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# Automatic construction of a TMF Terminological Database using a transducer cascade

Chihebeddine Ammar, Kais Haddar, Laurent Romary

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Chihebeddine Ammar, Kais Haddar, Laurent Romary. Automatic construction of a TMF Terminological Database using a transducer cascade. RANLP-2015, Sep 2015, Hissar, Bulgaria. hal-01276816

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# Automatic construction of a TMF Terminological Database using a transducer cascade

Chihebeddine Ammar

MIRACL & Sfax University
Sfax, Tunisia

chihebeddine.ammar@fss.rnu.tn

Kais Haddar

MIRACL & Sfax University
Sfax, Tunisia

kais.haddar@fss.rnu.tn

Laurent Romary

INRIA & Humboldt University
Berlin, Germany

laurent.romary@inria.fr

## Abstract

The automatic development of terminological databases, especially in a standardized format, has a crucial aspect for multiple applications related to technical and scientific knowledge that requires semantic and terminological descriptions covering multiple domains. In this context, we have two challenges: the first is the automatic extraction of terms in order to build a terminological database, and the second challenge is their normalization into a standardized format. To deal with these challenges, we propose an approach based on a cascade of transducers performed using CasSys tool of Unitex platform that benefits from both: the success of the rule-based approach for the extraction of terms, and the performance of the TMF standard for the representation of terms. We have tested and evaluated our approach on an Arabic scientific and technical documents for the Elevator domain and the results are very encouraging.

## 1 Introduction

The automation of terminology will reduce the time and cost that usually takes terminological database construction. It will also help us to construct terminological databases with broad coverage, especially for recent concepts and poor language coverage (Arabic for example). On the other side, the representation of terminological data in a standard format allows the integration and merging of terminological data from multiple source systems, while improving terminological data quality and maintaining maximum interoperability between different applications.

One of the very rich in terminology working area are the scientific and technical documents.

They cover several scientific and technical fields. That is why we will need several terminological databases, one for each field. For this reason, we decided to work on a specific domain: the elevators.

To automate any process, we need a framework. The choice of this framework is not an easy task. In fact, many frameworks exist, based on: formal grammars, logical formalism, discrete mathematics, etc. The rule-based approach requires: a thorough study of the characteristics of terms and construction of necessary resources such as dictionaries, trigger words and extraction rules.

Finite automata and in particularly transducers are often used in the Natural Language Processing (NLP). The general idea is to replace the rules of formal grammars with representation forms. Transducers offer a particularly nice and simple formulation, and prove their capability of representing complex grammars due to their graphic representation. They have a success for the extraction of named entities (NE) and terms. In fact, precision is more important for rule-based systems.

Another issue is to decide which standard will we choose to model our terminological databases, which standard will best represent scientific and technical terms and which model to use, onomasiological or semasiological?

Our main objective is to create a standardized terminological resource form a corpus of Arabic scientific and technical documents (patents, manuals, scientific papers) able to support automatic text processing applications. Our approach is based on a cascade of transducers performed using CasSys tool of Unitex. It aims to extract and annotate under standardized TMF - Terminological Markup Framework form technical terms of a specific field: the case of lifts field. The first step is a pre-treatment consisting on resolving some problems of the Arabic language (e.g. agglutination). The second step is to extract and annotate terms.
And the final one is a post-treatment consisting of
cleaning documents.

This paper is organized as follows. Section 2 is
devoted to the presentation of the previous work.
In section 3, the characteristics of Arabic scientific
and technical terms. We present, in section 4, we
argue the choice of terminology model. In section
5, we present our approach. Section 6 is devoted to
experimentation and evaluation and we conclude
and enunciate some perspectives in section 7.

## 2 Previous work

Three methods for building a terminological
knowledge base exist: manual, semi-automatic
and automatic. In the literature, there are some ter-
minological databases for scientific and technical
fields, most of them were constructed manually or
semi-automatically.

