=Paper=
{{Paper
|id=None
|storemode=property
|title=A New Corpus Resource for Studies in the Syntactic Characteristics of Terminologies in Contemporary English
|pdfUrl=https://ceur-ws.org/Vol-578/paper4.pdf
|volume=Vol-578
|dblpUrl=https://dblp.org/rec/conf/tia/FangCS09
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==A New Corpus Resource for Studies in the Syntactic Characteristics of Terminologies in Contemporary English ==
https://ceur-ws.org/Vol-578/paper4.pdf
A New Corpus Resource
for Studies in the Syntactic Characteristics
of Terminologies in Contemporary English
Alex C. Fang1, Jing Cao2 and Yang Song2
Dialogue Systems Group
Department of Chinese, Translation and Linguistics
City University of Hong Kong
Tat Chee Avenue, KowloonHong Kong SAR, PR China
1
acfang@cityu.edu.hk,
2
{cjing3,songyang2}@student.cityu.edu.hk
Abstract: In this paper, we present a new corpus resource that has been
constructed specially for the study of the syntactic characteristics of
terminologies. The corpus is based on the British component of the
International Corpus of English (ICE-GB), comprising four parallel subject
domains from two text categories (i.e. academic vs. popular prose) with a total
of about 200,000 running word tokens. The resource is richly annotated at
lexical, grammatical, syntactic, and terminological levels. It is also
parameterized according to both text categories and subject domains. The
corpus resource is expected to contribute towards a linguistically motivated
description of terms and their internal structures. It is also expected to provide
an analytical framework for the study of relations between terminological use
and text categories as well as subject domains.
Key words: syntactic tree, treebank, syntactic function, terminology, ICE-GB,
noun phrase, term annotation, corpus, syntax.
1 Introduction
Automatic term recognition (ATR) and extraction have been a challenging task
and encouraged rigorous efforts of researchers from a wide range of backgrounds
and disciplines. Nevertheless, past work on terminological extraction tends to focus
on specific subject domains, and mainly in the field of biochemistry and medicine
such as Ananiadou et al. 2000, Nenadic et al. 2005, Aubin and Hamon 2006, and
Ville-Ometz et al. 2007, to name just a few. Some work on other domains such as
computing (e.g. Eumeridou et al. 2004; L’Homme 2002; Nakagawa and Mori 2003),
economy (e.g. Rodriguez et al. 2007), and legislation (e.g. Ha et al. 2008; Kit and Liu
2008). Those studies are domain specific in a good sense that they concentrate on
domain-specific issues like domain knowledge and associated knowledge expressions
on the lexical level. Yet they are domain limited in an undesirable sense, which leads
to difficulty in evaluating the performance and interoperability of the existing term
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recognition systems across a set of different domains. Additionally, it remains an
issue how such systems will adapt to new domains.
Another noticeable issue is that, among the linguistic features employed in ATR
systems, syntactic features have been mainly observed at the phrasal level, and
seldom from the perspective of syntactic structures at a clausal level. Grammatical
patterns, such as ‘noun’, ‘noun + noun’, ‘adjective + noun’, ‘noun + preposition +
noun’, have been integrated with statistic measurements to determine the termhood
(e.g. Frantzi et al. 2000; Pazienza et al. 2005). Eumeridou et al. (2004) go beyond the
grammatical patterns and examine how term occurrences correlate the argument
structure of verbs across three domains chosen from the British National Corpus.
Their findings show an uneven distribution of terms in different argument structures1,
and they also notice the influence that different domains have upon term occurrences.
Although the study focuses on the verbal syntax only, it does indicate that syntactic
features of terminological entities warrant a worthwhile research topic and that text
categories such as registerial types and subject domains should also be a parameter to
consider. It is reasonable to believe that further improvement of ATR systems can be
achieved by exploring deeper, linguistically motivated analysis of the relation
between terminologies and linguistic parameters.
The main focus of this paper is to present a new corpus resource that has been
constructed specially for the study of the syntactic characteristics of terminologies.
