論 文

Tagging a Japanese Learner Corpus of English
and Comparing Trigrams with Thosein a Corpus of British Students’ Essays

Yoshihito Kamakura

要旨

本研究は，日本人英語学習者とイギリス人学生の作文から構成されたコーパスを数量的と質的に分析している。その分析において，３語の連なりから成るtrigramの頻度とその構成をイギリス人ネイティブ学生のものと比較し，日本人英語学習者特有の句構成（phraseology）を見出すことを目的としている。さらに，学習者コーパスへのタグ付けのため，二つの異なるタグ付記プログラムを用い，その特性と限界を調べている。
本研究の目的は，日本人英語学習者が多用する句構成を記述することである。英語母語話者とは異なる言い回しが中間言語におけるforeignlikenessと捉えられる。ネイティブの規範と異なる言い回しは，意味の伝達において誤解を呼ぶこともありうる。学習者コーパスに見られる過剰使用（overused）の句構成と通常より頻度が低い（underused）句構成を調べることで，学習者言語では句構成が固定されていることが明らかになった。学習者に共通する句構成を明らかにすることで，柔軟な句構成を用い，より豊かな表現をするよう，教員が指導できる。
本研究の結果として，綴りの間違いがあるものの，上記二つのタグ付記プログラムは学習者の句構成を分析することが十分可能であった。その結果，日本人英語学習者はinを含む前置詞句を文末に使う傾向があり，
さらに to 不定詞＋English＋inという句の過剰使用が見られた。一方，受動態の文で in が使用されることが少なく，学習者の過去分詞を用いた句の使用が限られていることを示している。

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Keywords: learner corpus（学習者コーパス），quantitative and qualitative features（量的と質的な特徴），trigram（3連語句），phraseology（フレイジオロジー，句構成），annotation（タグ付け）

Introduction
Learners of a second language may retain unique features in their production, unlike
those of the vernacular users of the target language. Learner corpora have been
established with the aim of discovering the key attributes of learner languages bycontrasting them with those in a corpus of native production. However, thecomparison of concordance lines as raw data may be arduous, in particular whenanalysing the environment of a given word - in this case, the words preceding andfollowing a preposition. A first attempt was made by means of trigrams to consider
the adjacent words to the preposition in the corpora of Japanese learners of English
and British students as follows:
Table 1 Top ten clusters of multi-word unit with in
the learner corpus of Japaneselearners (JP)ClusterFreq.

the corpus of British A-level essays(NS: native speaker)
ClusterFreq.

IN THE WORLD68

IN ORDER TO

19

IN THE FUTURE

52

IN THE UK

18

LANGUAGE IN THE

27

IN THE WORLD17

IN ORDER TO

22

IN MY OPINION16

ENGLISH IN THE

18

AN INCREASE IN

16

IN ENGLISH AND

16

THE UK

14

STUDY ENGLISH IN

16

IN THIS COUNTRY

13

PEOPLE IN THE

16

IN THE U

13

ENGLISH IN JAPAN14

IN THE CASE

13

TO SPEAK ENGLISH

13

THE FIELD OF

12

Although extracting the trigrams made it possible to compare the two corpora in―2―

Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays
terms of frequency, the trigrams in Table 1.1 may not always make the uniquefeatures of Japanese learners’ writing obvious. Aarts and Granger (1998) apply tagging
to a learner corpus and highlight the underlying features of learner writing. Learnersof a second language, particularly in a classroom setting, are likely to experience aheightened awareness of grammar. Two reasons can be suggested: one is thatsystematic instruction of grammar is in some way involved in establishing teachingsyllabuses and materials; the other is that grammar inevitably plays an essential role
for learners as they generate a sentence. Tagging a learner corpus could shed light on
the norms of learner language which originally derive from the grammatical
instructions in the classroom, while tagging a native-speakers’ corpus aims to reveal
the norms which underlie the conventional use of language. By annotating thecorpora and extracting trigrams, it is hoped that this study will enable a quantitative
comparison of the given corpora to be made and thus offer an insight about thepeculiarities of the second language in the hands of Japanese learners.
   The following section will consider the background of taggers as well as thetagging of learner corpora. The methodology of this research and the descriptions oftaggers will be discussed in the third section. The fourth section presents a case studyof learner corpus tagging and discussion of the results will follow in the closing
section.

1. Background
Corpus annotation can provide a corpus with additional linguistic information inorder to enrich the research. However, some claim that corpora should be ‘raw’ and‘pure’, adding nothing artificial to the language, since such annotation may distort
the features of a language in favour of existing linguistic theories and methods(Sinclair, 1991, Hunston 2002). Yet annotating corpora could indeed be beneficial, if‘reliable’ and ‘clearly-definable’ information is appended to the texts (Leech, 1997).
Moreover, it is maintained that tagged corpora could contribute to discovering thedistinctive features of learner grammar (Aart and Granger, 1998).
   Atwell and Elliot (1987) use tagging to detect errors in a text by finding that thesequence of uncommon tag pairs was caused by learner errors. They evaluate how
misspelled words affect the results of tagging and develop the way in which
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No. 27algorithms can generate a cohort of words similar to a mistyped word. Aarts andGranger (1998) extract the trigrams which are generated from the tagged learnercorpora by Dutch, Finish and French learners. The three-word units are comparedwith those of native English-speaking students and a comparison of the trigrams
illustrates the divergence of the interlanguage grammar from the native speakernorm.
   Computer annotation began with the word-class tagging of the Brown Corpus
in the early 1970s and the tagging project of the LOB Corpus launched between
1979 and 1982, aimed at achieving high rates of tagging accuracy. The bulk of earlytagging software employed probabilistic methods: the Penn Treebank TreeTagger(henceforth, TreeTagger) implements the probability of sequences where a given wordis preceded by other attributes and determines the particular tag for each word
(Schmid, 1994). In terms of tagging ill-formed text, it is interesting to note that
TreeTagger is still designed to annotate misspelled words correctly (Santorini, 199).
The first version of CLAWS part-of-speech tagger (henceforth, CLAWS) was designed
for tagging the LOB Corpus on a probabilistic basis and has since evolved to copewith tokenizing contracted forms and idiomatic phrases (Garside, 1987). Clearly,
taggers inevitably reflect the linguistic theories which determine the methods oftagging. The users of taggers are thus expected to implement the methods andtheories which underlie them.

