An Ontology-based Semantic Tagger for IE system
Narj`es Boufaden
Department of Computer Science
Universit´e de Montr´eal
Quebec, H3C 3J7 Canada
boufaden@iro.umontreal.ca
Abstract
In this paper, we present a method for
the semantic tagging of word chunks ex-
tracted from a written transcription of con-
versations. This work is part of an ongo-
ing project for an information extraction
system in the field of maritime Search And
Rescue (SAR). Our purpose is to auto-
matically annotate parts of texts with con-
cepts from a SAR ontology. Our approach
combines two knowledge sources a SAR
ontology and the Wordsmyth dictionary-
thesaurus, and it uses a similarity measure
for the classification. Evaluation is carried
out by comparing the output of the system
with key answers of predefined extraction
templates.
1 Introduction
This work is a part of a project aiming to imple-
ment an information extraction (IE) system in the
field of maritime Search And Rescue (SAR). It was
originally conducted by the Defense Research Es-
tablishment Valcartier (DREV) to develop a deci-
sion support tool to help in producing SAR plans
given the information extracted by the SAR IE sys-
tem from a collection of transcribed dialogs. The
goal of our project is to develop a robust approach
to extract relevant words for small-scale corpora and
transcribed speech dialogs. To achieve this task, we
developed a semantic tagger which annotates words
with domain-specific informations and a selection
process to extract or reject a word according to the
semantic tag and the context. The rationale behind
our approach, is that the relevance of a word depends
strongly on how close it is to the SAR domain and
its context of use. We believe that reasoning on se-
mantic tags instead of the word is a way of getting
around some of the problems of small-scale corpora.
In this paper, we focus on semantic tagging
based on a domain-specific ontology, a dictionary-
thesaurus and the overlapping coefficient similarity
measure (Manning and Schutze, 2001) to semanti-
cally annotate words.
We first describe the corpus (section 2), then the
overall IE system (section 3). Next we explain the
different components of the semantic tagger (section
4) and we present the preliminary results of our ex-
periments (section 5). Finally we give some direc-
tions for future work (section 6).
2 Corpus
The corpus is a collection of 95 manually tran-
scribed telephone conversations (about 39,000
words). They are mostly informative dialogs, where
two speakers (a caller C and an operator O) dis-
cuss the conditions and circumstances related to
a SAR mission. The conversations are either (1)
incident reports, such as reporting missing per-
sons or overdue boats, (2) SAR mission plans,
such as requesting an SAR airplane or coast guard
ships for a mission, or (3) debriefings, in which
case the results of the SAR mission are com-
municated. They can also be a combination of
the three kinds. Figure 1 is an excerpt of such
conversations. We can notice many disfluencies
1-O:Hi, it’s Mr. Joe Blue| {z }.
PERSON
...
3-O:We get|{z} an overdue boat| {z }, missing boat| {z } on the South Coast of Newfoundland| {z }...
STATUS MISSING-VESSEL MISSING-VESSEL LOCATION-TYPE
4-O:They did a radar search| {z }for us in the area| {z }.
DETECTION-MEANS LOCATION
5-C:Hum, hum.
8-O:And I am wondering| {z }about the possibility| {z } of outputting| {z }an Aurora| {z }in there for radar search| {z }.
STATUS-REQUEST STATUS-REQUEST TASK SAR-AIRCRAFT-TYPE DETECTION-MEANS
...
11-O:They got|{z} a South East| {z }to be flowing| {z }there and it’s just gonna|{z}be black thicker fog| {z }the whole, whole South Coast| {z }.
STATUS DIRECTION-TYPE STATUS STATUS WEATHER-TYPE LOCATION-TYPE
12-C:OK.
...
56-:Ha, they should go| {z } to get going| {z } at first light| {z }.
STATUS STATUS TIME
Figure 1: An Excerpt of a conversation reporting an overdue vessel:the incident, a request for an SAR
airplane (Aurora) and the use of another SAR airplane (king Air). The words in bold are candidates for the
extraction. The tag below each bold chunk is a domain-specific information automatically generated by the
semantic tagger. Chunks like possibility, go, flowing and first light are annotated by using sense tagging
outputs. Whereas chunk such as Mr. Joe Blue, the South coast of Newfoundland and Aurora are annotated
by the named concept extraction process.
