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Procedia - Social and Behavioral Sciences 95 (2013) 431 – 441

5th International Conference on Corpus Linguistics (CILC2013)

The OPT-ional Phenomenon in Singapore English: a Corpus-based
Approach Using Time Annotated Corpora
Eric YongMing Lai, Liling Tan, Vincent Wong, Lenny Teng Tao Loke, Francis Bond*
Division of Linguistics and Multilingual Studies, Nanyang Technological University, 14 Nanyang Drive, Singapore 637332

Abstract
The Optional Omission of Past Tense (OPT) is prevalent in the colloquial register of Singapore English (SCE). This paper
describes the investigation of the OPT phenomenon based on time annotated corpora. The Singapore and Hong Kong version of
the International Corpus of English were extended with time annotation for this study. In Singapore English sentences that
contain the perfective aspectual adverbs of already or yesterday, the OPT-ional phenomenon is found to be present 68.2% of thetime. Although this phenomenon is also found in Hong Kong English, it is significantly more prominent in Singapore English(p<0.05, z=6.27). In SCE, there are also syntactic constraints that influence the OPT occurrences, where the omission of pasttense occurs 39.8% more frequently in sentences with pre-verbal adverbs than post-verbal adverbs.
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Keywords: world Englishes; Singlish; Singapore English; Hong Kong English; corpus; annotation; tenses

1. Introduction
Singapore Standard English (SSE) and Singapore Colloquial English (SCE) are widely accepted as the two main
registers of spoken English in Singapore (Platt, 1977; Gupta, 1994; Boa and Hong, 2006). Although SSE is theofficially prescribed variety in the country, SCE is the predominant variety of day-to-day interactions (Cavallaro andNg, 2009). With regard to intelligibility, SSE is not significantly different from the globalized varieties of English(i.e. British English and American English) (Gupta, 2005, Kirkpatrick and Saunders, 2005). SCE, however, has beenrichly influenced by substratum languages (Deterding and Poedjosoedarmo, 1998; Wee, 2003; Lim, 2007) and the* Corresponding author. Tel.: +65-6592-1568 ; fax.: +65-6794-6303
E-mail address: bond@ieee.org

1877-0428 © 2013 The Authors. Published by Elsevier Ltd. Open access under CC BY-NC-ND license.
Selection and peer-review under responsibility of CILC2013.
doi:10.1016/j.sbspro.2013.10.666