2. Methodology
2.1. Data and research method
Two essay corpora were investigated, for purposes of comparison: the Showa
Women’s University learner corpus, about 85,000 words, which is composed of 175
essays by Japanese learners and the corpus of British A-level student essays, which is
extracted from the LOCNESS (Louvain Corpus of Native English) corpus, the size of
which is approximately 80,000 words; this has been adapted to correspond with thesize of the learner corpus. These corpora are designed according to the corpus design
criteria of the ICLE (the International Corpus of Learner English), which controls
many variables of the corpora to allow mutual comparison.
   The two corpora are tagged by TreeTagger and CLAWS and the trigrams of tags
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Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays
are enumerated and sorted by the Perl Program in order of frequency1. The overused
and underused trigrams in the learner corpus, when compared to the native corpus,
will be examined by reading the concordance lines in order to discover typical choicesof words in the trigrams2.

2.2. Tokenization
Tokenization, which assigns a token to a word unit, should be completed before thetagging of annotations, since without tokenization it might be impossible to assign
tags appropriately to word units. The languages which have one-character-size spaces
1 The Perl programme was employed to extract trigrams as follows:<Perl programme for extracting trigrams from the results of the Treetagger>$firstline=<>;$secondline=<>;($token, $firsttag, $lemma)=split(/\t/,$firstline,3);($token, $secondtag, $lemma)=split(/\t/,$secondline,3);
while(<>){
chomp $_;
$line=$_;($token, $thirdtag, $lemma)=split(/\t/,$line,3);
print “$firsttag $secondtag $thirdtag\n”;$firsttag=$secondtag;$secondtag=$thirdtag;}<The programme for extracting trigrams from the results of CLAWS>
while(<>){
chomp;$line=$_;@array=split(/ /,$line);for each $wordtag (@array){#if($wordtag ne “”){($word, $tag)=split(/\_/,$wordtag);
if ($tag ne “”){print “$word$tag\n”;}#}}}

2 Overuse and underuse are determined by comparing the raw frequencies of trigrams, that is,
no statistical comparison with probability is undertaken.

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between words - such as English, Spanish and other European languages - aremainly assigned one token per word, but some contend that ‘contracted forms’ may
cause deviation from the one-to-one relation between a word and a token (Leech,
1997, Garside, 1987). The contraction of words primarily occurs with such verbs as be,
have and modal verbs, followed by ’s, ’d or another abridged ending: am into ‘m; areinto ’re; have into ’ve. In addition, there are other contracted forms affecting
tokenization: the enclitic form of the negative particle not as n’t, attached to the ends
of verbs or modal verbs. In order to tag a text containing such contracted forms, thecontracted part of the word has to be segregated from the verb as a way of inserting a
one-character-size empty space directly before the contracted form: for instance, I’m
into I_’m; isn’t into is_n’t 3.
   The description of CLAWS tokenization is not available, so that the tokenizationneeds to be assessed by analysing the list of contracted forms (Leech and Smith, 2000).
The contraction attaching to be, have, modal verbs and n’t is seemingly the identicalclassification for tokenization to that for TreeTagger. CLAWS can in addition assign atoken to the contracted forms which are composed without the apostrophised n’t,
and also the contraction which is derived from the omission of a syllable in everyday
pronunciation (e.g. do you → d’you).

2.3. Tagsets
Tagsets represent the list of tags employed for tagging, where the content of thetagsets reflects the method of anatomising a language. There are various types of
3 Tokenization was carried out by the following script:
		
		
		
		
		
		
		
		
		
		
		
		
		
		

# do tokenization$TOKENIZER +1 +s +l $ABBR_LIST |# separate clitics from preceding words
sed -e “s/’s”‘$’”/ ‘s/g” \-e “s/s’”‘$’”/ ‘/g” \-e “s/n’t”‘$’”/ n’t/g” \-e “s/’re”‘$’”/ ‘re/g” \-e “s/’ve”‘$’”/ ‘ve/g” \-e “s/’d”‘$’”/ ‘d/g” \-e “s/’m”‘$’”/ ‘m/g” \-e “s/’em”‘$’”/ ‘em/g” \-e “s/’ll”‘$’”/ ‘ll/g” \-e ‘/^$/d’ |tr ‘ ‘ ‘\n’ |―6―

Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays
automatic and manual tag: semantic tags, syntactic tags, prosodic tags and error tags.
This research seeks to describe the grammar features of Japanese learners’ writings,
resulting in a concentration on the part-of-speech tagsets of TreeTagger and CLAWS(Santorini, 1991, UCREL, n.d.).
   The structure of a tag is closely related to the linguistic theories and methods
which are adopted when establishing the tags (Hunston, 2002). The methods of
encoding tags vary in their ways of categorising annotated words. For example,
prepositions are classified in the TreeTagger tagset into ‘tags for prepositions andsubordinating conjunctions’, while the CLAWS C7 tagset has four subcategories for
prepositions4.
   Tags embody the classification of tagging by being represented in an acronym
composed of two to five characters. In general, the formation of a tag indicates a
category hierarchy by beginning with the first one or two characters whichcorrespond to the part-of-speech of a given token: N=noun, V=verb, JJ=adjective, Ror RB=adverb, DT=determiner. Note that the grammar categories are arbitrarily
determined by the choice of grammar theory, so that a token may have distinct
symbols or categories for a word: for example, IN=preposition in TreeTagger;
II=general preposition, IF=for, IO=of, IW=with, without in CLAWS. Beneath thecore category of part-of-speech is a subordinate category which attributes a ‘contextdependent’ meaning to tag symbols (Leech, 1997: 27), such that N (noun) is followedby P, which refers to ‘proper’ or double P (PP) meaning ‘personal pronoun’.
2.3.1 The TreeTagger tagset
This tagset consists of 36 tags, fewer than the CLAWS tagset. However, the size of atagset is not necessarily important, since increases and decreases of size depend on
what is emphasised for the purpose of the tagging (Leech, 1997). The TreeTagger tagset
seems simple and recognisable when decoding the tags; however, the limitation of
the tag variety, in terms of discouraging ambiguity, may create an overlap in
categories which should be rigorously distinctive, for example, IN for prepositions
and subordinating conjunctions.
4 A free CLAWS tagging service is available on the Internet (http://www.comp.lanc.ac.uk/ucrel/claws/trial.html), offering the choice of UCREL C5 or C7 tagset. The C7 tagset is well explainedin an article by Leech (1997).

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Table 2 Extract from the TreeTagger tagset (Santorini, 1991)
DT
IN
JJ
NN
NNS
PP
SENT
VBP

Determiner
Preposition or subordinating conjunction
Adjective
Noun, singular or mass
Noun, plural
Personal pronoun
Sentence delimiter
Verb, non-3rd person singular present

2.3.2 CLAWS tagset (UCREL CLAWS7 tagset)
The CLAWS7 tagset consists of 141 tags, which mostly represent the attributes oftokens with three-character symbols. Some of these symbols are followed by the
number which stands for singular or plural - NN1 for singular common noun, AT1
for singular article such as a, an. These tagsets seem to retain high analysability;
however it is difficult to decode them without consulting the list of tagsets. It needs
to be determined in the trade-off whether conciseness and perspicuity are worth thesacrifice of high analysability.
Table 3 Extract of UCREL CLAWS7 tagset (UCREL n. d.)
AT

Article (e.g. the, no)AT1

Singular article (e.g. a, an, every)IF

For (as preposition)

II

General preposition

IO

Of (as preposition)IW

With, without (as preposition)

JJ

General adjective

NN1

Singular common noun (e.g. book, girl)

NN2

Plural common noun (e.g. books, girls)

TO

Infinitive marker (to)VVI

Infinitive (e.g. to give..., It will work…)

VVN

Past particle of lexical verb (e.g. bound in be bound to).

Full stop―8―

Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays   In this section, the methodologies, tokenization and tagsets were discussed 5.
Before interpreting the results of tagging, it may be necessary to ascertain how thetokenizer functions and what tags are assigned. The tokenization of taggers which aredefined by identical classification of contracted forms may not affect the results,
when the tagging is compared. However, note that the assignation of tags differs in
the two tagsets, which may closely relate to the interpretation of the tagged outcome.
On this basis, the tagging of a learner corpus and an application of tagged learnercorpora will be considered in the next section.

3. Case study of learner corpus tagging
3.1. Evaluation of learner corpus tagging
Practical applications of learner corpora are highly likely to involve analysing texts
which may contain misspelled words. Atwell and Elliot (1987) apply the peculiarityof the ill-formed word to detecting and marking learner errors. Typing errors may
result in mistakenly assigning an error word to an unmatched tag, which has adifferent meaning from what the learner intended. Before interpreting the tagged
results, it should be confirmed whether the two taggers have assigned suitable tags to
the remaining misspelled words and how they treat unidentified words in the processof tagging.
   TreeTagger yields the results of tagging, as seen in Table 4.1, in the order of‘token’, ‘part-of-speech tag’ and ‘lemma’. It is likely that the low flexibility of thetokenization may cause inaccurate tagging. Cannot is tagged as JJ (adjective) and thelemma turns out to be <unknown>, a word which may not be found in the word list
with which the tagger identifies the lemma of words. Had the tokenization divided it
into can and not, they could have been assigned to appropriate lemmas. Misspelled
5 A tagset for a learner corpus will be briefly discussed: the TOSCA-ICLE tagset is designed fortagging the ICLE corpus. Where it differs from TreeTagger and CLAWS is the uniformity of the tag
symbols which refer to the part-of-speech and the illustration of the subcategorised part inbrackets, e.g., N (sing, collect). The TOSCA-ICLE tagger is restricted in its circulation and it
proved impossible to contact the authors of the tagset and an ICLE researcher. The discussion on
the tagger exclusively centres on the tagset, which is available on a Kaszubski Webpage (Kaszubski,2003).

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despite showing an <unknown> lemma, which may not identify with any of thewords in the lemma list.
Table 4 Results of the Penn Treebank TreeTagger tagging
cannotpm. [sic.]

JJ
NN<unknown><unknown>

yout [sic.]
firends [sic.]