(Shriberg, 1994) such as repetitions (13-O: Ha,
do, is there, is there ...) , omissions
and interruptions (3-O: we’ve been, actu-
ally had a ...). And, there is about 3% of
transcription errors such as flowing instead of
blowing (11-O Figure 1).
The underlined words are the relevant informa-
tions that will be extracted to fill in the IE tem-
plates. They are, for example, the incident, its lo-
cation, SAR resources needed for the mission, the
result of the SAR mission and weather conditions.
3 Overall system
The information extraction system is a four stage
process (Figure 2). It begins with the extraction
of words that could be candidates to the extraction
(stage I). Then, the semantic tagger annotates the
extracted words (stage II). Next, given the context
and the semantic tag a word is extracted or rejected
(stage III). Finally, the extracted words are used
for the coreference resolution and to fill in IE tem-
plates (stage IV). The knowledge sources used for
the IE task are the SAR ontology and the Wordsmyth
dictionary-thesaurus1.
In this section we describe the extraction of can-
didates, the SAR ontology design and the topic seg-
mentation which have already been implemented.
We leave the description of the topic labeling, the
selection of relevant words and the template genera-
tion to future work. The semantic tagger, is detailed
in section 4.
3.1 Extraction of candidates
Candidates considered in the semantic tagging pro-
cess are noun phrases NP, proposition phrases PP,
verb phrases VP, adjectives ADJ and adverbs ADV.
To gather these candidates we used the Brill trans-
formational tagger (Brill, 1992) for the part-of-
speech step and the CASS partial parser for the pars-
ing step (Abney, 1994). However, because of the
disfluencies (repairs, substitutions and omissions)
encountered in the conversations, many errors oc-
curred when parsing large constructions. So, we re-
duced the set of grammatical rules used by CASS to
cover only minimal chunks and discard large con-
structions such as VP ! VX NP? ADV* or noun
1URL http://www.wordsmyth.net/.
Transcribed
Conversation
  Stage I
. . . . . . . . . . . . . . .
Extraction
of candidates
  Stage II:Semantic Tagging
. . . . . . . . . . . . . . .
Named
Concepts
Extraction
  
SAR
Ontologyoo
xxppppp
pppp
Sense Tagging
  
Wordsmyth
Dictionary
Thesaurus
oo
Stage III:Selecting relevant
candidates . . . . . . . . . . . . . . . _ _ _ _ 
 
 
 _ _ _ _
Topic
Labeling
wwppppp
p_ _ _ _ 
 
 
 
 
 _ _ _ _
Selection
of relevant
words
  
Topic
Segmentation
OO
{{wwww
wwww
w
Stage IV . . . . . . . . . . . . . . ._ _ _ _ _
 
 
 
 _ _ _ _ _
IE Templates
generation
Figure 2: Main stages of the full SAR information
extraction system. Dashed squares represent pro-
cesses which are not developed in this paper.
phrases NP ! NP CONJ NP. The evaluation of the
semantic tagging process shows that about 14.4% of
the semantic annotation errors are partially due to
part-of-speech and parsing errors.
3.2 Topic segmentation
Topic segmentation takes part to several stages in
our IE system (Figure 2). Dialogue-based IE sys-
tems have to deal with scattered information and
disfluencies. Question-answer pairs, widely used in
dialogues, are examples where information is con-
veyed through consecutive utterances. By divid-
ing the dialog into topical segment, we want to en-
sure the extraction of coherent and complete key an-
swers. Besides, topic segmentation is a valuable pre-
processing for coreference resolution, which is a dif-
ficult task in IE. Hence, for the extraction of relevant
candidates and the coreference resolution which is
part of the template generation stage (Figure 2), we
use topic segment as context instead of the utterance
or a word window of arbitrary size.