Existing term-annotated corpora are typically domain-specific, such as GENIA (Ohta
et al. 2002), and typically used as a resource for statistical training. The new corpus
resource is different in that it is built on general domains and is richly annotated for
syntactic information, especially for detailed annotation of the syntactic categories
and their functions within the clause complex that is often dependent on verb sub-
categorization. The corpus is based on the British component of the International
Corpus of English (ICE-GB), comprising four parallel subject domains from two text
categories (i.e. academic vs. popular prose) with a total of about 200,000 running
word tokens. The resource is richly annotated at lexical, grammatical, syntactic, and
terminological levels. It is also parameterized according to both text categories and
subject domains. The tree bank is expected to contribute towards a linguistically
motivated description of terms and their associated syntactic structures. It will also
provide an analytical framework for the study of relations between terminological use
and text types as well as subject domains. The richly annotated trees will facilitate
studies in the linguistic relations of terms for the purpose of ontology construction.
In the rest of this paper, we will first of all describe the construction of the corpus,
including the selection of the corpus material, the annotation schemes for grammar
and syntax, and an inter-annotator analysis of the manual annotation of terms. We
shall then report some of our initial empirical observations of the syntactic
characteristics of noun phrases (NP) that are terminological entities as opposed to
generic NPs across different types and domains. For this purpose, we will describe the
distribution of general NPs in terms of text categories and subject domains. We will
1
In lexical semantic terms, argument structure refers to the semantic type of the verb and its related
elements such as agent and theme. The same term is also loosely used in syntax to refer to the sub-
categorisation, or valency structure or complémentation type of verbs.
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A New Corpus Resource for Studies in the Syntactic Characteristics of Terminologies
in Contemporary English
then describe the distribution of terminological NPs according to the same parameters,
focusing on their syntactic functions in the tree structure.
2 Corpus Construction
2.1 Corpus resource for term annotation
Our on-going research attempts to extend the previous studies by exploring the
syntactic characteristics of terminological entities across different text types and
subject domains in contemporary English. To achieve our objectives, the British
component of the International Corpus of English (ICE-GB; Greenbaum 1996) was
chosen as a basis for the following reasons: First, it is encoded for a variety of text
categories and subject domains. Secondly, it is already grammatically tagged,
syntactically parsed and manually validated. Finally and most importantly, it is
annotated with a rich set of linguistically motivated syntactic relations that will
maximally enhance our intended study. The following sections will first describe the
resource created from the ICE-GB and introduce its part-of-speech (POS) and
syntactic annotations.
2.1.1 Creation of a sub-corpus
The British component of the International Corpus of English (ICE-GB) is a one-
million-word corpus comprising both spoken and written British English from the
1990s (Greenbaum 1996; Fang 2007). The spoken section represents 60% of the total
size of the corpus with 300 sample texts. The written section accounts for 40% of the
corpus with 200 texts. Each component text has about 2,000 word tokens. Table 1
summarizes the text categories in the ICE-GB together with the number of component
texts.
Table 1. The structure of ICE-GB
Spoken Written
Private 100 Student writing 20
Dialogue Non-printed
Public 80 Correspondence 30
Unscripted 70 Informational 100
Monologue Mixed 20 Instructional 20
Printed
Scripted 30 Persuasive 10
Creative 20
Given the purpose of our study, texts from the category of informational writing
constitute a suitable source of texts, which is further divided into three sub-categories:
academic writing, popular writing and press news reports. Two contrastive text types,
i.e., academic writing and popular writing, were chosen. The two text types cover four
parallel subject domains comprising ten texts each. Table 2 presents the composition
of the sub-corpus created from ICE-GB.
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Table 2. The structure of the sub-corpus
Text Type Subject Domain Domain Code # of Texts # of Words
Humanities AHUM 10 24,363
Academic Social sciences ASOC 10 24,280
writing Natural sciences ANAT 10 24,165
Technology ATEC 10 23,386
Humanities PHUM 10 27,168
Popular Social sciences PSOC 10 23,110
writing Natural sciences PNAT 10 23,150
Technology PTEC 10 23,584
Total 80 193,206
As can be seen from Table 2, the sub-corpus comprises 80 texts similar in size with a
total number of 193,206 word tokens.
2.1.2 Tree annotations in the ICE-GB
All the texts in ICE-GB are richly annotated grammatically and syntactically
(Fang 1996, 2000, 2006, 2007). When the 80 texts from ICE-GB were selected to
create the sub-corpus, a treebank was effectively created that comprises 8,306
syntactic trees.