For instance, the multilingual terminology of
the European Union, IATE<fnref n="1" />, contains 8,4 million
terms in 23 languages covering EU specific ter-
minology as well as multiple fields such as agri-
culture or information technology. The multilin-
gual terminology portal of the World Intellectual
Property Office, WIPO Pearl<fnref n="2" />, gives access to sci-
entific and technical terms in ten languages, in-
cluding Arabic, derived from patent documents. It
contains 15,000 concepts and 90,000 terms. Since
WIPO has not a collection of Arabic patents, Ara-
bic terms are often translations of the WIPO trans-
lation service. In (Lopez and Romary, 2010b),
the authors developed a multilingual terminologi-
cal database called GRISP covering multiple tech-
nical and scientific fields from various open re-
sources.

Three main approaches are generally followed
for extraction: rule-based (or linguistic) approach,
training based (or statistic) approach and hybrid
approach. What distinguishes the approaches
mentioned, is not the type of information consid-
ered, but their acquisition and handling. The lin-
guistic approach is based on human intuition, with
the manual construction of analysis models, usu-
ally in the form of contextual rules. It requires a
thorough study of the types of terms, but it has a
success for the extraction of NE and terms. In fact,
precision is more important for symbolic systems.

In previous work on non scientific and techni-
cal documents, there are those who used linguis-

tically methods based on syntactic analysis (see
for instance (Bourigault, 1992) and (Bourigault,
1994)). But the most used approach is a hybrid
approach combining statistical and linguistic tech-
niques (Dagan and Church, 1994).

The most recent work on scientific and technical
documents were mainly based on purely statistical
approaches. They used standard techniques of in-
formation retrieval and data extraction. Some of
them use machine learning tools to extract header
metadata using support vector machines (SVM)
(Do et al., 2013), hidden markov models (HMM)
(Binge, 2009), or conditional random fields (CRF)
(Lopez, 2009). Others use machine learning tools
to extract metadata of citations (Hetzner, 2008),
tables (Liu et al., 2007), figures (Choudhury et al.,
2013) or to identify concepts (Rao et al., 2013).
All these approaches rely on previous training and
natural language processing.

The need to allow exchanges between reference
formats (Geneter, DXLT, etc.) has brought to the
birth of the standard ISO 16642, TMF, specifying
the minimum structural requirements to be met by
every TML (terminological Markup Language).

## 3 Characteristics of Arabic scientific and technical terms

Our study corpus contains 60 Arabic documents: 50 patents, 5 scientific papers and 5 manuals and installation documents of elevators collected from multiple resources: manuals from the websites of elevator manufacturers, patents from multiple Arabic intellectual property offices and scientific papers from some Arabic journals. All of these documents are text files and contain a total number of 619k tokens.

This corpus will allow us to construct the neces-
sary resources such as dictionaries, trigger words
and extraction rules and to study the characteris-
tics of Arabic terms. Indeed, we noted the exist-
ence of some semantic relationships among terms
of our collection, such as synonymy.

In fact, some terms have the same signified and
different signifiers. For example, مصعد بدون
مصعد بدون وزن معادل وزن عکسی
"elevator without counterweight". Here, the two
terms have the same part (مصعد بدون وزن)
"elevator without weight") and two synonymous
words (مصعد بدون وزن معادل "equivalent" and عکسی "reverse").
Another type of semantic relationships is the hi-

<footnote n="1">¹http://iate.europa.eu</footnote>

<footnote n="2">²http://www.wipo.int/wippearl/search/home.html</footnote>
erarchical relationship in two ways. Firstly, from
the generic term to the specific term(s) (from hy-
peronym to hyponym). For example, hyperonym:

In Arabic texts, some factors make there auto-
matic analysis a painful task, such as: the agglu-
tination of Arabic terms. In fact, the Arabic lan-
guage is a highly agglutinative language from the
fact that clitics stick to nouns, verbs, adjectives
which they relate. Therefore, we find particles that
stick to the radicals, preventing their detection. In-
deed, textual forms are made up of the agglutina-
tion of prefixes (articles: definite article ال "the",
prepositions: ل "for", conjunctions: و "and"), and
suffixes (linked pronouns) to the stems (inflected
forms: أبواب "its doors", أبواب "doors" + ه "its").