The topic segmentation system we developed is
based on a multi-knowledge source modeled by a
hidden Markov model. (N. Boufaden and al., 2001)
showed that by using linguistic features modeled by
a Hidden Markov Model, it is possible to detect
about 67% of topics boundaries.
3.3 The SAR ontology
The SAR ontology is an important component of our
IE system. We build it using domain related infor-
mations such as airplane names, locations, organi-
zations, detection means (radar search, div-
ing), status of a SAR mission (completed,con-
tinuing, planned), instance of maritime inci-
dents (drifting, overdue) and weather condi-
tions (wind, rain, fog). All these informations
were gathered from SAR manuals provided by the
National Search and Rescue Secretariat (SARMan-
ual, 2000) and from a sample of conversations (10
conversations about 10% of the corpus) to enumer-
ate the different status informations.
Our ontology was designed for two tasks of the
semantic tagging:
1. Annotate with the corresponding concept all
the extracted words that are instances of the on-
tology. This task is achieved by the named con-
cept extraction process (section 4.1).
2. For each word not in the ontology, generate
a concept-based representation composed of
similarity scores that provide information about
the closeness of the word to the SAR domain.
This is achieved by the sense tagging process
(section 4.2).
In addition to SAR manuals and corpus, we used
the IE templates given by the DREV for the de-
sign of the ontology. We used a combination of the
top-down and bottom-up design approaches (Frid-
man and Hafner, 1997). For the former, we used
the templates to enumerate the questions to be cov-
ered by the ontology and distinguish the major top
level classes (Figure 4). For the latter, we collected
the named entities along with airplane names, ves-
sel types, detection means, alert types and incidents.
The taxonomy is based on two hierarchical relations:
the is-a relation and the part-of relation. The is-a re-
lation is used for the semantic tagging. Whereas, the
ENT: wonder
SYL: won-der
PRO: wuhn dEr
POS: intransitive verb
INF: wondered, wondering, wonders
DEF: 1. to experience a sensation of admiration or amazement (often fol. by at):
EXA: She wondered at his bravery in combat.
SYN: marvel
SIM: gape, stare, gawk
DEF: 2. to be curious or skeptical about something:
EXA: I wonder about his truthfulness.
SYN: speculate (1)
SIM: deliberate, ponder, think, reflect, puzzle, conjecture
...
Figure 3: A fragment of the Wordsmyth dictionary-thesaurus entry of the verb wonder which is a verb
describing a STATUS-REQUEST concept (8-O Figure 1). The ENT, SYL, PRO, POS, INF, DEF, EXA, SYN,
SIM acronyms are respectively the entry, the syllable, the pronunciation, the part-of-speech, inflexion form,
textual definition, example, synonim words and similar words fields. To build the SAR ontology we used
the information given in the fields DEF, SYN and SIM. Whereas, to compute the similarity scores we used
only the information of the DEF field.
part-of relation will be used in the template genera-
tion process.
The overall ontology is composed of 31 concepts.
In the is-a hierarchy, each concept is represented by
a set of instances and their textual definitions. For
each instance we added a set of synonyms and simi-
lar words and their textual definitions to increase the
size of the SAR vocabulary which was found to be
insufficient to make the sense tagging approach ef-
fective.
All the synonyms and similar words along with
their definitions are provided by the Wordsmyth
dictionary-thesaurus. Figure 3 is an example of
Wordsmyth entries. Only textual definitions that
fit the SAR context were kept. This procedure in-
creases the ontology size from 480 for a total of 783
instances.
Location Aircraft Vessel . . . Detectionmeans
     
     AAHHHH
XXXXX
XX
Physical
Entity
Event . . . SearchMission
     cc
Conceptual
Entity
!!!
!
‘‘‘‘‘
‘‘‘
T
Figure 4: Fragment of the is-a hierarchy. Location,
Aircraft . . . are concepts of the ontology
4 Semantic tagging
The purpose of the semantic tagging process is to an-
notate words with domain-specific informations. In
our case, domain-specific informations are the con-
cepts of the SAR ontology. We want to determine
the concept Ck which is semantically the most ap-
propriate to annotate a word w. Hence, we look
for C which has the highest similarity score for the
word w as shown in equation 1.