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resultant variety has undergone considerable phonological, morphological, and syntactic restructuring. This study
investigates one of these restructured features in SCE viz. the Optional Omission of Past Tense (OPT) in SCE.
In literature on SCE, Crewe (1977) was among the first to observe non-standard tense marking. This was further
discussed by Ho and Platt (1993) where the omission of the ‘-s’ suffix in third-person singular present tense (e.g. ‘heeat cheese’) was observed. Subsequently, Alsagoff and Ho (1998) noted an overgeneralization of the ‘-s’ suffix inthe first person present tense (e.g. ‘I eats cheese’). Brown (2000) stated that in SCE “present tense does notnecessarily mean the present time... It can also be used, in certain circumstances, to refer to the past and the future”(c.f. Sheng, 2007). Gut (2009) recognized this occasional lack of verbal past tense (e.g. ‘I eat cheese just now’) in
SCE and suggested a shift in the function of present tense marking. In addition, Bao (1995) claimed that already in
SCE marks the inchoative (1a) or perfective (1b) aspect and that the presence of already in a sentence allows SCE
speakers to use a present tense verb to convey the past.
(1) My baby speak already. (c.f. Bao, 1995)
a. 'My baby has started to speak'b. 'My baby has spoken'
The OPT phenomenon is a recurrent theme in studies on SCE (e.g. Alsagoff (2001), Deterding (2003), Ho(2003), Wee (2004), Fong (2004), Gut (2009)) that was investigated using word frequency and the rate of past tense
marking. This study offers an alternative corpus extension approach by adding temporal annotations using theTimeML standards (Pustejovsky et al, 2003).
The OPT seems to be motivated by the presence of time-adverbials such as yesterday in an utterance (Bao 1998;
Alsagoff 2001). We extend the notion of OPT to include the omission of past participle tense in the presence ofcompletive-adverbial already. For example, from ICE-SIN S1A:051:127:1:B,(2) *So they have ask for extension five times already.
This study hypothesizes a correlation between verbal tense and aspectual adverbs in SCE where the OPT is
substantially instigated by the use of perfective adverbs in SCE sentences. Focusing on two particular perfective
aspectual adverbs (already and yesterday) this study seeks to answer the following questions regarding OPT in SCE
using a corpus based approach:i. How often does the OPT phenomenon occur in SCE?
ii. Is the OPT phenomenon unique to SCE?
iii. Are there syntactic constraints governing the occurrence of OPT in SCE?
Other than the syntactic/lexemic constraints of the OPT phenomenon, previous researches had also looked at thephonemic motivations in final plosive deletions due to consonant clusters coda (e.g. Deterding and Poedjosoedarmo(1998), Bao (1998), Lim (2004), Gut (2005)). However phonemic constraints for OPT will not be discussed in thisstudy.
2. Corpus Based Approach
In the process of investigating the OPT phenomenon in SCE, existing corpora were extended with temporalannotations. To explore the nature of OPT occurrences, this study utilizes time tagged corpora, where the corpora
contain a layer of time annotation that describes verbs and adverbs as events that embed information about the tenseof a particular intention in each sentence. An OPT occurrence is identified as a sentence that is headed by a present
tense marked verb and a perfective adverb conveying the past.
This paper describes the process of the corpora extension and concludes with the investigation of the OPT
phenomenon in SCE. This paper is organized as follows; Section 3 is concerned with the choice of two corpora, one
being the representation of SCE and the other as a cross English variety comparison. Section 4 describes the corpora
extension task, which includes the removal of the original annotation and manual tagging of the corpora with

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temporal tags. Section 5 discusses the OPT phenomenon with the results extracted from the time-tagged corpora.
This paper concludes with Section 6, discussing future explorations of the OPT phenomenon.
3. Choice of Corpora
The chosen corpus to investigate the OPT phenomenon in SCE is the spoken section of the International Corpus
of English – Singapore (ICE-SIN). The spoken section (~600,000 words) of the ICE-SIN emulates the colloquial
register (SCE) of Singaporeans’ day-to-day conversations; e.g. the usage of adverbs immediately after verbs, such as“finish already” (see Fig. 1). More specifically, the private dialogues stratum (~200,000 words, which includes faceto-face conversations and phone calls) within the spoken section was chosen as the data for temporal annotation
extensions. Public dialogues (e.g. classroom lessons and business transactions) and monologues (e.g. legal
presentations and spontaneous commentaries) were excluded as they constitute a relatively more formal speechsetting, and speakers would have likely used the standard register (SSE) where OPT is theoretically absent as it isviewed as ungrammatical in SSE.
The written section (~400,000 words) of the corpus were also excluded in the annotation task as it representededited English texts (e.g. academic writing, student writing and news reports) where the OPT realizations might
have been sanitized (for detailed description of the ICE corpus design refer to Nelson, 1996a).
3.1. ICE-SIN corpus Representation
The ICE-SIN corpus is reasonably large in size, (~1,000,000 words) and it adopted the stratified sampling
method (McEnery and Wilson, 2001) as its corpus design. Invariably, the stratified corpus lacks the proportional
representation of the Singapore population as a whole. Nonetheless, the stratified sampling is sufficiently effective
in representing the language use of SCE. The corpus inputs were from both male and female informants across awide range of age groups, aged 18 and above. In addition, “all contributors to the corpus were educated through themedium of English and were either born in Singapore or have moved here at an early age and received education
through English in Singapore” (Nelson, 1996: 35-53).
Short of building a similar and updated version of the spoken section of the ICE-SIN, extending the current ICESIN with temporal annotations is the most resource effective way to explore the OPT phenomenon.
3.2. ICE-HK as a comparable corpus
To verify the uniqueness of the OPT phenomenon, the International Corpus of English – Hong Kong (ICE-HK)
was selected as the corpus for cross-corpora evaluation. The ICE corpora were built with the primary aim ofestablishing contrastive linguistics across different varieties of English worldwide. Every variety of English in the
ICE corpus was compiled using the same corpus design (Leitner, 1992).
Moreover, Singapore and Hong Kong share similar historical backgrounds – both were former British colonies,
with a Chinese-majority population where substrate influences from their Chinese 'dialects' were observed (Tan,
1997). The substratum characteristics of both Singapore and Hong Kong English (HKE) would have presented
similar occurrences of the OPT phenomenon, where sociolinguistics justifications motivate these grammaticaloccurrences.
3.3. Availability of ICE-SIN
Other than the representation of the corpus, the availability of the corpora was also taken into consideration in theselection of the corpora to explore the OPT phenomenon. All ICE corpora are free to be downloaded and theirusages are governed by the individual ICE license agreement crafted by the individual project heads of therespective Englishes. The ICE corpora chosen for this study can only be used for non-profit linguistic research
purposes and the redistribution of the ICE texts is prohibited. Although the prohibition to redistribute the annotatedtext may limit the potential usage of this corpus extension effort, this data could be used to train an automatic