JJ
NNS<unknown><unknown>

not
think
that

RB
VB
DT

not
think
that

I
though [sic.]
that

PP
RB
IN

I
<unknown>that   Misspelled words may cause error tagging. For example, the that which
introduces a complement clause, as in I do not think that most Japanese students are
too easy going, mainly falls into being tagged as a determiner (DT). However, the thatwhich follows an ill-formed word though as in I though that I would study hard incollege - maybe a mistake for thought - have a preposition tag (IN). The preceding
misspelled word may affect the choice of tagging for the complement subordinator.
   The C7 vertical format of the CLAWS tagging shows that the two principal
results are ‘token’ and ‘tag’ 6. It should be noted that the tagger indicates theprobability of tags. For example, a that which follows think as a complement
subordinate is estimated as 63% of conjunction that (CST) and 37% of singulardeterminer (DD1) and results in its being tagged as CST.

6 The horizontal tagging of CLAWS indicates only the highest probability’s being tagged, since
the tag format is designed for the application of the tagged text to the concordance software, notfor analysing the results of the tagging.

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Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays
Table 5 Results of UCREL CLAWS7 tagging
0000004
0000004
0000004

820 can
821 not
020 pm. [sic.]> 56 VM< 56 XX
93 RA

00000030000004

630 yout [sic.]
270 friends [sic.]

06 [NN1/99] VV0/1
98 NN2000000300000030000003

030 not
040 think
050 that

93 XX
93 VVI
97 [CST/63] DD1/37 RG%/0000000300000030000003

290 I
300 though [sic.]
310 that

93 [PPIS1/100] ZZ1%/0 MC1%/0
93 [RR@/98] CS/2
96 [CST/100] DD1%/0   These elaborated tags, in part, appear to deal with the problems of tokenizationand misspelled words which the TreeTagger raises. Cannot is tokenized into twowords can and not, tagged modal auxiliary and not respectively, while mistyped pm.
[sic.] and yout [sic.] have inappropriate tags. The complement subordinator that has
the right tag of conjunction that (CST), even following the tagging of an erroneousverb though [sic.] as a general adverb (RR).
   The bulk of tags are properly assigned; however the misspelled words which arefrequently found in learner writings may result in misleading tagging. Hence, despite
the possibility that tagging may present some findings which underlie the text,
verifying the matching of tags and perusing concordance lines is essential wheninterpreting the results of tagging a learner corpus, as well as tagging corpora ingeneral. We will therefore examine the trigrams of tags first and then scrutinise theconcordance lines.

3.2. Trigrams by TreeTagger
This section will investigate the results of trigrams of the two corpora being taggedby TreeTagger and continue the interpretation of the overuse and underuse trigrams
compared with those of native writings.
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Table 6 The ten most frequent trigrams and underuse of the two corpora by TreeTagger
JP
Freq.
1283
1017
581
538
526
508
505
504
404
392

125

IN
DT
NN
JJ
IN
IN
NN
NN
IN
IN

Trigram
DT
NN
IN
NN
PP
DT
SENT
IN
NN
JJ

VBN

IN

NN
IN
NN
IN
VBP
JJ
IN
DT
SENT
NN

Freq.
1833
1678
1148
780
747
657
426
391
388
357

NS
Trigram
IN
DT
DT
NN
NN
IN
IN
DT
JJ
NN
NN
IN
NNS
IN
VBN
IN
IN
DT
IN
JJ

NN
IN
DT
JJ
IN
NN
DT
DT
NNS
NNS

DT

208

IN

NN

SENT

144

IN

PP

VBP

a) IN+PP+VBP (Preposition or Subordinating conjunction + Personal pronoun +Verb, non-3rd person singular present)
This trigram shows the distinctive frequencies being considerably overused. The IN
tag refers to prepositions (in, on, at. etc.) and subordinating conjunctions (as, if,because and other subordinators) of the latter, which do not correspond to the target
preposition in. By reading the concordance lines extracted according to the trigram,many of the IN tags turn out to be tagged not on preposition in, but on subordinating
conjunctions, such as although, because, if, that and the like. The tags PP and VBP,
indicating personal pronouns and verbs, illustrate this set of tags as beginners ofclauses. Despite the attempt to tag a phrase containing the preposition in, this trigram
only managed to reveal the overuse of phrases which were composed of subordinating
conjunction, personal pronoun and singular present verb. Yet the concordance lines
extracted on the basis of the trigram show an interesting phraseology by Japaneselearners: of 526 instances of IN+PP+VBP; the clauses starting with if (168) occur
more frequently than those with that (152). This may imply that Japanese learners are
inclined to use if-clauses in their essay writings. It should, however, be noted that essay― 12 ―

Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays
topics selected by the learners might relate to the surge of frequency of if: that is, ahypothetical question as essay topic is likely to encourage learners to argue by using ifclauses excessively. Reading concordance lines can avoid matching with different tags
from what one expects, and lead to findings which may be useful and suggestive.
b) IN+NN+SENT (Preposition or subordinating conjunction + Noun, singular or
mass + Sentence delimiter)
The tokenization of TreeTagger nominates a tag ‘SENT’ for full stops, while commas
are assigned to ‘,’ only. This trigram seems to illustrate that the Japanese learners tend
to use prepositional phrases at the ending of a sentence. The 63 instances of inphrases do not dramatically vary in the types of noun: college, future, school, society,summer, university, workplace, world predominate in the phrases. Japanese learners
tend to express the time when they were studying in a college, school and university
by using the phrase ‘in + INSTITUTION’. This prepositional phrase does not containarticle the between the preposition and college, school, university, which implies a lackof articles in the writings by Japanese learners. Different terms for institutions oflearning - college, school, university - are employed for referring to either time orplace ambiguously, as shown below:
1 uld get along in the world while I would be in university. So I will stud
2 lusion, It is very comfortable for me to be in university. There are a lot
3