PU CL(main,montr,pass,pres)
SU NP()
DT DTP()
DTCE ART(def) {The}
NPHD N(com,plu) {fibres}
NPPO PP()
P PREP(ge) {of}
PC NP()
NPHD N(com,sing) {group B}
VB VP(montr,pres,pass)
OP AUX(pass,pres) {are}
MVB V(montr,edp) {found}
A PP()
P PREP(ge) {in}
PC NP()
DT DTP()
DTCE ART(def) {the}
NPPR AJP(attru)
AJHD ADJ(ge) {autonomic}
NPPR AJP(attru)
AJHD ADJ(ge) {nervous}
NPHD N(com,sing) {system}
PUNC PUNC(per) {.}
Fig. 1 – An example of syntactic annotations in the ICE-GB
As noted in Figure 1 above, the tree structure is richly annotated with fine-grained
grammatical and syntactic information. At the grammatical level, words are coded
with part-of-speech (POS) tags that include a head tag (such as nouns, verb, and
adjectives) with a set of attributes indicating the subcategorizations of the head tag.
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A New Corpus Resource for Studies in the Syntactic Characteristics of Terminologies
in Contemporary English
For instance, the verb found enclosed within a pair of curly brackets is tagged as
V(montr,edp), namely, a mono-transitive verb in past participial form. As another
example, {The} is assigned a label ART(def), meaning it is a definite article, and
{fibres} is a common noun in its plural form. Syntactically, each node comprises
two labels: one representing its syntactic category (such as noun phrase and adjective
phrase) and the other the syntactic function. Take the node SU NP() as an example,
which indicates that it is a noun phrase (NP) functioning as the subject (SU) of the
clause. The same NP comprises a determiner (DT), the head (NPHD) and a post-
modifier (NPPO). The definite article The constitutes the central determiner (DTCE), a
daughter node of DT. See Appendix for a complete list of all the parsing symbols.
With such a system of syntactic categories and their associated syntactic functions,
the corpus forms a valuable testbed according to which grammatical relations of
various kinds can be investigated. The syntactic framework will also form an
informative context within which terms and term relations can be usefully examined.
2.2 Term annotation
Term annotation was carried out manually during a period of four months, and has
gone through the following procedures:
• Training of the annotators: The training session helps the annotators get
familiar with the special format of the target texts, which are parsed and
represented in a form exemplified in Fig. 1.
• Analysis of inter-annotator agreement: This step was taken to establish the
consistency and therefore the quality of the annotations by the three different
annotators given the same text, and a higher statistic agreement will
demonstrate the confidence of the manual annotation.
• Actual annotation: With an annotation guideline, annotators mark up the
terms with the help of dictionaries, online dictionaries and term banks.
• Manual examination of terminological annotations.
In the remaining of this section, we shall first describe the annotation guideline and
then report the results from the inter-annotator agreement test. The basic statistics of
the terminologically annotated corpus resource will be presented in Section 3.
2.2.1 Annotation guideline
Before describing the guideline, we first introduce the operational definition of
terminological entities. To our understanding, terms by definition primarily
correspond to noun-phrase (NP) groups and thus consist of words that are single
nouns or complex noun phrases (Kageura et al. 2004; Nakagawa 2001; Nakagawa and
Mori 2003). Following Eumeridou et al. (2004), we also consider terms in a
pragmatic sense. Take text w2a-031 for example. The text is about “blind shaft
drilling” under the domain of technology. In addition to terms in technology and
engineering, we may also mark up terminological entities from related domains such
as environment. Given such a definition, a working guideline for annotation was made:
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• Among the NPs, proper names of places, countries, organizations or
institutes are excluded from the current study, and therefore, will not be
annotated.
• Variant terms will be annotated.
o Singular and plural forms of a term will both be regarded as terms
in case some termbanks only collect singular form of a term.
o When an N1+N2 compound is a term, the sequence N2 + of + N1 will
also be treated as a term.
o Variant spellings of the same term will be accepted.
• With nested terms, we only mark up the longest part as a multi-word term.
• Terms are marked with ‘<’ at the beginning and ‘>’ at the end in the tree
diagram, and the resulting NP is described by an additional attribute ‘term’.
See Figure 2.