C = argmax
Ck
sim(w;Ck) (1)
Basically, our approach is a two part process (fig-
ure 2). The named concept extraction is similar to
named entity extraction based on gazetteer (MUC,
1991). However it is a more general task since it
also recognizes entities such as, aircraft names, boat
names and detection means. It uses a finite state
automaton and the SAR ontology to recognize the
named concepts.
The sense tagging process generates a based-
concept representation for each word which couldn’t
be tagged by the named concept extraction process.
The concept-based representation is a vector of sim-
ilarity scores that measures how close is a word to
the SAR domain. As we mentioned before (section
1), the concept-based representation using similarity
scores is a way to get around the problem of small-
scale corpora. Because we assume that the closer a
word is to an SAR concept, the more relevant it is,
this process is a key element for the selection of rel-
evant words (figure 2). In the next two sections, we
detail each component of the semantic tagger.
4.1 Named concept extraction
This task, like the named entity extraction task, an-
notates words that are not instances of the ontol-
ogy. Basically, for every chunk, we look for the first
match with an instance concept. The match is based
on the word and its part-of-speech. When a match
succeeds, the semantic tag assigned is the concept
of the instance matched. The propagation of the se-
mantic tag is done by a two level automaton. The
first level propagates the semantic tag of the head
to the whole chunk. The second level deals with
cases where the first level automaton fails to recog-
nize collocations which are instances of the ontol-
ogy.
These cases occur when :
 the syntactic parser fails to produce a correct
parse. This mainly happens when the part of
speech tag isn’t correct because of disfluencies
encountered in the utterance or because of tran-
scription errors.
 the grammatical coverage is insufficient to
parse large constructions.
Whenever one of these reasons occur, the second
level automaton tries to match chunk collocations in-
stead of individual chunks. For example, the chunk
Rescue Coordination Centre which is an
organization, is an example where the parser pro-
duces two NP chunks (NP1:Rescue Coordina-
tionand NP2:Centre) instead of only one chunk.
In this case, the first level automaton fails to recog-
nize the organization. However, in the second level
automaton, the collocation NP1 NP2 is considered
for matching with an instance of the concept organi-
zation. Figure 5 shows two output examples of the
named concept extraction.
Finally, if the automaton fails to tag a chunk,
it assigns the tag OTHER if it’s an NP, OTHER-
PROPERTIES if it’s a ADJ or ADV and OTHER-
STATUS if it’s a VP.
4.2 Sense tagging
Sense tagging takes place when a chunk is not an
instance of the ontology. In this case, the semantic
tagger looks for the most appropriate concept to an-
notate the chunk (equation 1). However, a first step
before annotation is to determine what word sense
is intended in conversations. Many studies (Resnik,
1999; Lesk, 1986; Stevenson, 2002) tackle the sense
tagging problem with approaches based on similar-
ity measures. Sense tagging is concerned with the
selection of the right word sense over all the pos-
sible word senses given some context or a particu-
lar domain. Our assumption is that when conversa-
tions are domain-specific, relevant words are too. It
means that sense tagging comes back to the prob-
lem of selecting the closer word sense with regard to
the SAR ontology. This assumption is translated in
equation 2.
w = argmax
w(l)
1
Nl all concepts ksim(w(l);k)
(2)
Where Nl is the number of positive similarity
scores of the w(l) similarity vector. w(l) is the word
w given the word sense l. The closer word sense w 
is the highest mean computed from element of the
w(l) similarity vector.
In what follows, we explain how are generated the
similarity vectors and the result of our experiments.