d to this. Seventhly why do I learn English in University. I just learn Engl

4nglish in University. I just learn English in University. But I need to

5 the best of time. We can learn special field in university. I learn English
6 be in university. There are a lot of friends in university. And I can have a7

ther country’s language. I studying German in university. German is difficu

8

nd I want to find the job after graduation in university. Finally, in the   By contrast, the nouns in the prepositional phrases in the native writings show
considerable variety, unlike those in the learner corpus. They are not only words
which refer to time and place, but also to words which signify the state which theagent of a clause goes into - advance, charge, failure, general, luxury, unemployment,
which do not occur with article the. As a result, prepositional phrases which denote a
metaphorical state, such as in advance, in failure, in luxury, may be difficult forlearners to learn to produce in the writings of the second language.

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No. 27

PP want_VBP to_TO travel_VB in_IN advance_NN ._SENT Also2

to_TO keep_VB farmers_NNS in_IN business_NN ._SENT The

3

DT human_NN is_VBZ still_RB in_IN charge_NN ._SENT The_D

4

N to_TO its_PP$ treatment_NN in_IN court_NN ._SENT Our_PP

5

to_TO these_DT changes_NNS in_IN demography_NN ._SENT

6

an_MD do_VB is_VBZ flee_VB in_IN desparation_NN ._SENT It

7

T of_IN course_NN ended_VBN in_IN failure_NN ._SENT There_

8

G beef_NN ,_, or_CC meat_NN in_IN general_NN ._SENT Meat

9

en_NNS alike_RB travel_VBP in_IN luxury_NN ._SENT British

10_

CC more_JJR importantly_RB in_IN manufacturing_NN ._SEN

11

T single_JJ market_NN is_VBZ in_IN operation_NN ._SENT It_P

12

VBD instantly_RB divided_VBN in_IN opinion_NN ._SENT The_13

B down_RP ,_, resulting_VBG in_IN unemployment_NN ._SEN

14

JJ yard_NN is_VBZ already_RB in_IN use_NN ._SENT The_DTc) VBN+IN+DT (Verb, past participle + Preposition or subordinating conjunction +Determiner)
This trigram is underused by Japanese learners; that is, trigrams of this kind in thelearner corpus are much more rarely than in the native corpus. The trigrams by thelearners interestingly show a regular patterning of multi-word units together withparticular past participles: be interested/located/spoken/ written in. The variations of thepast participles in the trigram turn out to be smaller that those which occur in the
British students’ corpus, which may result in the underuse of this trigram. Of thelimited variety of the sequence of past participle + in + determiner, the dominantfrequency of interested in may result from the materials which learners use inclassroom: a textbook for Japanese learners of English presents be interested in as alikely set phrase (Kasashima et al. 2006). Such input provided in the classroom is likely
to affect the outputs of learners; in some cases learners’ outputs may be formulatedby the instruction to use a pseudo-set phrase.
VBN+IN+DT in the learner corpus
1

I_PP am_VBP interested_VBN in_IN another_DT country_NN cul

2

T I_PP ‘m_VBP interested_VBN in_IN another_DT country_NN ._3

e_PP can_MD interested_VBN in_IN another_DT culture_NN and

4

d_CC are_VBP interested_VBN in_IN the_DT final_JJ information

5

I_PP am_VBP interested_VBN in_IN the_DT first_JJ type_NN ._6

ENT It_PP ‘s_VBZ located_VBN in_IN the_DT central_JJ Bali_NP― 14 ―

Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays
7

ms_NNS are_VBP located_VBN in_IN an_DT inconvenient_JJ plac

8nglish_NP is_VBZ spoken_VBN in_IN all_DT around_IN the_DT9nglish_NP is_VBZ spoken_VBN in_IN any_DT places_NNS ._SE10

nglish_NP is_VBZ spoken_VBN in_IN the_DT United_NP States_

11

nglish_NP is_VBZ spoken_VBN in_IN the_DT world_NN ._SENT

12

nglish_NP is_VBZ spoken_VBN in_IN the_DT world_NN ._SENT

13

f_IN it_PP is_VBZ written_VBN in_IN a_DT language_NN you_P

14_IN it_PP is_VBZ written_VBN in_IN a_DT language_NN you_P   The components of this trigram among British students appear to be chosendifferently from those among Japanese learners. The use of the past participles in thewritings by the British seems to fall into two subcategories: where the verbs recurrently
follow an auxiliary verb + be and where they can follow either be or a noun. Adopted,
involved, left, used and other past participles which frequently occur in the British
student corpus could be introduced to learners as frequent words in passive clauses bynative speakers. Such suggestions may enrich learners’ expressions in their outputs.
be VBN in DT in the native corpus
1

DT could_MD be_VB adopted_VBN in_IN the_DT U.K._NP It_PP would

2

ed_VB to_TO be_VB adopted_VBN in_IN the_DT U.K._NP in_IN the_D3

NP must_MD be_VB involved_VBN in_IN the_DT decision_NN making_4_DT injuries_NNS involved_VBN in_IN the_DT sport_NN5

money_NN are_VBP involved_VBN in_IN this_DT treatment_NN ,_, as_

6oject_NN are_VBP involved_VBN in_IN the_DT collosal_JJ task_NN o7

he_DT people_NNS involved_VBN in_IN this_DT persevere_VB or_CC

8

N ,_, he_PP was_VBD left_VBN in_IN a_DT coma_NN with_IN serio

9,_, was_VBD also_RB left_VBN in_IN a_DT critical_JJ condition_NN

10

RB much_RB been_VBN left_VBN in_IN the_DT dark_NN as_IN their_

11
12

N has_VBZ been_VBN used_VBN in_IN the_DT past_NN ,_, we_PP n
T Genetics_NP is_VBZ used_VBN in_IN another_DT way_NN linked_V