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temporal-event tagger to tag future English speech data with the OPT phenomenon. An algorithm can be devised totime-tag SCE data showing the OPT phenomenon, which would provide an ideal model for a tagger to train on.
The time annotated data will be sent to the respective contact person for the ICE-SIN and ICE-HK corpora andthe hosting of the extended corpora will be at the project teams’ discretion. Permission would be sought toredistribute a sample of the time tagged data if the project teams reject the full redistribution of the temporalannotations.
4. Corpora Extensions
The objective of the annotation extension task was to provide more conclusive corpus-based results of when andhow the OPT phenomenon sentences with perfective adverbs (already and yesterday) were filtered out for the taskof temporal tagging, to reduce unnecessary annotations by human. The resulting outputs of the corpora extensions
are stored in Time Markup Language (TimeML) format; TimeML is used in the temporal time annotation evaluation
task (TempEval) of the computational semantic evaluation exercise SemEval (Pustejovsky et al, 2003; SemEval,n.d.). TimeML abides by the Extensible Markup Language (XML) format where the syntax of the ML can bechecked for its well-formedness.
4.1. Cleaning up the original ICE annotations
The original ICE annotations were marked with extra-segmental annotations at the discoursal level and other
English-related annotations. An example of the original tags is as follow (refer to the ICE corpus manual for the full
ICE annotations guide; Nelson, 1996):
Table 1. Example of ICE original annotations.
Original ICE tags<$[A-Z]><#><[>…</[><,>and<,,><foreign>…</foreign><unclear>…</unclear>

Annotation description
Speaker identification
Text unit markers
Overlapping turns
Short and long pauses
Foreign word
Unclear speech

As the OPT phenomenon occurs at the segmental level (i.e. the lexical, clausal and sentential level), all theoriginal discoursal markups and English related tags were removed as they were of no value to the time tagged
corpora and the OPT investigation. Future research may look into whether discoursal data will affect the temporalutterances. It must be noted that similarities between the original ICE tags and XML will result in loud technical‘noises’ when processing the time-tagged data.
As for socially determined factors such as gender, age and education, the ICE-SIN has no metadata encoded in
the text. However, the domain based strata that the corpus design is based on can be coded in the textfile name inaccordance to how the individual sentences were tagged (e.g. in the text unit tag <ICE-SINS1A-049#83:1:C>,
S1A refers to the Private Dialogue data and 049 points back to the Direct Conversations section of the corpus).
A text unit corresponds loosely to a sentence, though it may be syntactically incomplete. In the spoken sector of
the ICE corpora, a change of speaker’s turn always corresponds to a new text unit (Nelson, 1996). A python script
was written to clean the original annotation and extract the text units with the target perfective adverbs already andyesterday. Sentences with <unclear>…</unclear> tags and mono-word sentences were excluded from the tagging
task because even if the OPT phenomenon is present; they remain inconclusive.
4.2. Time tagging the ICE corpora
The time tagging of the ICE corpora adhered to the TimeML standards and to investigate the OPT phenomenon,
only three basic tag types from the TimeML were used, viz. <EVENT>, <MAKEINSTANCE> and <TLINK> tags.