4.3 Similarity vector representation
A similarity vector is a vector where each element
is a similarity score between a word(l) (the word w
given the sense word l) and a concept Ck from the
SAR ontology. The similarity score is based on the
overlap coefficient similarity measure (Manning and
Schutze, 2001). This measure counts the number of
lemmatized content words in common between the
textual definition of the word and the concept. It is
defined as :
sim(w(l);Ck) = jDw(l) j\jDCk jmin(jD
w(l) j;jDCk j)
(3)
where Dw(l) and DCk are the sets of lemmatized
content words extracted from the textual definitions
3-O:an overdue boat
VESSEL:[dt,an],[OTHER-PROPERTIES,overdue],[VESSEL,boat]
11-O:black thicker fog
WEATHER-TYPE:[COLOR-TYPE,black],[OTHER-PROPERTIES,thicker],[WEATHER-TYPE,fog]
Figure 5: Output of the named concept extraction process. For both chunks the head semantic tag is propa-
gated to the whole chunk
for each concept Ck of the SAR ontology; Ck2fincident,detection-means,status. . .g
for each instance Ij of Ck; Ij2fbroken,missing,overdue. . .gfor the concept incident
for each synonym Si of Ij; Si2fsmach,crack. . .gfor the instance broken
sim(w(l);Si)= jDw(l)j\jDSijmin(jD
w(l)j;jDSij)
end
~vj def= (sim(w(l);S1);:::;sim(w(l);SNj))
sim(w(l);Ij)=mediane(~vj)
end
~vk def= (sim(w(l);I1);:::;sim(w(l);IMk))
sim(w(l);Ck)=max(~vk)
end
~vw(l) def= (sim(w(l);C1);:::;sim(w(l);CM))
Figure 6: Similarity measure algorithm. Nj is the number of synonyms for the instance Ij, Mk the number
of the instance for the concept Ck and M the number of concepts in the ontology.
of w(l) and Ck. The textual definitions are provided
by the Wordsmyth thesaurus-dictionary.
However, since we have represented each concept
by a set of instances and their synonyms in the SAR
ontology (section 3.3), we modified the similarity
measure to take into account the textual definition
of concept instances and their synonyms. Basically,
we compute the similarity score between w(l) and
each synonym Si of a concept instance Ij. Then,
the similarity score between w(l) and the instance
concept Ij is the median of the resulting similarity
vector representing the similarity scores over all the
synonyms. Finally, the similarity score between a
concept Ck and w(l) is the highest similarity score
over all the concept instances. The algorithm de-
scribing these steps is given in Figure 6.
5 Preliminary results and discussion
The evaluation of the semantic tagging process was
done on 521 extracted chunks (about 10 conversa-
tions). Only relevant chunks where considered for
Chunk Mean sim Nearest concepts
get 0.5 0.5 - status
suitable 0.53 0.53 - status
possibility 0.14 0.29-status;0.25-person
first light 0.25 0.25 - time
Table 1: Output samples from the semantic tagger.
Mean sim is the mean of the similarity scores. It is
the selection criteria used to choose the closest word
sense.
the evaluation. The evaluation criteria is an assess-
ment about the appropriateness of the selected con-
cept to annotate the word. For example, the concept
time is appropriate for the word first light, whereas
the concept incident is not for the word detachment
which is closer to the search unit concept.
Table 2 shows the recall and precision scores for
each component and for the overall semantic tagger.
The third column shows the input error rates for each
component. The error rate in the first row comprises
Process Recall Precis. Inp.Err
Named concept
extraction 85.3% 94.8% 7.3%
Semantic tagger using
sense tagging output 93.5% 72.6% 11.3%
Average performance
of the semantic tagger 89.4% 83.7% 8.3%
Table 2: Precision and Recall scores for each com-
ponents of the semantic tagger
error rates of the part-of-speech tagger, the parsing
and the manual transcription. The error rate in the
second row are mostly part-of-speech errors. In spite
of the significant error rate, the approach based on
partial parsing is effective. The use of a minimal
grammar coverage to produce chunks reduced con-
siderably the parsing error rate.
As far as we know, no previous published work
on domain-specific WSD for speech transcriptions
has been presented, although, word sense disam-
biguation is an active research field as demonstrated
by SENSEVAL competitions2. Hence it is diffi-
cult to compare our results to similar experiments.
However, some comparative studies (Maynard and
Ananiadou, 1998; Li Shiuan and Hwee Tou, 1997)
on domain-specific well-written texts show results
ranging from 51,25% to 73,90%. Given the fact
that our corpus is composed of speech transcriptions
with the effect of increasing parsing errors, we con-
sider our results to be very encouraging.