13

F_NP would_MD be_VB used_VBN in_IN a_DT case_NN where_WRB

14

BZ very_RB widley_VB used_VBN in_IN the_DT modern_JJ day_NN w

be VBN in DT, or NN VBN in DT in the native corpus
1

r_NN parties_NNS embroiled_VBN in_IN a_DT heated_JJ debate_NN ov

2irect_JJ elections_NNS held_VBN in_IN each_DT country_NN to_TO el

3

accident_NN or_CC killed_VBN in_IN a_DT *_SYM attack_NN in_IN4

DT mother_NN and_CC placed_VBN in_IN an_DT environment_NN which_― 15 ―

愛知大学

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言語と文化

No. 27m_IN a_DT woman_NN placed_VBN in_IN a_DT test_NN tube_NN and_C

6

ill_MD then_RB be_VB placed_VBN in_IN the_DT womens_NP womb_NN

7

and_CC information_NN stored_VBN in_IN a_DT computer_NN is_VBZ kn

The comparison of learner and native examples of the trigrams generated from thetagged words by TreeTagger illustrates that the patterning of multi-word units may beboth overused and underused. However, the tag trigram a) has not managed to show
three-word sequences containing preposition in. The following section will investigate
the way in which the trigrams are tagged by CLAWS.

3.3. Trigrams by CLAWS
CLAWS tags the two identical corpora being tagged by TreeTagger in the previous
section. The CLAWS tagger, which is made up of more tags than TreeTagger, may
shed light on features which were not previously elicited by the TreeTagger tagging.
Table 7 The ten most frequent trigrams and underuse of the two corpora according to CLAWS
JP
Freq.
518
283
205
196
196
191
183
174
170
161

60

NS
Trigram
II
AT
NN1
II
JJ
NN1
II
AT
II
AT1
VVN
II
AT
NN1
II
AT1
II
JJ
NN2
II

NN1
AT
II
JJ
NN1
AT
II
JJ
NN2
AT

II
II
TO
VVI
NN1
II
NN1
JJ
NN1
NN1

Trigram
AT
NN1
VVI
NN1
II
JJ
II
NN1
.
II

NN1
.
II
II
AT1
NN1
NN1
II
II
AT

Freq.
763
361
266
261
235
207
175
170
168
148

VVN

II

AT

102

TO

VVI

II

91

II

NN1

.

40

VVI

NN1

II

― 16 ―

Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essaysd) II+NN1+ . (General preposition + Singular common noun + Full stop)
As mentioned in the section on tagsets, these two taggers each have original tags
which do not correspond in the system of coding. In decoding and interpreting theresults of tagging, the unlikeness of tag representation should be borne in mind.
However, this trigram does represent the same three-word units as are encoded as
IN+NN+SENT in TreeTagger. Although the representations are coded by different
symbols, the words of the prepositional phrases are individually identified in bothtagged trigrams. Interestingly, this trigram occupies the second place for frequency- 113 instances of in out of 283 preposition tokens, which is compared to ninth
place in the TreeTagger trigram results - 62 instances of in out of 404 instances. This
divergence illustrates that the specification of a tag can affect the interpretations oftagging. Therefore, a close examination of tagsets may be essential before interpretingthe outputs by any particular tagger.
e) TO+VVI+II (Infinitive marker [to] + Infinitive + General preposition)
This trigram does not appear to be on the frequency list of TreeTagger, although thedivergence of the tagsets does not affect the chance of finding this trigram. It isfollowed by nouns which illustrate typical features in learner writings, seemingly
related to the choice of verbs. The nouns, preceded by in, refer to a sequence withget, live, spread, study, survive and work. These verbs preceding in suggest that thepreference of learners is to represent the locative meaning of the preposition. At thesame time, in English often recurs after communicate, say and speak which closely
relate to the utterance of language. As a result, Japanese learners are likely to grasp
the meanings of in in terms of location and language, while the other meanings
might be too unfamiliar for the learners to use in their writings.
to VVI in place
1
2

NN2 ._.

Hurryup_VV0 to_TO get_VVI in_II this_DD1 boat_NN1 !_! “_”enger_NN1 tried_VVD to_TO get_VVI in_II

3

e_VBI

4

1 always_RR good_JJ to_TO live_VVI in_II

56_difficult_JJ to_TO live_VVI in_II this_DD1 world_NN1 ._.

So_RR

a_AT1 hospital_NN1 with_IW m

We_PPIS2 have_VH0 to_TO live_VVI in_II these_DD2 stuation_NN1 ._.
AT society_NN1 ._.

To

To_TO live_VVI in_II a_AT1 nation_NN1 society_NN1

7 nt_JJ for_IF us_PPIO2 to_TO live_VVI in_II the_AT8

But_

fullboats_NN2 ,_, Rowe_NP1 m

twenty-first_MD century

PH1 is_VBZ apt_JJ to_TO spread_VVI in_II world_NN1 ._.

― 17 ―

Many_DA2 foreig

愛知大学

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言語と文化

No. 27s_VHZ began_VVN to_TO spread_VVI in_II Japan_NP1 ._.

