=Paper=
{{Paper
|id=Vol-1168/CLEF2002wn-adhoc-LlopisEt2002
|storemode=property
|title=IR-n System at Clef 2002
|pdfUrl=https://ceur-ws.org/Vol-1168/CLEF2002wn-adhoc-LlopisEt2002.pdf
|volume=Vol-1168
|dblpUrl=https://dblp.org/rec/conf/clef/LlopisGF02a
}}
==IR-n System at Clef 2002==
https://ceur-ws.org/Vol-1168/CLEF2002wn-adhoc-LlopisEt2002.pdf
IR-n system at CLEF-2002
Fernando Llopis, Jose L. Vicedo and Antonio Ferrandez
Grupo de investigacion en Procesamiento del Lenguaje y Sistemas de Informacion
Departamento de Lenguajes y Sistemas Informaticos
Universidad de Alicante
Alicante, Spain
fllopis,vicedo,antoniog@dlsi.ua.es
Abstract
Passage Retrieval is an alternative to traditional document-oriented Information
Retrieval. These systems use contiguous text fragments (or passages), instead of full
documents, as basic unit of information. IR-n system is a passage retrieval system
that use groups of contiguous sentences as unit of information. This paper reports on
experiments with IR-n system at Clef-2002 where it has obtained considerable better
results than last participation.
1 Introduction
Information Retrieval (IR) systems receive as input a user's query and as result, they return a set
of documents ranked by their relevance to the query. There are di erent techniques for measuring
the relevance of a document to a query, but most of them take into account the number of times
that query terms appear in documents, the importance or discrimination value of these terms in
the document collection, as well as the size of each document.
One of the main problems related to document-oriented retrieval systems is that they not
consider the proximity of appearance of query terms into the documents [6](see Figure 1).
The death of General Custer
At Gettysburg he remained with General Gregg
General Custer was Civil War
east of town to face jeb Stuart's threat to the Union
rear, although he was previously ordered to the
Union Major soldier. One of the most famous and controversial
figures in United States Military history. Graduated last in his
West Point Class (June 1861). Spent first part of the Civil War
Possibly. south. The combined Union force defeated Stuart.
Returning to the Army of the Potomac in early
1865, he fought at Five Forks; and in the
as a courier and staff officer. Promoted from Captain to
Brigadier General of Volunteers just prior to the Battle of
Gettysburg, and was given command of the Michigan
more Appomattox Campaign. His victories against the
rebel cavalry came at a time when that force was a
ghost of its former self Custer was brevetted in the
"Wolverines" Cavalary brigade.
He helped defeat General Stuart's attempt to make a cavalry
relevant regulars through grades to major general for
Gettysburg, Yellow Tavern, Winchester, Five Forks,
and the Appomattox Campaign. In addition he was
strike behind Union lines on the 3rd Day of the Battle (July 3,
brevetted major general of volunteers for
1863), thus markedly contributing to the Army of the Potomac's
Winchester.
victory (a large monument to his Brigade now stands in the East
Remaining in the army after the war, in 1866 he
Cavalry Field in Gettysburg). Participated in nearly every
was appointed Lt. Col. of the newly authorized 7th
cavalry action in Virginia from that point until the end of the
Cavalry, remaining its active commander until his
war, always performing boldly, most often brilliantly, and
always seeking publicity for himself and his actions. Ended the
war as a Major General of Volunteers and a Brevet Major
Same death. He took part in the 1867 Sioux and Cheyenne
expedition, but was court-martialed and suspended
from duty one year for paying an unauthorized visit
General in the Regular Army.
Upon Army reorganization in 1886, he was appointed relevance to his wife.
Lieutenant Colonel of the soon to be renown 7th United States
Cavalry. Fought in the various actions against the Western
Indians, often with a singular brutality (exemplified by his
wiping out of a Cheyenne village on the Washita in November
death General
The of
1868). His exploits on the Plains were romanticized by Eastern
Unites States newspapermen, and he was elevated to legendary Custer occurs in June 25, 1876, at the
status in his time. The death of his friend, Lucarelli
battle of Little Big Horn, which resulted in the
extermination of his immediate command and a total
loss of some 266 officers and men. On June 28th, the
change his life.
bodies were given a hasty burial on the field. The
following year, what may have been Custer's
remains were disinterred and given a military funeral
at West Point. (Monaghan, Jan, Custer:
Figure 1: Document-oriented retrieval
The death of General Custer
At Gettysburg he remained with General Gregg east
General Custer was Civil War
of town to face jeb Stuart's threat to the Union rear,
although he was previously ordered to the south. The
Union Major soldier. One of the most famous and controversial combined Union force defeated Stuart.
