Stanford parser toolkit. This linguistic information such as lemma, POS tag, and dependency tag is used as features in machine learning based disambiguation model. Additionally we used English Wordnet G. Miller, 1990)7 for extracting the Hypernym and Lexicographer file information. The snippet of the features extracted data is shown in Fig. 2. For training the model we used SVM-Light, a public distribution of SVM (Support Vector Machines) by (Joachims, 1999)12. We applied SVM linear kernel with 10-fold cross validation on training data which assist to choose the values of the regularization parameter 1 individually for each tag.

By using this method, the prepositions of English are disambiguated and corresponding Tamil postpositions are identified. This aids to precisely translate the prepositional phrase and noun phrases in the English sentence. This model can easily plug in to the rule based or statistical machine translation system in order to improve their performance.

4.1 Feature set used in disambiguation

It is more significant to find out relevant features when using machine learning models for language processing applications. Choosing the right input features for a machine learning algorithm is one of the deciding factors for a successful model development. The features used for cross-lingual prepositions disambiguation are divided into three categories. These are collocation features, dependency features and Wordnet Features.

Local Collocation Features: Surrounding words and POS tags are considered with the words around the target preposition. These features are combinations of word form and its corresponding Lemma and POS tag surrounding the target preposition within a window of 5 words.

Table 3. Distribution of prepositions in sentences.

Prepositions for at with on

ku 253 42 - -

ku-Aka 211 - - -

Aka 28 - - -

il - 362 - 186

utan - - 248 -

Al - - 174 -

Phrase 26 38 - 91

mEl - - - 48

NULL 35 - - -

TOTAL 554 442 423 325

Table 4. Accuracies of collocation and dependency features.

Collocation (C) Dependency(D) Collocation +Dependency (CD)

P R F Score P R F Score P R F Score

for_ku 0.723 0.483 0.579 0.789 0.49 0.605 0.744 0.483 0.585

for_ku-Aka 0.639 0.288 0.397 0.568 0.251 0.348 0.622 0.344 0.443

for_NUL 1.000 0.229 0.372 1 0.286 0.444 1 0.286 0.444

with_utan 0.633 0.578 0.604 0.627 0.55 0.586 0.652 0.606 0.628

with_Al 0.614 0.242 0.347 0.778 0.039 0.075 0.615 0.18 0.278

at_Phrase 0.684 0.351 0.464 0 0 0 0.667 0.162 0.261

at_il 0.829 0.971 0.894 0.812 0.976 0.886 0.841 0.973 0.902

at_ku 0.739 0.386 0.507 0.792 0.432 0.559 0.75 0.409 0.529

on_il 0.632 0.77 0.694 0.628 0.695 0.66 0.642 0.775 0.702

on_Phrase 0.833 0.376 0.519 0.84 0.452 0.587 0.854 0.441 0.582

Average 0.7326 0.4674 0.5377 0.6834 0.4171 0.475 0.7387 0.4659 0.5354

Dependency Features: Dependency features are extracted to capture the textual relations between the words and the target preposition. Dependency label, governor, governor POS tag, governor dependency label and dependent word features are extracted within a window of 5 words.

Wordnet Features: WordNet is used in order to collect the hypernyms and Lexicographer file information of governor and dependent within a window of 5 words. Wordnet synsets are structured into forty-five lexicographer files based on syntactic category and logical grouping. The names of the lexicographer files are of the form: pos.suffix, where pos is either noun, verb, adj or adv. suffix is used to organize groups of synsets into different files, for example noun.animal is the lexicographer file name for the word cat and dog.

5. Experiments and Results

The annotated dataset consists of totally 1743 sentences and the distribution with respect to the number of sentences for each preposition and their Tamil postpositions (aka. preposition tag) are revealed in Table 3. The preposition tag contains the Tamil postposition information. To disambiguate a preposition p, in the sentence given S, this proposed system uses the collocations and linguistic information as features. Experiments were conducted with different feature combinations and evaluated using well known scores, precision and recall.

Table 4 and 5 shows the precision, recall and F measure values for with and without Wordnet features. From the result we can easily infer that collocation and wordnet plays a major role in preposition disambiguation. Apart

Table 5.