Another problem is the ambiguity which may
be caused by several factors. For example, Arabic
language is one of the Semitic languages that is
defined as a diacritized language. Unfortunately,
diacritics are rarely used in current Arabic writing
conventions. So two or more words in Arabic can
be homographic. such as يُعَدّ "return", يُعَدّ "pre-
pare", يُعَدّ "count".

Despite documents of our corpus are in Arabic
language, some of them have a literal translation
of key terms and technical words. These transla-
tions can be in English or French and are usually
of a very high quality because they are made by
professional human translators. They facilitate the
task of our terminological database implementa-
tion (Language Section and Term Section of the
TMF model) and make it multilingual.

4 TMF Terminological Model

The terminology is interested in what the term
means: notions, concepts, and words or phrases
that they nominate. This is the notional or con-

ceptual approach. Motivated from the industrial
practice terminology, the Terminological Markup
Framework (TMF<fnref n="3" />) (Romary, 2001) was devel-
oped as a standard for onomasiological (sense to
term) resources. In this paper, we need a generic
model able to cover a variety of terminological re-
sources. That is why we consider that the stan-
dard TMF is the most appropriate for our termi-
nological database. The meta-model of the stan-
dard TMF is defined by logical hierarchical lev-
els. It thus represents a structural hierarchy of the
relevant nodes in linguistic description. The meta-
model describes the main structural elements and
their internal connections.

It is combined with data categories (ISO
12620⁴) from a data category selection (DCS). Us-
ing the data model based on ISO 16642 allows
us to fulfill the requirements of standardization
and to exploit Data Category Registry (DCR) fol-
lowing the ISO 12620 standard for facilitating the
implementation of filters and converters between
different terminology instances and to produce a
Generic Mapping Tool (GMT) representation, i.e.
a canonical XML representation. The main role
of our terminological extractor is to automatically
generate terms in GMT format and create a nor-
malized terminological database of scientific and
technical terms.

Figure 1 shows an example of scientific termi-
nological entry (Multi-car elevator) in the form of
an XML document conforming GMT in two lan-
guages (Arabic and English).

5 Proposed approach

The extraction method of Arabic terms that we
advocate is rule-based. In fact, the rules that are
manually built, express the structure of the infor-
mation to extract and take the form of transducers.
These transducers generally operate morphosyn-
tactic information, as well as those contained in
the resources (lexicons or dictionaries). Moreover,
they allow the description of possible sequences of
constituents of Arabic terms belonging to the field
of elevators. The approach that we propose to ex-
tract terms for the field of elevators is composed
of two steps (Figure. 2): (i) identifying the neces-

<footnote n="3">³ISO 16642:2003. Computer Applications in Terminology: Terminological Markup Framework</footnote>

<footnote n="4">⁴ISO 12620:2009. Terminology and Other Language and Content Resources – Specification of Data Categories and Management of a Data Category Registry for Language Resources</footnote>
<figure><img src="image_4.png" /><figcaption>Figure 1: Terminological entry conforming GMT</figcaption></figure>

sary resources to identify terms to extract, (ii) the
creation of a cascade of transducer each of which
has its own role.

In the following, we detail the different re-
sources and steps of our approach.

**5.1 Necessary linguistic resources**

For our approach, we construct linguistic resouces
from our study corpus like dictionaries, trigger
words and extraction rules (syntactic patterns). In
the following, we present these resources.