Finally, results reported in Table 2 should be re-
garded as a basis for further improvement. In partic-
ular, the selection criteria in the sense tagging pro-
cess could be improved by considering other mea-
sures than the mean of all similarity scores as shown
in equation 2.
6 Future work
Extraction of relevant words is a hub for several ap-
plications such as question-answering and summa-
rization. It is based on semantically tagging words
and selecting the most relevant ones given the con-
text. In this paper, we developed a semantic tag-
ging approach that uses a domain-specific ontology,
a dictionary-thesaurus and the overlapping coeffi-
2URL:http://www.senseval.org/.
cient similarity measure to annotate words. We have
shown how the use of concepts to represent words
can alleviate the problem of small-scale corpora for
the selection of relevant words.
The next step in our project is the selection of rel-
evant words given the concepts annotating them and
the topic segments where they appear. Selection will
be based on a combination of a probabilistic model
taking into account the probability of observing a
concept given a word and the probability of observ-
ing that concept given a relevant topic.
Acknowledgments
We are grateful to Robert Parks at Wordsmyth orga-
nization for giving us the electronic Wordsmyth ver-
sion. Thanks to the Defense Research Establishment
Valcartier for providing us with the dialog transcrip-
tions and to National Search and rescue Secretariat
for the valuable SAR manuals.
References
S. Abney. 1994. Partial parsing. Tutorial given at ANLP.
N. Boufaden G. Lapalme and Y. Bengio. 2001. Topic
segmentation : A first stage to dialog-based informa-
tion extraction. In Natural Language Processing Rim
Symposium, NLPRS’01, pages 273–280.
E. Brill. 1992. A simple rule-based part-of-speech tag-
ger. In Proceedings of the Third Conference on Ap-
plied Natural Language Processing, Trento, Italy.
Manual. Fisheries and Oceans Canada, Canadian Coast
Guard, Search and Rescue, 2000. SAR Seamanship
Reference Manual, Canadian Government Publishing,
Public Works and Government Services Canada edi-
tion, November. ISBN 0-660-18352-8.
N. Fridman and C.D. Hafner. 1997. State of the art in
ontology design. AI Magazine, 18(3):53–74.
M. Lesk. 1986. Automatic sense disambiguation using
machine readable dictionaries: how to tell a pine cone
from an ice cream cone. In Proceedings of ACM SIG-
DOC Conference, pages 24–26, Toronto, Canada.
C. D. Manning and H. Schutze, 2001. Foundations
of Statistical Natural Language Processing, chapter
Word Sense Disambiguation, pages 294–303. The
MIT Press Cambridge, Massachusetts London Eng-
land.
MUC,1991. Proceedings of the Third Message Under-
standing Conference. Morgan Kaufman.
D. Maynard and S. Ananiadou, 1998. 1998. Term
Sense Disambiguation using a Domain-Specific The-
saurus. In Proceedings of 1st International Confer-
ence on Language Resource and Evaluation (LREC),
Granada, Spain.
P. Resnik, 1999. Natural Language Processing using
Very Large Corpora, chapter Disambiguating Noun
Groupings with Respect to WordNet senses, pages 77–
98. S. Amstrong, K. Church, P. Isabelle, S. Manzi,
E, Tzoukermann and D. Yarowsky, kluwer Academic
Press edition.
E. Shriberg. Preliminaries to a Theory of Speech Disflu-
encies. Th`ese de doctorat, University of California at
Berkeley.
P. Li Shiuan and N. Hwee Tou 1997. Domain-Specific
Semantic Class Disambiguation Using Wordnet. In
Proceedings of the fifth Workshop on Very Large Cor-
pora, pages 56–64, Beijing and Hong Kong.
M. Stevenson. 2002. Combining Disambiguation Tech-
niques to Enrich an Ontology. In Proceedings of
the Fifteen European Conference on Artificial Intel-
ligence, workshop on Machine Learning and Natu-
ral Language Processing for Ontology Engineering,
Lyon,France.