10

nt_VVD abroad_RL to_TO study_VVI

11

he_AT division_NN1 to_TO study_VVI in_II foreign_JJ

12
13
14
15

_VBZ going_VVGK to_TO study_VVI

But_CCB ,_, no

in_II Boston_NP1 ._.

She_PPHS1 isuniversities_NN2

in_II graduate_NN1 school_NN1 told_V

TO be_VBI able_JK to_TO survive_VVI in_II the_AT international_JJ

socie

an_AT1 idea_NN1 to_TO survive_VVI in_II this_DD1 international_JJ soc
tudy_VVI English_JJ to_TO work_VVI in_II social_JJ._.

Chinese_JJ a16aybe_RR want_VV0 to_TO work_VVI in_II Asia_NP1 ._.

17

y_PPHS2 want_VV0 to_TO work_VVI in_II Japan_NP1 to_TO earn_VVI

If_CS this_DD1

18

S I_PPIS1 want_VV0 to_TO work_VVI in_II international_JJ airplane_NN1

mo

to VVI in English
1

k_VVI to_TO communicate_VVI

in_II English_NN1 ._.

I_PPIS1 d

2

PHS2 want_VV0 to_TO say_VVI in_II

3

PPIS1 want_VV0 to_TO say_VVI in_II English_NN1 ,_, but_CCB atEnglish_NN1 ._.

That_DD1

4

R children_NN2 to_TO speak_VVI in_II exact_JJ

5_PPIO1 fun_JJ to_TO speak_VVI in_II English_NN1 ._.

Japanese_J

6_JJ hesitant_JJ to_TO speak_VVI in_II English_NN1 ._.

They_PPH7

PPY need_VV0 to_TO speak_VVI in_II

pronunciation_NN1

English_NN1 ._.

Second_N

8

S2 need_VV0 to_TO speak_VVI

in_II their_APPGE business_NN1 ,9potunity_NN1 to_TO speak_VVI

in_II English_NN1 from_RT31 now_10

O used_VMK to_TO speak_VVI

in_II English_NN1 ._.

Anyway_Rf) VVI+NN1+II (Infinitive +Singular common noun + General preposition)
There are 196 instances of this trigram, of which 38 instances contain preposition in.
The 31 instances out of 38 of the trigrams contain English as object, following such
verbs as learn, speak, study, teach and use. It is likely that the learner may be inclined
to employ a patterning to learn/speak/study/teach/use English in. In addition, thistrigram appears to be followed often by future and world, together with junior highschool, college and university, all institutions. These components of the prepositional
phrases are identified with what has been discovered in the TreeTagger trigram ofb) IN+NN+SENT (Preposition or subordinating conjunction + Noun, singular or
mass + Sentence delimiter) and the CLAWS trigram of e) II+NN1+. (General
preposition + Singular common noun + Full stop). It could thus imply that thelearner frequently employs a trigram of VVI+NN1+II which precedes anothermulti-word unit: VVI+NN1+II+NN1+. (full stop). Furthermore, this unit is foundto co-occur with need to, which can expand the range of this set phrasing. This
― 18 ―

Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays
expansion of a set phrase agrees with the features of the idiom principle, as stated,that a semi-fabricated phrase has an indeterminate boundary (Sinclair 1991: 111). The
idiom principle focuses on the features of English used by native speakers, while thisfinding may endorse the application of the idiom principle to a learner language.
Table 8 Recurrent patterning of VVI+NN1+II+NN1+. in the learner corpus
TO

VVI

begin

learn

start

speak

need
want

to

study

NNI

II

NN1

Full Stop

futureschoolEnglishincollege.

university

use

world

VVI+NN1+II
1” I_PPIS1 will_VM learn_VVI English_NN1 in_II2start_VV0 to_TO

junior_JJ high_JJ school_NN1

learn_VVI English_NN1 in_II junior_JJ high_JJ school_NN1 .

3

do_VD0 I_PPIS1 learn_VVI English_NN1 in_II University_NN1

._.

I_P

4

desire_VV0 to_TO learn_VVI English_NN1 in_II workplace_NN1 ,_,we_PPIS2 s5need_VV0 to_TO speak_VVI English_NN1 in_II company_NN1 in_II

Japan_NP1

6

ease_VV0 to_TO speak_VVI English_NN1 in_II

7

need_VV0 to_TO speak_VVI English_NN1 in_II the_AT future_NN1

8

ided_VVD to_TO study_VVI English_NN1 in_II collage_NN1 ._.

Other_JJ rea

9egan_VVD to_TO study_VVI English_NN1 in_II junior_JJ high_JJ

school_NN1

the_AT world_NN1 ._.
._.

Many_
So_

10

have_VH0 to_TO study_VVI English_NN1 in_II junior_JJ

11start_VV0 to_TO study_VVI English_NN1 in_II junior_JJ high_JJ

high_JJ school_NN1

12

nted_VVD to_TO study_VVI English_NN1 in_II this_DD1

13

want_VV0 to_TO teach_VVI English_NN1 in_II the_AT future_NN1 ._.