M. Anand Kumar et al. / Procedía Computer Science 54 (2015) 291 Accuracies of collocation, dependency and wordnet features.

Dependency+Wordnet (DW) Collocation +Wordnet (CW) Colloc+Depen+Wordnet (CDW)

P R FScore P R F Score P R F Score

for_ku 0.780 0.521 0.625 0.708 0.533 0.608 0.759 0.510 0.610

for_ku-Aka 0.579 0.326 0.417 0.602 0.358 0.449 0.62 0.409 0.493

for_NUL 0.909 0.286 0.435 0.909 0.286 0.435 0.909 0.286 0.435

with_utan 0.679 0.741 0.709 0.721 0.781 0.750 0.699 0.757 0.727

with_Al 0.654 0.393 0.491 0.703 0.466 0.561 0.710 0.427 0.533

at_Phrase 0.600 0.162 0.255 0.625 0.270 0.377 0.625 0.135 0.222

at_il 0.821 0.965 0.887 0.839 0.960 0.896 0.838 0.965 0.897

at_ku 0.765 0.295 0.426 0.708 0.386 0.500 0.750 0.409 0.529

on_il 0.623 0.663 0.642 0.659 0.743 0.698 0.624 0.711 0.665

on_Phrase 0.848 0.419 0.561 0.826 0.409 0.547 0.854 0.441 0.582

Average 0.7258 0.4771 0.5448 0.730 0.5192 0.5821 0.7388 0.505 0.5693

Fig. 3. F-Scores for various feature combination.

Table 6. An average F scores for prepositions.

Prepositions C D C&D D & W C&W CDW

for 0.450 0.466 0.490 0.492 0.497 0.513

with 0.480 0.331 0.453 0.600 0.656 0.630

at 0.622 0.482 0.564 0.523 0.591 0.549

on 0.610 0.624 0.642 0.602 0.623 0.624

from the typical hypernym feature, here we used Lexicographer file information as a feature to disambiguate the target preposition. In addition to that target preposition's dependent child has a key place in disambiguation process.

Figure 3 shows the average F-Scores for various feature combinations. This clearly infers the importance of Wordnet in disambiguation process. Compare to the first three feature combinations (without wordnet) the last three combinations (with wordnet) had positive influence in F measure. Particularly, for the preposition "with" the accuracy is improved suddenly when the the wordnet feature is used. Table 6 shows the average F-Scores for feature combinations. Finally the feature set "Collocation +Wordnet" shows the promising results in the experiments compared with others.

It is also unsatisfactory to see that this proposed method is totally failed to disambiguate few preposition tags (for-Aka, for- phrase and on-mEl). However, this is because of the sentence size which we have used in our experiments.

The overall finding is that, similar to verbs and nouns the preposition is also disambiguated by its context information. Our investigation revealed that machine learning method can be suitable for solving the ambiguity in preposition and selecting the equivalent in target language using existing knowledge sources as features. This improved the performance of preposition translation. This initial attempt has lot of scope to further improvement in future. Data size can be improved and feature set can be further tuned with an efficient use of English WordNet and the Semantic rol's of verb in the sentences.

6. Conclusion

We have inferred from the mapping of English prepositions with Tamil postpositions that the head of the ad-positions (preposition and postposition) and the word occurring immediate to the left decide the interpretation of the ad-positions. While disambiguating prepositions the maximal accuracy can be achieved by considering the context, features, and granularity. In language processing research preposition disambiguation plays a vital role especially in Machine Translation across languages. Rule based machine translation performance can be improved by implementing preposition disambiguation at transfer level. Though the preliminary result is encouraging, various issues still need to be addressed, i.e. increasing the coverage of prepositions and improving the result by using world knowledge. The multi-words with preposition and phrasal verbs are further challenges to the enhancement of the proposed model. The proposed system can be easily converted to generic framework for handling prepositions in English to Indian language translation system and translation among Indian languages. The experimental results shows that Wordnet with dependency information features of the source sentence is helpful for disambiguate or translate the prepositions with accuracy.

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