PU CL(main,montr,pass,pres) A PP()
SU NP(term) P PREP(ge) {in}
DT DTP() PC NP(term)
DTCE ART(def) {The} DT DTP()
NPHD N(com,plu) {} DTCE ART(def) {the}
NPPO PP() NPPR AJP(attru)
P PREP(ge) {of} AJHD ADJ(ge) {}
Fig. 2 – Examples of term annotations in the tree structure
2.2.2 Inter-annotator agreement
Three annotators were trained to mark up terms. All the three annotators are
university students majoring in linguistics. Among them, two are undergraduates who
have been admitted to postgraduate study and one is a PhD candidate. To measure the
inter-annotator agreement, two texts were taken from the pre-selected sub-corpus
from ICE-GB, with a total number of about 4,000 words. During the annotation stage,
the annotators were allowed to refer to the guideline or other sources such as online
termbanks and dictionaries, in addition to their linguistic knowledge. They were not
allowed to confer with each other over the annotation.
We then compared the annotations among the three annotators by using F score,
which is considered to be a standard measure to determine the inter-annotator
agreement (Corbett et al. 2007) and has been commonly used in previous studies (see,
for example, Demetriou and Gaizauskas 2003, Morgan et al. 2004, Vlachos and
Gasperin 2006 and Kolarik 2008). Therefore, the inter-annotator agreement was
computed pair-wise using a measure defined in (1):
(1)
6
A New Corpus Resource for Studies in the Syntactic Characteristics of Terminologies
in Contemporary English
where M1 and M2 are the number of markable terms in a given text marked up by
Annotators 1 and 2 respectively, and C is the total number of times both annotators
agree on a markable term in that same text. To calculate the F score, the total number
of terms marked by annotators A, B, and C were counted respectively. Next, all of
the exact matches were found and counted. For an exact match, the left and right
boundaries had to match entirely.
Table 3. A summary of the inter-annotator agreement
Paired # of Terms
Annotator # of Terms F Score
Annotators in Common
A 604 A-B 575 95.99%
B 594 A-C 576 96.16%
C 594 B-C 584 98.32%
Table 3 summarizes the inter-annotator agreement. Annotators A, B and C
respectively identified 604, 594 and 594 terms independently. The total number of
commonly identified terms is given for paired annotators. All the F scores for each
paired annotators all above 95%, suggesting a high level of inter-annotator agreement.
The results suggest that a high level of agreement is possible by training and by
referring to the annotation guideline. Such a finding shows that trained annotators can
achieve a high level of consistency even without expert domain knowledge, a finding
that is contrary to the past experience that extensive training is needed for consistent
annotation of terms in specialized domains such as biochemistry and medicine.
After the inter-annotator agreement test, the three annotators carried out the actual
annotation and met to discuss the uncertain situations when necessary. Finally, the
annotated corpus was manually validated by one annotator with the help of online
resources and specialized dictionaries.
3 Syntactic Features of NP Constructions
In this section, we present some initial descriptive statistics and chart the
distribution of NP constructions across different text categories and domains. We will
first explain how we retrieve the syntactic functions of NPs according to the tree
structure, followed by a description of the basic statistics of NP constructions in the
corpus. We shall then present the preliminary observations of the syntactic features of
NPs that are marked as terms.
3.1 A general description of NP constructions by category and domain
As explained in Section 2.1.2, every NP is assigned a function label and additional
attributes if necessary. To count the frequency of NP constructions in trees is
straightforward in most cases except for two scenarios, where the functions are
labeled as CJ (conjoin; see Fig. 3) and DEFUNC (appositive NP that does not perform
any syntactic function; see Fig. 4). In Fig. 3, the direct object NP is described by the
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attribute coordn, indicating the presence of a coordinated construction whose
conjoins are marked as CJ. In such a scenario, a CJ will inherit the function of its
mother node and be counted as a separate OD NP. Therefore, instead of counting one
OD and two CJ functions, we count two OD functions for the NPs in Fig 3. Similarly,
NPs with DEFUNC labels are also relocated and assigned the function label of the
governing NP. See Fig. 4 for an example, where DEFUNC NP is treated as SU NP. In this
particular case, instead of one DEFUNC and one SU, two SU functions are counted.