figures in United States Military history. Graduated last in his Returning to the Army of the Potomac in early
West Point Class (June 1861). Spent first part of the Civil War 1865, he fought at Five Forks; and in the
as a courier and staff officer. Promoted from Captain to Appomattox Campaign. His victories against the
Brigadier General of Volunteers just prior to the Battle of rebel cavalry came at a time when that force was a
Gettysburg, and was given command of the Michigan ghost of its former self Custer was brevetted in the
"Wolverines" Cavalary brigade. regulars through grades to major general for
Gettysburg, Yellow Tavern, Winchester, Five Forks,
He helped defeat General Stuart's attempt to make a cavalry
and the Appomattox Campaign. In addition he was
strike behind Union lines on the 3rd Day of the Battle (July 3,
brevetted major general of volunteers for
1863), thus markedly contributing to the Army of the Potomac's
Winchester.
victory (a large monument to his Brigade now stands in the East
Remaining in the army after the war, in 1866 he
Cavalry Field in Gettysburg). Participated in nearly every
was appointed Lt. Col. of the newly authorized 7th
cavalry action in Virginia from that point until the end of the
Cavalry, remaining its active commander until his
war, always performing boldly, most often brilliantly, and
death. He took part in the 1867 Sioux and Cheyenne
always seeking publicity for himself and his actions. Ended the
expedition, but was court-martialed and suspended
war as a Major General of Volunteers and a Brevet Major
from duty one year for paying an unauthorized visit
General in the Regular Army.
to his wife.
Upon Army reorganization in 1886, he was appointed
Lieutenant Colonel of the soon to be renown 7th United States
Cavalry. Fought in the various actions against the Western
Indians, often with a singular brutality (exemplified by his
wiping out of a Cheyenne village on the Washita in November
death General
The of
1868). His exploits on the Plains were romanticized by Eastern
Unites States newspapermen, and he was elevated to legendary More Custer occurs in June 25, 1876, at the
status in his time. The death of his friend, Lucarelli
relevant
battle of Little Big Horn, which resulted in the
extermination of his immediate command and a total
loss of some 266 officers and men. On June 28th, the
change his life.
bodies were given a hasty burial on the field. The
following year, what may have been Custer's
remains were disinterred and given a military funeral
at West Point. (Monaghan, Jan, r:
Figure 2: Passage retrieval
A possible alternative to these models consists on computing the similarity between a document
and a query in accordance with the relevance of the passages each document is divided (see Figure
2). This approach, called Passage Retrieval (PR), is not so a ected by the length of the documents
and besides, they add the concept of proximity to the similarity measure by analysing small pieces
of text instead of whole documents. Figures 1 and 2 show the main di erences between both
approaches.
PR systems can be classi ed in accordance with the way of dividing documents into passages.
PR community generally agrees with the classi cation proposed in [1], where the author distin-
guishes between discourse models, semantic models, and window models. The rst one uses the
structural properties of the documents, such as sentences or paragraphs [2] in order to de ne the
passages. The second one divides each document into semantic pieces according to the di erent
topics in the document [3]. The last one uses windows of a xed size (usually a number of terms)
to determine passage boundaries [5].
At rst glance, we could think that discourse-based models would be the most e ective, in
retrieval terms, since they use the structure of the document itself. However, this model greatest
problem relies on detecting passage boundaries since it depends on the writing style of the author
of each document. On the other hand, window models have as main advantage that they are
simpler to accomplish, since the passages have a previously known size, whereas the remaining
models have to bear in mind the variable size of each passage. Nevertheless, discourse-based and
semantic models have the main advantage that they return full information units of the document,
which is quite important if these units are used as input by other applications.
The passage extraction model that we propose (IR-n) allows us to bene t from the advantages
of discourse-based models since self-contained information units of text, such as sentences, are
used for building passages. Moreover, the relevance measure which, unlike other discourse-based
models, is not based on the number of passage terms, but on a xed number of passage sentences.
This fact allows a simpler calculation of this measure unlike other discourse-based or semantic
models. Although each passage is made up by a xed number of sentences, we consider that our
proposal di ers from the window models since our passages do not have a xed size (i.e. a xed
number of words) since we use sentences with a variable size.
This paper is structured as follows. The following section presents the basic features of IR-
n system. Third section describe the main improvements introduced for Clef-2002 Conference.
Fourth section describes the di erent runs performed for this campaign and discusses the results
obtained. Finally, last section extracts initial conclusions and opens directions for future work.