If_CS

14

us_PPIO2 to_TO use_VVI English_NN1

We_PPISschool_NN1

university_NN1 ._.

in_II the_AT future_NN1 ._.

g) VVN+II+AT (Past participle of lexical verb + General preposition + Article)
An underused trigram in CLAWS to be compared to the native usage is VVN+II+AT,
which represents the equivalent three-word unit to VBN+IN+DT (Verb, past
participle + Preposition or subordinating conjunction + Determiner) in the TreeTagger
tagging. However, the extracts of this unit from the native corpus do not show
typical patterning of phrases, such as involved in the, which has been found in the― 19 ―

愛知大学

言語と文化

No. 27

results of TreeTagger. This is because TreeTagger tags involved as a past participle verb
whereas CLAWS assigns the word to a JJ (adjective) tag, resulting in its not being
identified as a past participle verb. This discrepancy may endorse the view thatmethods of assigning words to tags vary between taggers and the characteristics of atagger may lead to one system’s using a different tag from another.
VVN+II+AT in the native corpus
1

CST could_VM be_VBI adopted_VVN in_II2

NN1 should_VM be_VBI banned_VVN in_II the_AT United_NP1

the_AT U.K._NP1 It_PPH1 woul3

_AT brain_NN1 is_VBZ encased_VVN in_II the_AT skull_NN1 ,_, but_CCB

4

uld_VM become_VVI engulfed_VVN

Kingdom_N

in_II the_AT new_JJ Single_JJ Europe

5mb_NN1 ._.

6

ind_NN1 can_VM be_VBI found_VVN in_II the_AT fact_NN1 that_CST the_A

When_CS formed_VVN in_II the_AT womb_NN1 they_PPHS2

7

d_VVN to_TO be_VBI implanted_VVN in_II

8

not_XX originally_RR included_VVN in_II the_AT

the_AT mother_NN1 years_NNT

9

N1 ,_, originally_RR invested_VVN in_II the_AT Crown_NN1 ,_,today_R

10

PH1 may_VM be_VBI irradiated_VVN in_II the_AT cooking_NN1

process_N

11

ry_RG much_RR been_VBN left_VVN in_II the_AT dark_NN1 as_CSA their_

12

AT1 fuss_NN1 was_VBDZ made_VVN in_II the_AT newspapers_NN2

treaty_NN1 )_) ,_, gives

over_II

This section shows that the two taggers generate seven types of trigram to extract
typical multi-word units. It is also clear that the two taggers retain their unique
attributes, as should not be forgotten in the process of interpreting the trigrams.

4. Discussion and conclusion
This study had the purpose of making a quantitative and qualitative comparison
between a Japanese learner corpus and one by British students. Annotating thelearner corpus has enabled us to compare the environment of IN and has shown theoveruse and underuse of trigrams containing the preceding and following words of acertain preposition. As a result of this, there appear to be two distinctive features in
the writings by Japanese students: the overuse of the prepositional phrase containing
IN in the final position of a sentence; the overuse of to-infinitive before IN; and theunderuse of past participle before IN as a prepositional phrase. Reading theconcordance lines of the given trigrams reveals that Japanese learners tend to use IN― 20 ―

Tagging a Japanese Learner Corpus of English and Comparing Trigramswith Those in a Corpus of British Students’ Essays
before the nouns denoting places and times, such as college, school, society, university,
workplace, world, future and summer (Trigrams b and d). Likewise. the verbs
representing the actions in a location frequently occur with to-infinitive and IN(Trigram e), while at the same time the verbs signifying speech activity - say, speak,communicate - recur with IN in Japanese learners’ writings (Trigram e and f ). Notethat the topics which are chosen by learners for the compilation of a learner corpus
are highly likely to correlate to the frequency of certain words.
   Tagging the Japanese learner corpus in comparison to the British students’corpus has shed light on familiar and unfamiliar sequences co-occurring with IN
preposition to Japanese learners of English. A close reading of the concordance linesof Trigram d) and f ) has led to a recurrent patterning by Japanese learners: for
instance, . . . need to learn English in future. This finding endorses the ‘idiom principle’,
which is based on the analysis of native speakers’ text: ‘[m]any phrases haveindeterminate extent’ (Sinclair, 1991: 111–2). In this respect, Japanese learners show
the same features of phraseology as native speakers do. By contrast, the underuse of
the trigrams containing a past participle has appeared likely to be due to therestricted vocabularies of -ed in a passive clause. This limited variation conforms
insufficiently, in comparison to the native speakers’ corpus, to another phenomenonof the idiom principle: ‘[m]any phrases allow internal lexical variation’ (ibid. 111–2).
More descriptions on the phraseology of learner language are required in order toaccount for the creativity and strategies of learners.
   As has been seen, annotating a learner corpus may enrich the comparison with anative-speaker corpus from a quantitative viewpoint and may reveal a tendency oflanguage, which a ‘raw corpus’ (Leech, 1997: 4) that is, a non-annotated corpus, fails to
detect. Acknowledging the benefits, it should be remembered that a tagger is chosen
according to the target of research. The IN tag of TreeTagger may not be suitable for
examining preposition in phrases (Trigram a). Thus, a careful examination of tagsets
and concordance lines may avoid making misleading interpretations of annotation. Itshould be noted that misspelled words are likely to affect the tag matching; however,the drawback may be to some extent overcome by scrutinizing the tagged concordancelines. Admittedly, this limitation needs investigating and addressing in a further study.
   Differences between the written production by Japanese learners and by British
native speakers have been examined in this study. Drawing attention to such― 21 ―

愛知大学

言語と文化

No. 27differences may encourage language instructors in classroom to raise learners’
awareness of the internal lexical variation in a phrase and present learners with morevariations to promote greater flexibility. In addition, contrasting the phraseology of alearner group may help teachers to know which are the common features influenced
by the shared settings of the group, such as materials, teaching method and thetransfer from the first language. As regards learners, in particular at an advanced level,
the comparison of their phraseology with native ones could help an autonomousstudy of language learning by eliciting underlying features of their output.

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