(Semi)automatic glossary detection
for Bulgarian
Laska Laskova
Linguistic Modelling Laboratory
Institute for Parallel Processing
Bulgarian Academy of Sciences
The project concerned
Language Technology for eLearning
Goal:
Domain:
For:
Via:



(http://www.lt4el.eu/)
improving the retrieval of learning material
Computer Science for non-computer scientists
Bulgarian, Czech, Dutch, English, German, Maltese,
Polish, Portuguese, Romanian
implementing multilingual language technology tools
in Learning Management Systems (ILIAS):
key word extractor
glossary candidate detector
semantic knowledge (ontologies)
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Content

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
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Introduction
Preprocessing steps
Definition. Definitory context types
Grammar types
Conclusion
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Introduction

Bulgarian corpus:

54 learning objects (.html, .htm, .doc format):


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207 000 tokens appr.
12 LO: Calimera Guidelines, section 3
15 LO: Common MS Office programs tutorials
27 LO: Internet Technologies tutorials for beginners and
advanced users
Problematic features:



High number of non-standard words
32 % terms including English words
Unusual text structure
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Preprocessing steps
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Preprocessing steps

Non-standard words
Some examples:
TCP/IP
#FF00FF
.zip
"
Не-Е-Число
име_на_рамка
getNodeNam ()

Problematic steps:
tokenization
lemmatization
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Preprocessing steps

Unusual text structure
•••••
 Problematic steps: sentence segmentation
definition mark-up
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Definition. Definitory context
types


Definitory context (definition):
 Canonical structure: definiendum – connector – definiens
 Canonical content: genus proximum – differentia
Some “departures form the norm”:
 Structure. No definiendum and no connector:
“Показва текущия адрес на документа, който разглеждате в Web.”
(“Shows the current address of the document you are browsing.”) (Address bar)

Content. Definition by function (“what it does”). No genus
proximum (and no connector):
“Рамките разделят страницата на отделни части, във всяка от които се
показва друга страница.”
(“Frames divide the web page into parts, such that each part in turn displays
another page.”)
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Definition. Definitory context
types
Manually annotated definitory contexts: 767, 826 sentences
Type
is_def
verb_def
layout_def
definiendum
connector
Manual
NP/pronoun
aux 3sg/3pl
220
NP/pronoun/
verb 3sg/3pl
404


70
punct_def
NP
: - =
32
 Some definitions span over one sentence / define one definiendum
 Pron_def type (54 manually annotated) definitions are included in
is_def and verb_def type groups, depending on the connector
 Other_def type (59 manually annotated): definitions with various
patterns, less then two occurrences
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Grammar tool

The implemented tool:
LXtransduce (Tobin, University
of Edinburgh)
 Transducer which
processes an XML input
stream and rewrites it
according to a set of rules
provided in the grammar
file
 Rules:
 regular expressions
 references to other
rules
 top-down grammar
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Definitory context types
1. is_def
“XML
language is a
set of specific
elements for
standard text
mark-up.”

Structure:



NP is/are NP
Coordinated NP is/are NP
Pronoun is/are NP
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Grammar
1. is_def

Some characteristics of is_def grammar:

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36 simple rules for matching basic tokens: N, Adj,
Adv, “,” ,”-”, etc.
Rule for “съм” (“to be”) verb in 3sg and 3pl present
tense.
Rules for 4 types of premodified NP structures.
Complex rule for NP postmodifier.
Rule for any kind of token, used in main rule:

(NP)+ is/are (premodified NP (postmodifier)+)+ (ANY)+
Why bother with the rule for NP postmodifier?

(NP)+ is/are NP (ANY)+
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Definitory context types
2. verb_def
“Reversed”
definition:
“(We) call this
place insertion
point”

Structure:



NP V NP
 (null subject) V NP
Pronoun V NP
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RANLP 2007
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Grammar
2. verb_def

Some characteristics of verb_def grammar:

Open list of verb forms (3sg, 3pl present tense) – 99 items.
Why so many?
“Logical equivalence”: “означава” (means), “представлява”
(represents), “се нарича” (is called), etc.
 “What it does” (often in sentences, part of multi-sentence
definitions): “вмъква” (inserts), “изобразява” (depicts), etc.
Constraints on the structure of verb complex.


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Definitory context types
3. layout_def

Structure:


 -  - NP
no main verb
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Grammar
3. layout_def

Some characteristics of verb_def grammar:

How do we match a sentence without a main verb?
<rule name= "tok_not_verb“>
<first> … <query match="tok [not (@ctag = 'V')
and not (@ctag = 'P' and contains (@msd, 'Pr'))
and not (. = '.') and not (. = '[') and not (. = ':')
and not (. = '=') and not (@class = 'internet')
and not (@class = 'num')]"/> … </first> </rule>
+ (NOT_VERB)+
 Main rule: premodified NP (postmodifier)
 Less rules for matching tokens with different POS tag.
 Simplified premodified NP structure.

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Definitory context types
4. punct_def

Structure


NP : / – / = NP
The definiendum may be preceded by an adjunct (PP)
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Grammar
4. punct_def

Some characteristics of punct_def grammar:

Rule matching first NP premodifier (if necessary, after a
bullet in a list).
Example: “222. Тематични карти (…)”
 <rule name="first_tok"> …
<ref name="bullet_02" suppress="true"/>
<query match="s [not(@id='s1')/tok [3]
[.~‘^[А-Я]\S*$‘]
[@ctag='A' or … or @ctag='M']"/> … </rule>
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RANLP 2007
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Definitory context types.
Evaluation
• Recall is more important than precision
Matching
level
Type
sentence
is_def
token
sentence
verb_def
token
sentence
punct_def
token
layout_def
sentence
token
Precision
Recall
F2–score
37.58
20.16
36.86
16.65
22.77
12.67
21.69
26.97
72.27
70.54
70.32
56.68
71.87
62.55
65.71
55.59
55.27
38.49
53.98
31.47
41.81
27.05
39.20
41.07
• Grammars performance
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Conclusion.
The good outcome example
 ILIAS – extended with Glossary Candidate Detector

Questions?
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Thank you!
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RANLP 2007
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