**5.1.1 Dictionaries**

For the domain of elevator, subject of our study,
we identified the following dictionaries: a dictio-
nary of inflected nouns and their canonical forms,
a dictionary of inflected verbs, a dictionary for ad-
jectives, a dictionary for trigger words of the do-
main and dictionaries of particles, possessive pro-
nouns, demonstrative pronouns and relative pro-
nouns. The structure of the various dictionary en-
tries is not the same. It can vary from one dic-
tionary to another. It must contain the grammati-
cal category of the entry (noun, adjective), but, ac-
cording to the dictionary, it may contain also: gen-
der (masculine, feminine or neutral) and number
(singular, dual, plural or broken plural), definition

(defined or undefined), case (accusative, nomina-
tive or genitive) or mode (indicative, subjunctive
or jussive), person (1st person, 2nd person or 3rd
person) and voice (active or passive).

**5.1.2 Trigger words**

The extraction rules generally use morphosyntac-
tic information such as trigger words for the de-
tection of the beginning of a term. We opted for
the increased number of rules and triggers in order
to have as efficient as possible extraction system.
We identified 162 trigger words, some of them can
trigger the recognition of up to 5 terms. For this
reason we classified them in classes.

**5.1.3 Extraction rules**

To facilitate identification of the necessary trans-
ducers for the extraction of terms, we have built
a set of extraction rules. Indeed, they give the ar-
rangement of the various constituents of the terms
of a readily transferable linearly as graphs. We
identified 12 extraction rules. Table 1 shows some
of them. Four grammatical features are attribu-
ated here: gender (masculine (m) or feminine (f)),
number (singular (s), dual (d) or plural (p)), defi-
nition (defined (r) or undefined (n)) and case (ac-
cusative (a), nominative (u) or genitive (i)).

Examples of trigger words are: "تحريك" "mobilization" for the rules **R1** and **R5**, "صيانة" "maintenance" for the rules **R2** and **R3**.

<figure><img src="image_5.png" /><figcaption>Figure 2: Proposed approach</figcaption></figure>
<table><caption>Table 1: Some extraction rules of Arabic patent terms</caption><thead><tr><th>Rule number</th><th>Extraction rules</th></tr></thead><tbody><tr><td>R1</td><td>&lt;Pattern 1&gt;:=&lt;Trigger word&gt;<br/>&lt;N:nums&gt;&lt;PREP&gt;(&lt;N:nums&gt;)<sup>+</sup></td></tr><tr><td>R2</td><td>&lt;Pattern 2&gt;:=&lt;N:nums&gt;<br/>&lt;PREP&gt;&lt;N:nufs&gt;[&lt;Adj:nufs&gt;]</td></tr><tr><td>R3</td><td>&lt;Pattern 3&gt;:=&lt;Trigger word&gt;<br/>&lt;N:nums&gt;&lt;Adj:nums&gt;</td></tr><tr><td>R4</td><td>&lt;Pattern 4&gt;:=&lt;Trigger word&gt;<br/>&lt;N:nufs&gt;&lt;N:rums&gt;</td></tr><tr><td>R5</td><td>&lt;Pattern 5&gt;:=&lt;Trigger word&gt;<br/>&lt;N:nufp&gt;&lt;N:rums&gt;</td></tr></tbody></table>

nance” for the rule **R3** and رفع "lifting" for the rule **R4**. The Table 2 shows some extracted terms due to the precedent extraction rules (here identified by their number in Table 1).

<table><caption>Table 2: Terms extracted due to extraction rules</caption><thead><tr><th>Rule number</th><th>Extracted terms</th></tr></thead><tbody><tr><td>R1</td><td>مصد بدون وزن عکسی<br/>"elevator without counterweight"</td></tr><tr><td>R2</td><td>مصد ببكرة مجزوزة<br/>"elevator with splined roller"</td></tr><tr><td>R3</td><td>مصد آلي<br/>"automatic elevator"</td></tr><tr><td>R4</td><td>عربة المصد<br/>"elevator car"<br/>لوحة التحكم<br/>"contor panel"</td></tr><tr><td>R5</td><td>أحبال الرفع<br/>"hoisting ropes"</td></tr></tbody></table>

## 5.2 Implementation of extraction rules

We created three types of transducers. The first one is the transducer of pre-treatment solving Arabic prefixes and suffixes agglutination. To recognize the agglutinative character, we should enter inside the token. As Unitex works on a tokenized version of the text, it is not possible to make queries entering within the tokens, except

with morphological filters or the morphological mode that is more appropriate in our case. To do this, we must define the whole portion of grammar using the symbols `<` and `>` as presented in the Figure. 3). The transducer annotate every part of the agglutinated token with appropriate grammatical category.