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Although TimeML offered a variety of other tags that will produce finer-grained definition of temporal ordering,
past linguistic researches on time relations had shown that it is more appropriate to capture time relations in natural
language using a reduced set of temporal tags (Schilder, 1997; Freska, 1992). The reduced number of tags would
also be preferred as it increases the reliability of the annotation by both computer and human (Verhagen, 2005).
Table 2. Reduced TimeML Annotation Schema.
Tags

Attribute

Values

EVENT

class

'ASPECTUAL' | 'OCCURRENCE' | 'STATE'

MAKEINSTANCE

pos'ADJECTIVE' | 'VERB'

TLINK

tense

'PAST' | 'PRESENT' | 'NONE'

aspect'PROGRESSIVE' | 'PERFECTIVE' | 'PERFECTIVE_PROGRESSIVE |'NONE'

relType'BEGINS' | 'DURING' | 'END'

The <EVENT> tags were used to annotate words in the sentence that mark semantic events. The tag contained theclass attribute that determined the nature of the semantic events that were being tagged. The <EVENT> tags wereapplied on (i) the head verb of each sentence and (ii) the already or yesterday adverb.
For every <EVENT> tag there was a corresponding <MAKEINSTANCCILCE> tag that encoded the tense,
aspect and its pos (part of speech). The pos attribute in <MAKEINSTANCE> was a coarse grained tag which
only differentiate the main word classes (i.e. “VERB” or “ADVERB”).
Table 3. Annotation of the tense and aspect attributes adheres to the following paradigm.
Active Voice

Passive Voice

Tense

Aspectteach

is taught

PRESENTNONE

is teaching

is being taught

PRESENTPROGRESSIVE

has taught

has been taught

PRESENTPERFECTIVE

has been teaching-

PRESENTPREFECTIVE_PROGRESSIVE

taught

was taught

PAST

NONE

was teaching

was being taught

PAST

PROGRESSIVE

had taught

has been taught

PAST

PERFECTIVE

had been teaching

-

PAST

PREFECTIVE_PROGRESSIVE

For every pair of head verb and adverb <EVENT> tags, the annotators added a <TLINK> tag that encoded thetemporal relation type between the <EVENT> tags. The <TLINK> tag had the relation type (relType) attribute thatspecify whether an event had begun (“BEGINS”), had ended (“ENDS”) or the event was ongoing (“DURING”) at
the time when the sentence was recorded.
Figure 1 shows an example of the original source textfile S1A-049.txt from the ICE-SIN and the resultant time
annotated output in TimeML format. From sentence ICE-SIN:S1A-049#83:1:C, the head verb Finish was taggedwith the <EVENT> tag and for its <MAKEINSTANCE> the annotator respectively assigned PRESENT and NONE
to its tense and aspect attribute. For the adverb already, it was tagged with a second <EVENT> tag and theannotator assigned PAST and PERFECTIVE to its tense and aspect attribute. Finally the annotator linked the eventswith the <TLINK> tag and assigned the ENDs value to the relType attribute to specify that the event when thesentence was recorded. With such annotations we identified the OPT phenomenon by looking at the tense and aspectdisparity between the <EVENT> tags.

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314
315
316

317
318
319
320
321
322
323
324
325
326
327
328
329

<$B>
<ICE-SIN:S1A-049#81:1:B>
Ya because now it is the midyear exam so I have to markquite
a lot of papers<$A><ICE-SIN:S1A-049#82:1:A>
How many classes you teaching<$C><ICE-SIN:S1A-049#83:1:C>
Finish already

1
2
3
4
5
6
7
8
9
10
11

12

<$B>
<ICE-SIN:S1A-049#84:1:B>
I have four classes

13<?xml version=”1.0”?><!DOCTYPE TimeML SYSTEM “TimeML.dtd”><TimeML><EVENT eid=”e1” class=”OCCURRENCE”>
Finish</EVENT><MAKEINSTANCE eiid=”ei1” eventID=”e1”
pos=”VERB” tense=”PRESENT” aspect=”NONE”/><EVENT eid=”e2” class=”ASPECTUAL”>
already</EVENT><MAKEINSTANCE eiid=”ei2” eventID=”e2”
pos=”ADVERB” tense=”PAST”
aspect=”PERFECTIVE”/><TLINK eventInstanceID=”ei1”
relatedToEventInstance=”ei2”
relTypes=”ENDS”/></TimeML>