2 IR-n system
The system proposed has the main following features:
1. A document is divided into passages that are made up by a number N of sentences.
2. Passages overlap. First passage contains from sentence 1 to N, second passage contains from
sentence 2 to N + 1, etc.
3. The similarity between a passage p and a query q is computed as follows:
P assage similarity =
XW Wp;t q;t (1)
t2p^q
Where
Wp;t = loge (fp;t + 1),
fp;t is the number of appearances of term t in passage p,
Wq;t = loge (fq;t + 1) idf ,
fq;t represents the number of appearances of term t in question q,
idf = loge (n=ft + 1),
n is the number of documents of the collection and
ft is the number of documents term t appears in.
As it can be observed, this formulation is similar to the cosine measure de ned in [9]. The
main di erence is that length normalisation is omitted. Instead, our proposal accomplishes
length normalisation by de ning passage size as a xed number of textual discourse units.
In this case, the discourse unit selected is the sentence and a passage is de ned as a xed
number N of sentences. This way, although the number of terms of each passage may vary,
the number of sentences is constant.
IR-n system has been developed in C++ and runs in a Linux cheap computer, without addi-
tional software requirements.
3 IR-n system from Clef-2001 to Clef-2002
In last Clef edition IR-n system [8] was used in two retrieval tasks: monolingual (Spanish) and
bilingual (Spanish-English). Bilingual task results were satisfactory however, monoligual results
were very poor ranging below the average of the results obtained by all the participant systems.
After analysing those results we arrived at a series of conclusions that are summed up in the
following points:
- We had several problems on processing SGML original les. Consequently, some documents
were not indexed correctly.
- The Spanish lemmatizer that we selected (conexor) produced a high number of errors.
- The type of document collection used, press reports of small size, did not allow big di erences
between passage retrieval and document retrieval approaches. This fact was con rmed when
verifying that the results obtained by our system were similar to the baseline system (cosine
model) whereas when retrieving from Los Angeles Times collection the improvement achieved
by the passage approach was considerable.
Precision at N documents
Recall 5 10 20 30 200 AvgP Inc
Baseline 94.02 0.6000 0.5408 0.4582 0.4054 0.1826 0.4699 0.00
IR-n 7 sentences 94.54 0.6612 0.5796 0.4939 0.4490 0.1917 0.5039 7.23%
IR-n 8 sentences 94.95 0.6735 0.6061 0.4929 0.4537 0.1924 0.5017 6.76%
Table 1: Results for short questions
Precision at N documents
Recall 5 10 20 30 200 AvgP Inc
Baseline 95.62 0.6163 0.5612 0.4857 0.4367 0.1943 0.5010 0.00
IR-n 6 sentences 96.18 0.6653 0.5918 0.5020 0.4469 0.1995 0.5156 2.92%
IR-n 7 sentences 95.99 0.6816 0.5939 0.4990 0.4490 0.1983 0.5150 2.79%
Table 2: Results for long questions
- We could not make any previous experiment for determining the optimum size of the passage
since it was the rst time this approach was applied.
The main changes proposed for Clef-2002 were designed to solve these problems. Therefore,
the following changes were introduced:
- Documents and questions preprocess was improved.
- The Spanish lemmatizer was replaced by a simple stemmer.
- A serie of experiments was performed to determine the suitable size of the passages (the
number N of sentences).
- The relevance measure was modi ed in order to increase the score of the passages when a
sentence contained more than a consecutive word of the question.
- Long questions treatment was changed.
- We added a question expansion module that could be applied optionally.
3.1 Training process
We developed a serie of experiments in order to optimize system performance. These experiments
were carried out on the same document collection (EFE agency), but using the 49 test questions
proposed in Clef-2001.
As baseline system we selected the well-known document retrieval model based on the cosine
similarity measure [9]. The experiments were designed for detecting the best value for N (the
number of sentences that make up a passage). Initially, we detected the interval where the best
results were obtained and then, we proceeded to determine the optimum value for N. System
performance was measured using the standard average interpolated precision (AvgP).
For short questions, best results were obtained when passages were 7 or 8 sentences length.
For long questions, best results were achieved for passages of 6 or 7 sentences. Tables 1 and 2
show these results for short and long questions respectively.
In both cases better results are obtained although, the di erence with baseline is more con-
siderable when using long queries. After analysing these results, we determined to x the size of
passages to 7 sentences since this length achieved the best results for short questions and they
also were nearly the best for long queries.