<figure><img src="image_6.png" /><figcaption>Figure 3: Transducer of resolution of agglutination</figcaption></figure>

The second transducer, as shown in Figure. 4, includes all subgraphs of term extraction and annotation under the format GMT ("extraction_trasducers" box). In order to improve terms extraction, trigger words are regrouped into the "trigger_words" box.

<figure><img src="image_7.png" /><figcaption>Figure 4: The main extraction transducer</figcaption></figure>

Figure. 5 shows one of the transducers that extract and annotate terms. It also recognizes the French or English translation of the term if available thanks to the "French_Translation" and "English_Translation" subgraph and annotate them in a new Language Section (LS) in the GMT format as shown in Figure. 1.

The final transducer is a post-treatment transducer consisting on document cleaning: its role is to delete all text remains (which is not XML). Figure. 6 is an overview of this transducer. The subgraph "XML" recognize all the XML element that could be contained by the `<struct type="TE">` GMT element.
<figure><img src="image_8.png" /><figcaption>Figure 5: Example of extraction subgraph</figcaption></figure>

## 6 Expression and evaluation

Our test corpus contains 160 Arabic documents from multiple resources: 100 patents, 50 scientific papers and 10 manuals and installation documents of elevators, with a total number of 1665635 tokens. Our transducers are called in a specific order in a transducer cascade that is directly implemented in the linguistic platform Unitex<fnref n="5" /> using the CasSys tool (Friburger and Maurel, 2004). Each graph adds its own annotations due to the mode "Replace". This mode provides, as output, a recognized term surrounded by a GMT annotation defined in the transducers.

In order to conduct an evaluation, we applied the cascade implemented on the test corpus. We manually evaluated the quality of our work on the test corpus. The total number of terms is 852. Table 3 gives an overview of the obtained results.

<table><caption>Table 3: Overview of the obtained results</caption><thead><tr><td>Terms</td><td>Extracted terms</td><td>Erroneous terms</td></tr></thead><tbody><tr><td>852</td><td>827</td><td>59</td></tr></tbody></table>

The obtained results are satisfactory, the transducers were able to cover the majority of the terms contained therein with a precision of 0.95 and a recall of 0.97 with a F-score of 0.95. We therefore find that the proposed method is effective.

<figure><img src="image_9.png" /><figcaption>Figure 6: Post-treatment transducer</figcaption></figure>

The noise can be caused by the absence of diacritics in our corpus and dictionaries, which could create ambiguity problem. It may also be caused by the absence of high granularity features of our dictionary entries. For this reason, we will try to add other semantic and grammatical features to our dictionary entries to improve our results. Despite the good results, we were forced to spend our terminological database to a terminologist to correct erroneous terms and their definitions. We believe that the automatic integration and merging of our database with other existing databases can help us to automatically correct errors.

## 7 Conclusion

In this paper, we built a set of transducers. Then we generated a cascade allowing extraction of scientific and technical terms. The extracted terms were represented in a standardized format (GMT). The generation of this cascade is performed using the CasSys tool, built-in Unitex linguistic platform. The operation of the transducer cascade required the construction of resources such as dictionaries.

In the immediate future, we will create a transducer cascade to extract bibliographic data and metadata of citations, tables, formulas and figures from scientific and technical documents and patents. We will also extract terms using a satatistic approach. Finally, we will try to combine the two approaches in a hybrid one.

<footnote n="5">⁵Unitex: http://www-igm.univ-mlv.fr/ unitex/</footnote>
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