OD NP(coordn)
CJ NP() SU NP()
DT DTP() DT DTP()
DTCE ART(def) {the} DTCE ART(def) {the}
NPHD N(com,plu) {gods} NPHD NADJ(sing) {unconscious}
COOR CONJUNC(coord) {and} DEFUNC NP(appos)
CJ NP() NPHD PRON(ref,sing) {itself}
NPHD N(com,plu) {customs}
Fig. 4 – An example of DEFUNC NP
Fig. 3 – An example of CJ NP
Table 4. Summary of NP constructions
AHUM ASOC ANAT ATEC PHUM PSOC PNAT PTEC
Function Freq Freq Freq Freq Freq Freq Freq Freq
A 26 40 14 66 54 61 60 40
AJPR 0 1 13 10 7 1 3 4
AVPR 3 6 9 3 16 11 11 8
CO 15 3 11 8 21 9 8 6
CS 215 147 144 184 260 180 190 172
DT 210 64 13 32 174 98 69 58
ELE 176 77 97 193 242 52 60 200
FOC 17 4 4 6 4 8 9 3
NPPO 250 150 419 237 246 59 32 99
NPPR 1 10 23 21 15 13 14 12
OD 850 806 634 778 924 951 812 947
OI 15 13 1 7 31 27 12 9
PC 3138 2982 3301 2834 3060 2585 2807 2356
PMOD 0 0 4 2 4 2 4 1
PROD 1 4 0 1 1 1 1 4
PRSU 33 53 39 37 29 55 32 42
SU 1685 1640 1626 1597 1986 1957 1859 1850
Total 6635 6000 6352 6016 7074 6070 5983 5811
With this treatment of conjoin and appositive NPs, NP constructions in all the
eight subject domains were retrieved and summarized in Table 4. As can be observed
in Table 4, there is an uneven distribution of 17 different functions of NPs across
domains. In general, NPs seem to occur most frequently at the position of PC in all the
domains, followed by SU and OD. Nevertheless, when we examine the functions by
category and domain, we notice more interesting patterns. First, NPs in domains of
academic writing tend to occur less frequently at the position of SU than those in their
counterparts of popular writing. Second, domains in academic writing are more likely
8
A New Corpus Resource for Studies in the Syntactic Characteristics of Terminologies
in Contemporary English
to have a comparatively higher occurrence of PC as a syntactic function than their
counterparts in popular writing. They also tend to have fewer occurrences of OD.
3.2 A statistical description of term-NP constructions
When examining the distribution of term-NPs, we also related the CJ and DEFUNC
functions to their mother nodes. Accordingly, the actual distribution of term-NPs
across difference categories and domains were calculated and presented in Table 5.
Table 5. Summary of term-NP constructions
AHUM ASOC ANAT ATEC PHUM PSOC PNAT PTEC
Function Freq Freq Freq Freq Freq Freq Freq Freq
A 4 3 1 16 2 1 1 5
AJPR 0 0 2 4 0 1 0 1
AVPR 0 0 0 1 0 1 1 0
CO 12 1 10 5 7 4 4 4
CS 106 48 63 76 85 55 68 36
DT 140 29 8 20 73 54 40 35
ELE 16 35 40 47 56 14 42 92
FOC 12 1 3 4 2 0 6 2
NPPO 10 14 7 5 10 1 1 3
NPPR 0 7 14 9 2 5 11 6
OD 456 341 316 408 316 331 379 480
OI 5 5 0 45 6 4 4 2
PC 1637 1435 1886 1496 1043 982 1199 1082
SU 510 536 753 654 422 442 673 621
Total 2908 2455 3103 2790 2024 1895 2429 2369
Interesting features emerge from the initial frequency count. First, academic
writing tends to have more terms than popular writing in both parameters (i.e.
category and domain). In a broad sense, the total number of terms in academic writing
is higher than that of popular writing. From the perspective of subject domains,
individual domains belonging to academic writing tend to have more terms than their
counterparts in popular writing. Such a result suggests that formal writing tends to
contain more term candidates than informal writing. Second, science domains (i.e.
NAT and TEC) tend to contain more terms than arts domains (i.e. HUM and SOC). It
can be also noticed that the number of terms in AHUM is higher than that of ATEC,
and it is understandable since AHUM has the highest number of NPs among the
domains in academic writing. Third, across the eight domains term-NPs seem to
appear most frequently at the position of PC, followed by SU and OD. Fourth, it would
be easy to make a contrastive study on certain syntactic functions across the eight
domains. For example, terms are more likely to occur at the position of A in ATEC
when compared with the other seven domains, and they are more likely to appear at
the position CS in AHUM when examined across domains. Such information can be
taken as a flexible value in assigning weights to syntactic functions in accordance
with particular domains in ATR.
9
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It is worth mentioning that syntactic labels at the phrasal level can be further
classified at the clausal level. For example, a considerable number of NPs occur at the
position of PC, which should be related to its mother node, namely PP, whose
functions could be analyzed differently as A PP and NPPO PP, revealing further
variations of use across the eight categories.