Once we had determined the optimum length for passages, we designed a second experiment
for adapting the similarity measure described before in such a way that allowed increasing this
measure when more than one question term was found into a sentence and they presented the
Precision at N documents
Recall 5 10 20 30 200 AvgP Inc
IR-n base 94.54 0.6612 0.5796 0.4939 0.4490 0.1917 0.5039 0.00
IR-n factor 1.1 94.95 0.6653 0.5918 0.5041 0.4497 0.1935 0.5102 1.25%
IR-n factor 1.2 94.84 0.6694 0.5878 0.5010 0.4510 0.1933 0.5127 1.74%
IR-n factor 1.3 94.47 0.6735 0.5857 0.4990 0.4537 0.1930 0.5100 1.21%
IR-n factor 1.4 94.28 0.6653 0.5878 0.5041 0.4531 0.1914 0.5081 0.83%
Table 3: Results for short questions.
Precision at N documents
Recall 5 10 20 30 200 AvgP Inc
IR-n base 95.99 0.6816 0.5939 0.4990 0.4490 0.1983 0.5150 0.00
IR-n factor 1.1 95.88 0.6735 0.5898 0.5010 0.4510 0.1969 0.5098 -1.00%
IR-n factor 1.2 95.47 0.6694 0.5898 0.5082 0.4510 0.1959 0.5047 -2.00%
IR-n factor 1.3 95.40 0.6490 0.5959 0.5031 0.4524 0.1945 0.4975 -3.39%
IR-n factor 1.4 94.95 0.6449 0.6000 0.5061 0.4517 0.1919 0.4930 -4.27%
Table 4: Results for long questions.
same order in both question and sentence. This experiment consisted on optimizing the value
that increases the score of a question term when this circumstances happen. Thus, the passage
similarity formula previously mentioned changed as follows:
P assage similarity =
XW Wp;t q;t (2)
t2p^q
The factor takes value 1 for a term that appears into a sentence whose terms previous and
later in the question are not in the same phrase, and another value in the opposite case. This
experiment has applied several coeÆcients in order to obtain the optimum value for . Tables 3
and 4 shows the results obtained for short and long questions respectively.
In these tables it is possible to observe that, for short questions, results improve for values
of 1.1 and 1.2 whereas results slightly get worse for long questions.
4 Clef-2002: Experiments and Results
As the results obtained in Clef-2001 for monolingual task were not the expected, this year our
participation was focused to improve the Spanish monolingual task.
4.1 Runs Description
We carried out four runs for monolingual task. Two with title + description and two with title +
description + narrative. For all the runs passage length was set to 7 sentences and the value 1.1
was assigned to the proximity coeÆcient. These runs are described below.
To clarify the di erences between the four runs we will consider the following example question:
C103
Con icto de intereses en Italia
Encontrar documentos que discutan el problema del con icto de interes es del primer
ministro italiano, Silvio Berlusconi.
Los documentos relevantes se referiran de forma explcita al con icto de intereses
entre el Berlusconi poltico y cabeza del gobierno italiano, y el Berlusconi hombre de negocios.
Tambien pueden incluir informacion sobre propuestas o soluciones adoptadas para resolver este
con icto.
4.1.1 IR-n1.
This run takes only short questions (title + description). The example question was processed as
follows:
Con icto de intereses en Italia. Encontrar documentos que discutan el problema del con icto
de intereses del primer ministro italiano, Silvio Berlusconi.
4.1.2 IR-n2.
This run is a little more complex. The question is divided into several queries. Each query
contains an isolated idea appearing into the whole question. Then each query is posed for retrieval,
evaluating this way, how passages respond to each of them. This approach is fully described in [7]
and basic steps are summed up as follows:
1. Question narrative is divided according to the sentences it contains.
2. The system generates as many queries as sentences are detected. Each query contains title
,description and a sentence of the narrative.
3. Each generated query is processed separately recovering best 5,000 documents.
4. Relevant documents are punctuated with the maximum similarity value obtained for all the
generated queries processed.
5. Best 1,000 relevant documents are nally retrieved.
In this case, from the example question described before the system generates the following
two queries:
Query 1. Con icto de intereses en Italia. Encontrar documentos que discutan el problema del
con icto de interes es del primer ministro italiano, Silvio Berlusconi. Los documentos relevantes
se referiran de forma explcita al con icto de intereses entre el Berlusconi poltico y cabeza del
gobierno italiano, y el Berlusconi hombre de negocios.
Query 2. Con icto de intereses en Italia. Encontrar documentos que discutan el problema
del con icto de interes es del primer ministro italiano, Silvio Berlusconi.Tambien pueden incluir
informacion sobre propuestas o soluciones adoptadas para resolver este con icto.
4.1.3 IR-n3.
This run is similar to IR-n1 but applies query expansion according to the model de ned in [4].
