Proceedings of the 43rd Annual Meeting of the ACL, pages 483–490,
Ann Arbor, June 2005. c©2005 Association for Computational Linguistics
Multi-Field Information Extraction and Cross-Document Fusion
Gideon S. Mann and David Yarowsky
Department of Computer Science
The Johns Hopkins University
Baltimore, MD 21218 USA
{gsm,yarowsky}@cs.jhu.edu
Abstract
In this paper, we examine the task of extracting a
set of biographic facts about target individuals from
a collection of Web pages. We automatically anno-
tate training text with positive and negative exam-
ples of fact extractions and train Rote, Na¨ıve Bayes,
and Conditional Random Field extraction models
for fact extraction from individual Web pages. We
then propose and evaluate methods for fusing the
extracted information across documents to return a
consensus answer. A novel cross-field bootstrapping
method leverages data interdependencies to yield
improved performance.
1 Introduction
Much recent statistical information extraction re-
search has applied graphical models to extract in-
formation from one particular document after train-
ing on a large corpus of annotated data (Leek, 1997;
Freitag and McCallum, 1999).1 Such systems are
widely applicable, yet there remain many informa-
tion extraction tasks that are not readily amenable to
these methods. Annotated data required for training
statistical extraction systems is sometimes unavail-
able, while there are examples of the desired infor-
mation. Further, the goal may be to find a few inter-
related pieces of information that are stated multiple
times in a set of documents.
Here, we investigate one task that meets the above
criteria. Given the name of a celebrity such as
1Alternatively, Riloff (1996) trains on in-domain and
out-of-domain texts and then has a human filtering step.
Huffman (1995) proposes a method to train a different type of
extraction system by example.
“Frank Zappa”, our goal is to extract a set of bio-
graphic facts (e.g., birthdate, birth place and occupa-
tion) about that person from documents on the Web.
First, we describe a general method of automatic
annotation for training from positive and negative
examples and use the method to train Rote, Na¨ıve
Bayes, and Conditional Random Field models (Sec-
tion 2). We then examine how multiple extractions
can be combined to form one consensus answer
(Section 3). We compare fusion methods and show
that frequency voting outperforms the single high-
est confidence answer by an average of 11% across
the various extractors. Increasing the number of re-
trieved documents boosts the overall system accu-
racy as additional documents which mention the in-
dividual in question lead to higher recall. This im-
proved recall more than compensates for a loss in
per-extraction precision from these additional doc-
uments. Next, we present a method for cross-field
bootstrapping (Section 4) which improves per-field
accuracy by 7%. We demonstrate that a small train-
ing set with only the most relevant documents can be
as effective as a larger training set with additional,
less relevant documents (Section 5).
2 Training by Automatic Annotation
Typically, statistical extraction systems (such as
HMMs and CRFs) are trained using hand-annotated
data. Annotating the necessary data by hand is time-
consuming and brittle, since it may require large-
scale re-annotation when the annotation scheme
changes. For the special case of Rote extrac-
tors, a more attractive alternative has been proposed
by Brin (1998), Agichtein and Gravano (2000), and
Ravichandran and Hovy (2002).
483
Essentially, for any text snippet of the form
A1pA2qA3, these systems estimate the probability
that a relationship r(p,q) holds between entities p
and q, given the interstitial context, as2
P(r(p,q) | pA2q) = P(r(p,q) | pA2q)
=
summationtext
x,y∈T c(xA2y)summationtext
x c(xA2)
That is, the probability of a relationship r(p,q) is
the number of times that pattern xA2y predicts any
relationship r(x,y) in the training set T. c(.) is the
count. We will refer to x as the hook3 and y as the
target. In this paper, the hook is always an indi-
vidual. Training a Rote extractor is straightforward
given a set T of example relationships r(x,y). For
each hook, download a separate set of relevant doc-
uments (a hook corpus, Dx) from the Web.4 Then
for any particular pattern A2 and an element x, count
how often the pattern xA2 predicts y and how often
it retrieves a spurious ¯y.5
This annotation method extends to training other
statistical models with positive examples, for exam-
ple a Na¨ıve Bayes (NB) unigram model. In this
model, instead of looking for an exact A2 pattern
as above, each individual word in the pattern A2 is
used to predict the presence of a relationship.