4 Conclusion
In this paper, we presented a new corpus resource that has been constructed
specially for the study of the syntactic characteristics of terminologies for a
linguistically motivated description of terms and their internal structures. The corpus
is based on the British component of the International Corpus of English, comprising
four parallel subject domains from two text categories (i.e. academic vs. popular
prose) with a total of about 200,000 running word tokens. It is richly annotated at
lexical, grammatical, syntactic, and terminological levels. It is parameterized
according to both text categories and subject domains. We first described the
construction of the corpus, including the selection of the corpus material, the
annotation schemes for grammar and syntax, and an inter-annotator analysis of the
annotation of terms. We then described the corpus resource by reporting some of our
initial empirical observations of NP constructions and term-NP constructions.
Interesting patterns were observed in terms of syntactic distribution of NPs and term-
NPs across different categories and domains. In particular, term-NPs show observable
difference across different categories and domains. In other words, the corpus
resource can provide an analytical framework for the study of relations between
terminological use and text types as well as subject domains.
Acknowlegement
The work described in this paper was supported partially by research grants (Nos
7002190, 7200120 and 7002387) from City University of Hong Kong. The authors
would like to thank the reviewers for their valuable comments and suggestions.
References
ANANIADOU, S., ALBERT S. & SCHUHMANN D. (2000). Evaluation of Automatic Term
Recognition of Nuclear Receptors from MEDLINE. Genome Informatics 11. p. 450-451.
AUBIN, S. & HAMON T. (2006). Improving Term Extraction with Terminological Resources. In
FinTAL 2006, LNAI 4139. p. 380-387.
CORBETT, P., BATCHELOR, C. & TEUFEL, S. (2007). Annotation of Chemical Named Entities. In
Proceedings of BioNLP 2007: Biological, translational, and clinical language processing. p.
57-64.
10
A New Corpus Resource for Studies in the Syntactic Characteristics of Terminologies
in Contemporary English
DEMETRIOU, G. & GAIZAUSKAS, R. (2003). Corpus resources for development and evaluation of
a biological text mining system. In Proceedings of the Intelligent Systems for Molecular
Biology (ISMB) Workshop on Text Mining (BioLINK2003).
EUMERIDOU, E., NKWENTI-AZEH, B. & MCNAUGHT, J. (2004). An Analysis of Verb
Subcategorization Frames in Three Special Language Corpora with View towards Automatic
Term Recognition. Computers and the Humanities, 38. p. 37-60.
FANG, A.C. (1996). AUTASYS: Automatic Tagging and Cross-Tagset Mapping. In S.
GREENBAUM Ed. p. 110-124.
FANG, A.C. (2000). From Cases to Rules and Vice Versa: Robust Practical Parsing with
Analogy. In Proceedings of the Sixth International Workshop on Parsing Technologies. p. 77-
88.
FANG, A.C. (2006). Evaluating the Performance of the Survey Parser with the NIST Scheme. In
LNCS 3878: Computational Linguistics and Intelligent Text Processing, ed. by A. Gelbukh.
Berlin Heidelberg: Springer-Verlag. P. 168-179.
FANG, A.C. (2007). English Corpora and Automated Grammatical Analysis. Beijing: The
Commercial Pess.
FRANTZI, K., ANANIADOU, S. & MIMA, H. (2000). Automatic recognition of multi-word terms:
the C-value/NC-value method. International Journal of Digital Libraries, 3(2). p.117-132.
GREENBAUM, S. (1996). Comparing English World: The International Corpus of English.
Oxford: Oxford University Press.
HA, L. A., MITKOV, R. & CORPAS, G. (2008). Mutual Terminology Extraction Using a Statistical
Framework. In Procesamiento del lenguaje natural. N. 41. p. 107-112.
KIT, C. & LIU, X. (2008). Measuring Mono-word Termhood by Rank Difference via Corpus
Comparison. Terminology 14:2. p. 204-229.
KOLÁIK, C. & KLINGER, R. (2008). Chemical Names: Terminological Resources and Corpora
Annotation. LREC 2008 Workshop: Building and Evaluating Resources for Biomedical Text. p.
51-58.
L'HOMME, M.C. (2002). What can Verbs and Adjectives Tell us about Terms? In Proceedings
of Terminology and Knowledge Engineering (TKE 2002). p. 65-70. France.