Fig. 1. Sample text from S1A-049.txt (left); Time tagged sample of sentence ICE-SIN:S1A-049#83:1:C (right)

The original selected stratum of the ICE-SIN and ICE-HK contain 30,117 and 63,075 text units respectively. The
corpus design ensures that the number of words is ~200,000 from the private dialogue stratum. The difference in thenumber of text units meant that the ICE-HK text units are on average two times shorter than those in the ICE-SIN.
After cleaning the original ICE annotation and removing mono-word and <unclear> text units, the extracted
counts of ICE-SIN and ICE-HK text units are 21,401 and 38,910 respectively. The drop from 63,075 to 38,910
means that approximately half of the ICE-HK data are made up of mono-word sentences or unclear content,
reflecting a relatively poorer quality of the corpus content and speech recordings.
The remaining cleaned and extracted text units that contained the selected perfective adverbs already andyesterday were manually time annotated. The manual annotations resulted in 286 meaningful occurrences of the
OPT phenomenon in the extracted ICE-SIN texts and 187 such occurrences in the ICE-HK texts.
4.3. Inter-Annotation Agreement (IAA) of the time annotations
A subset of four time-tagged textfiles was annotated by all four annotators (two were randomly selected from the
ICE-SIN and ICE-HK respectively). Due to the involvement of more than two annotators, the common double
annotation IAA calculation was inappropriate and the traditional Cohen kappa contingency table would have overgeneralized the fine differences between the annotators’ annotations. Also, the IAA standards set by the TimeBank
1.2 were based on the IAA of two annotators (Pustejovsky et al, 2003). Since the annotation task in this study
involved 4 raters, the kappa coefficient was used as a gauge for the IAA score across all four annotators.
The kappa coefficient calculations were based on Warren’s (2010) percentage of overall agreement of the fixedmarginal multirater kappa (Siegel and Castellan, 1988) and the free-marginal multirater kappa (Randolph, 2005).
The value of the percentage of overall agreement (Po) kappa ranges from -1.0 to 1.0, with kappa scores of:-1.0 indicating the perfectly true negatives (i.e. perfect disagreement between raters is below chance),
0.0 indicating the annotators' agreement is equal with chance and,
1.0 indicating perfectly true positives (i.e. the perfect agreement of all raters is above chance).
Following the TimeBank 1.2 corpus standards for temporal annotation IAA, the Po kappa was calculated for eachtype of TimeML tags (i.e. the <EVENT>, <MAKEINSTANCE> and <TLINK> tags) involved in the annotation
extension task. The multirater Po kappa calculations require the number of cases and the number of possible
categories. For example, when considering the IAA of the <EVENT> tag, the number of cases refer to the number of<EVENT> tags that the annotators are required to tag; for each textfile there are two <EVENT> tags within asentence – referring to the verb and adverb respectively. The category number referred to the number of possible

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differences, e.g. an annotator can tag an <EVENT> tag with any of the 3 categories; class=“STATE” orclass=“ASPECTUAL” or class=“OCCURRENCE”. Table 4 summarized the IAA calculation criterion. Since
the <MAKEINSTANCE> tags had two attributes that were crucial in the OPT analysis, the IAA calculation of the<MAKEINSTANCE> tag is based on the tense and aspect attributes rather than <MAKEINSTANCE> tag as awhole.
Table 4. IAA Calculation Criterion.
Tag Types<EVENT>
tense
aspect<TLINK>

No. of cases
33
33Categories of tag types
class::= “STATE”|“OCCURRENCE”|“ASPECTUAL”
tense::= “PAST”|“PRESENT”|“NONE”
aspect ::= “PERFECTIVE”|“PROGRESSIVE”|“NONE”
relType::= “BEGINS”|“ENDS”|“DURING”