This expansion consists on detecting the 10 more excellent terms of rst 5 recovered documents,
and adding them to the original question.
4.1.4 IR-n4.
This run uses long questions formed by title, description and narrative. The example questions
was posed for retrieval as follows.
Con icto de intereses en Italia. Encontrar documentos que discutan el problema del con icto de
interes es del primer ministro italiano, Silvio Berlusconi. Los documentos relevantes se referiran
de forma explcita al con icto de intereses entre el Berlusconi poltico y cabeza del gobierno italiano,
y el Berlusconi hombre de negocios. Tambien pueden incluir informacion sobre propuestas o
soluciones adoptadas para resolver este con icto.
Precision at N documents
Recall 5 10 20 30 200 AvgP Inc
Median clef2002 systems 0.4490 0.00
IR-n1 90.08 0.6800 0.5820 0.5140 0.4620 0.1837 0.4684 +4.32%
IR-n2 92.64 0.7200 0.6380 0.5600 0.4813 0.1898 0.5067 +12.85%
IR-n3 93.51 0.6920 0.5920 0.5190 0.4667 0.2018 0.4980 +10.91%
IR-n4 91.83 0.7120 0.6120 0.5380 0.4867 0.1936 0.4976 +10.82%
Table 5: Results comparison.
4.2 Results
In this section the results achieved by our four runs are compared with the obtained by all the
systems that participated at this conference. Table 5 shows the average precision for monolin-
gual runs and computes the increment of precision achieved. This increment (or decrement) was
calculated by taking as base the median average precision of all participant systems.
As it can be observed, our four runs performed better than median results. Our baseline
(IR-n1) improved around a 4% and the remaining runs performed better between 11 and 13%.
5 Conclusions and Future Work
General conclusions are positive. We have obtained considerably better results than in previous
edition. This fact has been caused mainly by three aspects. First, the better preprocessing of
documents carried out. Second, the system has been correctly trained to obtain the optimum size
of passage. Third, the errors introduced by the Spanish lemmatizer have been avoided by using a
simple stemmer.
After this new experience, we are examining several lines of future work. We want to analyse
the possible improvements that could be obtained using another type of lemmatizer instead of the
simple stemmer that we have used this year. On the other hand we are going to continue studying
modi cations for the relevance formula in order to improve the application of vicinity factors.
6 Acknowledgements
This work has been supported by the Spanish Government (CICYT) with grant TIC2000-0664-
C02-02.
References
[1] James P. Callan. Passage-Level Evidence in Document Retrieval. In Proceedings of the 17th An-
nual International Conference on Research and Development in Information Retrieval, pages
302{310, London, UK, July 1994. Springer Verlag.
[2] J. Allan G. Salton and C. Buckley. Approaches to passage retrieval in full text information
systems. In Sixteenth International ACM SIGIR Conference on Research and Development in
Information Retrieval, pages 49{58, Pittsburgh, PA, jun 1993.
[3] Marti A. Hearst and Christian Plaunt. Subtopic structuring for full-length document access.
In Proc. 16th ACM SIGIR Conf. Research and Development in Information Retrieval, pages
59{68, 1993.
[4] P. Jourlin, S.E. Johnson, K. Sparck Jones, and P.C. Woodland. General query expansion
techniques for spoken document retrieval. In Proc. ESCA Workshop on Extracting Information
from Spoken Audio, pages 8{13, Cambridge, UK, 1999.
[5] Marcin Kaszkiel and Justin Zobel. Passage Retrieval Revisited. In Proceedings of the 20th
Annual International ACM SIGIR Conference on Research and Development in Information
Retrieval, Text Structures, pages 178{185, Philadelphia, PA, USA, 1997.
[6] Marcin Kaszkiel and Justin Zobel. E ective Ranking with Arbitrary Passages. Journal of the
American Society for Information Science (JASIS), 52(4):344{364, February 2001.
[7] Fernando Llopis, Antonio Ferrandez, and Jose L. Vicedo. Using Long Queries in a Passage
Retrieval System. In O. Cairo, E. L. Sucar, and F. J. Cantu, editors, Proceeding of Mexican
International Conference on Arti cial Intelligence, volume 2313 of Lectures Notes in Arti cial
Intelligence, Merida, Mexico, 2002. Springer-Verlag.
[8] Fernando Llopis and Jose L. Vicedo. IR-n system, a passage retrieval systema at CLEF 2001.
In Workshop of Cross-Language Evaluation Forum (CLEF 2001), Lecture notes in Computer
Science, Darmstadt, Germany, 2001. Springer-Verlag.
[9] Gerard A. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval
of Information by Computer. Addison Wesley, New York, 1989.