MORGAN, A.-A., HIRSCHMAN, L., COLOSIMO, M., YEH, A.-S. & COLOMBE, J.-B. (2004). Gene
Name Identification and Normalization using a Model Organism Database. Journal of
Biomedical Informatics, 37(6). p. 398-410.
NAKAGAWA, H. & MORI, T. (2003). Automatic term recognition based on statistics of
compound nouns and their components. Terminology, 9 (2). p. 201-219.
NENADIC, G., SPASIC, I., & ANANIADOU, S. (2005). Mining Biomedical Abstracts: What’s in a
Term? In K.-Y. Su, J. Tsujii, J.-H. Lee and O. Kwong (Eds.) Natural Language Processing –
IJCNLP 2004 First International Joint Conference, Lecture Notes in Computer Science vol.
3248. p. 797-806.
OHTA, T., TATEISI, Y., MIMA, H. & TSUJII, J. (2002). GENIA Corpus: An Annotated Research
Abstract Corpus in Molecular Biology Domain. In Proceedings of the Human Language
Technology Conference, San Diego, USA.
PAZIENZA, M.T., PENNACCHIOTTI, M. & ZANZOTTO, F.M. (2005). Terminology Extraction: An
Analysis of Linguistic and Statistical Approaches. StudFuzz 185. p. 255-279.
RODRÍGUEZ, B., MARIO, F., NOYA, E. D., OTERO, P.G., MARTÍNEZ, M.L., MATO, E. M.M., ROJO,
G. SANTALLA DEL RÍO, M.P., & DOCÍO, S.S. (2007). A Corpus and Lexical Resources for Multi-
word Terminology Extraction in the Field of Economy in a Minority Language. In Proceedings
of the 3rd Language and Technology Conference. p. 359-363.
VILLE-OMETZ, F., ROYAUTÉ, J. & ZASADZINSKI, A. (2007). Enhancing automatic recognition
and extraction of term variants with linguistic features. Terminology 13 (1). p. 35-59.
11
TIA’09
VLACHOS, A. & GASPERIN, C. (2006). Bootstrapping and Evaluating Named Entity Recognition
in the Biomedical Domain. In Proceedings of BioNLP in HLT-NAACL. p. 138-145.
Appendix: A Complete List of Parsing Symbols
A Adverbial INDET Indetermined
ADJ Adjective INTOP Interrogative operator
ADV Adverb INVOP Inversion operator
AJHD Adjective phrase head LIM Limitor
AJP Adjectve phrase LK Linker
AJPO Adjective phrase postmodifier MVB Main verb
AJPR Adjective phrase premodifier N Noun
ANTIT Anticipatory it NADJ Nominal adjective
ART Article NONCL Non-clause
AUX Auxiliary NOOD Notional object
AVB Auxiliary verb NOSU Notional subject
AVHD Adverb phrase head NP Noun phrase
AVP Adverb phrase NPHD Noun phrase head
AVPO Adverb phrase postmodifier NPPO Noun phrase postmodifier
AVPR Adverb phrase premodifier NPPR Noun phrase premodifier
CF Focus complement NUM Numeral
CJ Conjoin OD Direct object
CL Clause OI Indirect object
CLEFTIT Cleft it OP Operator
CLOP Cleft operator P Prepositional
CO Object complement PARA Paratactic
CONJUNC Conjunctor PC Prepositional complement
CONNEC Connector PMOD Preposition premodifier
COOR Coordinator PP Prepositiional phrase
CS Subject complement PRED Predicate
CT Transitive complement PREDG Predicate group
DEFUNC Detached function PREP Preposition
DISMK Discourse marker PROD Provisional object
DISP Disparate coordination PROFM Pro-nominal form
DT Determiner PRON Pronoun
DTCE Central determiner PRSU Provisional subject
DTDE Deterred determiner PRTCL Particle
DTP Determiner phrase PS Stranded preposition
DTPE Pre-determiner PU Parsing unit
DTPO Determiner postmodifier PUNC Punctuation
DTPR Determiner premodifier REACT Reactional signal
DTPS Post-determiner SBMO Subordinator phrase premodifier
ELE Clause element SU Subject
EXOP Existential operator there SUB Subordinator
EXTHERE Existential there SUBHD Subordinator phrase head
FOC Focus SUBP Subordinator phrase
FRM Formulaic expression TO Infinitive to
GENF Genitive function V Verb
GENM Genitive marker VB Verbal
IMPOP Imperative operator VP Verb phrase
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