The IAA scores for the time annotation task were above the widely accepted inter-rater agreement threshold, i.e.
>0.70 (Warrens, 2010). The fixed marginal score on the <TLINK> tags were extremely low because there was one
sentence instance with omitted past tense that can be of perfective or inchoative intention and the annotators differ intheir tagging (2 annotators tagged the <TLINK> with the ENDS attribute, 1 annotator tagged it as BEGINS and thelast annotator tagged it as DURING). The 0.08 score on the fixed marginal kappa measured how the extent of theannotators’ variation but the effect was counter-balanced by the free marginal kappa that weighs their similarity.
Table 5. IAA of the Time Annotation Task.
Tag Types<EVENT>
tense
aspect<TLINK>

Overall Po
0.75
0.81
0.85
0.79

Fixed Marginal
0.57
0.43
0.70
0.08

Free Marginal
0.63
0.77
0.78
0.69

5. The OPT-ional Phenomenon
To differentiate the sentences displaying the OPT phenomenon from other sentences in the time-annotatedcorpora, the tense disparity between a verb <EVENT> and an adverb <EVENT> was considered. A verb would beannotated with the <EVENT> tag and assigned either the STATE or OCCURRENCE value for its class attribute.
The class attribute for an adverb <EVENT> tag would be assigned the ASPECTUAL value. Since all the<MAKEINSTANCE> tags for the perfective adverbs contained the PAST tense attribute, we identified an OPT
occurrence when the verbal <MAKEINSTANCE> tag contained the PRESENT tense attribute. The contingency
table to define sentences with and without the occurrence of the OPT phenomenon is summarized below (see Table
6). Although the <TLINK> tags were not utilized by this study, the relType=“BEGINS” andrelType=“ENDS” attributes can be used to explore the incohative and perfective aspects of already as Bao(1995) had observed.
Table 6. Contingency Table for OPT Phenomenon
EVENT (verb)

EVENT (adverb)

MAKEINSTANCE(verb)

MAKEINSTANCE(adverb)

OPT phenomenonclass::=“state”|“occurrence”

class::=“aspectual”

tense::=“PRESENT”

tense::= “PAST”

OPT present

class::=“state”|“occurrence”

class::=“aspectual”

tense::= “PAST”

tense::= “PAST”

OPT absent

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5.1. How often does the OPT phenomenon occur in SCE?
Table 7 presents the likelihood of OPT occurrences from the time annotated sentences; V-RB refers to thesentences which contain post-verbal adverbs and RB-V refers to the sentences which contain pre-verbal adverbs.
The OPT phenomenon manifested 68.18% of the time when an already or yesterday adverb was present in thesentence in the ICE-SIN.
Table 7: OPT Occurrences in sentences containing already and yesterday in the ICE-SIN
Syntactic Structure
V-RB

OPT present
30

OPT absent
56

Total no. of sentences
86

RB-V

165

35

200

Total occurrences

195 (68.18%)

91 (31.89%)286

To verify whether the OPT phenomenon occurred in sentences without perfective adverbs, we extracted arandom subset of the 50 untagged sentences from the spoken private dialogue stratum of the ICE-SIN to check
whether OPT occurred in the absence of adverbs.
In contrast to the sentences with aspectual adverbs, these sentences lacked the temporal information needed todetermine the intended tense. Thus it was not possible to annotate temporal information to these sentences intrasententially. The Time ML required a minimum of two events for the temporal relation between the two events tobe meaningfully annotated. Hence, a different methodology was used to determine the OPT occurrence – anannotator was required to determine the occurrence of OPT in non-adverbial sentences. The tenses of randomly
selected sentences were determined from the context of three sentences before and after the selected text units. Eachsentence was checked for its tense and was subsequently categorized as (a) unknown (sentences that are ambiguousor cannot be determined by the context), (b) positively present, (c) positively past or (d) OPT occurrence. To avoid
this meticulous categorization, future annotation tasks that seek to further discuss the OPT phenomenon should
consider a different methodology of inter-sentential time annotation.
Disregarding the unknown sentences from the categorization task, the OPT phenomenon was present 17.14% oftime in the sentences without perfective adverbs however the OPT occurrences in these sentences were significantly
lower (p<0.05, z=5.71) than OPT occurrences in sentences with already and yesterday.
5.2. Is the OPT-ional phenomenon uniquely Singaporean?
Using the same definition of the OPT phenomenon in Table 6, the omission of past tense was also present in thetime annotated ICE-HK; the OPT phenomenon was present in 36.55% of the sentences with the already andyesterday adverb (see Table 8).
Kortmann and Lunkenheimer (2011) observed that the morphological simplification is a recurrent feature acrossWorld Englishes, where they are often motivated by the lack of morphology in contact varieties. In the case of
Singapore and Hong Kong Englishes, it is possible that typologically isolating Chinese substrates (e.g. Mandarin,
Cantonese, Min, etc.) had influenced the occurrences of the OPT phenomenon in SCE and Hong Kong English. The
rate of occurrence in the ICE-SIN (68.18%) is above chance (50%) while the rate (31.89%) in the ICE-HK is below
the opportunistic threshold. Though the OPT is not absolutely unique to SCE, it is more salient as compared to Hong
Kong English.
Table 8: OPT Occurrences in already and yesterday sentence in ICE-HK
Syntactic Structure
V-RB

OPT
27

non-OPT
37

Total no. of sentences
64

RB-V

45

78

123

Total occurrences

72 (36.55%)

115 (63.45%)

187

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5.3. Are there syntactic constraints to the occurrences of OPT in SCE?
From Table 7 and 8, both Englishes reflected a higher rate of occurrences of the OPT phenomenon in sentenceswith pre-verbal adverbs. However, it remained uncertain as to why the pre-verbal adverbs stimulate the OPT
phenomenon more extensively than post verbal adverbs. From the already examples provided in Bao (1995) and theanecdotal experiences of the authors of this paper, it went against the common perception that the OPT phenomenon
is more common in sentences with post-verbal adverbs. The current layer of temporal annotation is insufficient to
explain this incongruity.
5.4. Relevance of the investigated OPT in SCE today
While the ICE-SIN is representative of SCE in size and stratification, it must be noted that the data was collectedin the 1990s (more than 20 years ago from the time of this writing). The linguistic variation of SCE can be said to becomparatively more volatile than the relatively well-established British English, given the observation of a sharp
language shift where the usage of English at home in Singapore has increased in the late 2000s (Vaish, 2008).
Therefore, the corpus data might not be representative of the current variety of SCE spoken in Singapore.
Nevertheless, time disparity is often disregarded in corpus linguistics studies when recently built corpora werecrossed referenced with the de facto British National Corpus (2007); e.g. Ferraresi et al. (2008)’s cross-corpora
evaluation of ukWaC corpus with the BNC.
Still, the question remains; is the OPT-ional phenomenon still relevant today? Anecdotally, as speakers regularly
exposed to Singapore English, we are positive that the OPT phenomenon is present in SCE today. However, from anempirical perspective, we can only be as certain as what the data had presented.
6. Conclusion
This paper described the investigation of the OPT phenomenon in SCE and HKE. In the process, the ICE-SIN
and ICE-HK were extended with TimeML temporal tags.
From the temporal annotations, we observed that the rate of the occurrence of the OPT phenomenon was 68.2%
in SCE sentences and 36.6% in HKE sentences with the perfective adverbs already or yesterday. Although the OPT
phenomenon was not unique to Singapore as it is also found in Hong Kong English, it was more salient in Singapore
English. By looking at the pre/post-verbal position of the perfective adverb, we observed that the OPT phenomenon
was more prominent in sentences where the adverb occurs before the verb.
Future researchers could attempt to further extend the corpora with full grammatical sentence parses (e.g.
Context-Free Grammar or Dependency Grammar parses) or look into the phonological environment where the OPT
phenomenon occurs.
Finally, this paper also discusses the time disparity limitations in cross-corpora comparison. The time disparity
between built corpora and the time of linguistic investigation urges the need for corpora to be self-sustainable so as
to be relevant for continual linguistic research. Otherwise, corpus-based researches would be considered historical as
the data may no longer be relevant to the linguistic phenomenon under investigation. The task of bridging the gap
between a natural language phenomenon and pre-dated corpus representation is indeed a conundrum, but this must
not deter future corpora-based researches.
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