P(r(p,q) | pA2q)
∝P(pA2q | r(p,q))P(r(p,q))
=P(A2 | r(p,q))
=
productdisplay
a∈A2
P(a | r(p,q))
We perform add-lambda smoothing for out-of-
vocabulary words and thus assign a positive prob-
ability to any sequence. As before, a set of relevant
2The above Rote models also condition on the preceding and
trailing words, for simplicity we only model interstitial words
A2.
3Following (Ravichandran and Hovy, 2002).
4In the following experiments we assume that there is one
main object of interest p, for whom we want to find certain
pieces of information r(p,q), where r denotes the type of re-
lationship (e.g., birthday) and q is a value (e.g., May 20th). We
require one hook corpus for each hook, not a separate one for
each relationship.
5Having a functional constraint ∀¯q negationslash= q,¯r(p, ¯q) makes this
estimate much more reliable, but it is possible to use this method
of estimation even when this constraint does not hold.
documents is downloaded for each particular hook.
Then every hook and target is annotated. From that
markup, we can pick out the interstitial A2 patterns
and calculate the necessary probabilities.
Since the NB model assigns a positive probability
to every sequence, we need to pick out likely tar-
gets from those proposed by the NB extractor. We
construct a background model which is a basic un-
igram language model, P(A2) = producttexta∈A2 P(a). We
then pick targets chosen by the confidence estimate
CNB(q) = logP(A2 | r(p,q))P(A
2)
However, this confidence estimate does not work-
well in our dataset.
We propose to use negative examples to estimate
P(A2 | ¯r(p,q))6 as well as P(A2 | r(p,q)). For
each relationship, we define the target set Er to be
all potential targets and model it using regular ex-
pressions.7 In training, for each relationship r(p,q),
we markup the hook p, the target q, and all spuri-
ous targets (¯q ∈ {Er −q}) which provide negative
examples. Targets can then be chosen with the fol-
lowing confidence estimate
CNB+E(q) = logP(A2 | r(p,q))P(A
2 | ¯r(p,q))
We call this NB+E in the following experiments.
The above process describes a general method for
automatically annotating a corpus with positive and
negative examples, and this corpus can be used to
train statistical models that rely on annotated data.8
In this paper, we test automatic annotation using
Conditional Random Fields (CRFs) (Lafferty et al.,
2001) which have achieved high performance for in-
formation extraction. CRFs are undirected graphical
models that estimate the conditional probability of a
state sequence given an output sequence
P(s | o) = 1Z exp
parenleftbigg Tsummationdisplay
t=1
summationdisplay
k
λkfk(st−1,st,o,t)
parenrightbigg
6¯r stands in for all other possible relationships (including no
relationship) between p and q. P(A2 | ¯r(p,q)) is estimated as
P(A2 | r(p,q)) is, except with spurious targets.
7e.g., Ebirthyear = {\d\d\d\d}. This is the only source of
human knowledge put into the system and required only around
4 hours of effort, less effort than annotating an entire corpus or
writing information extraction rules.
8This corpus markup gives automatic annotation that yields
noisier training data than manual annotation would.
484
p qA_2
B
p
A_2
A_2
q
B
q
Figure 1: CRF state-transition graphs for extracting a relation-
ship r(p,q) from a sentence pA2q. Left: CRF Extraction with
a background model (B). Right: CRF+E As before but with
spurious target prediction (pA2¯q).
We use the Mallet system (McCallum, 2002) for
training and evaluation of the CRFs. In order to ex-
amine the improvement by using negative examples,
we train CRFs with two topologies (Figure 1). The
first, CRF, models the target relationship and back-
ground sequences and is trained on a corpus where
targets (positive examples) are annotated. The sec-
ond, CRF+E, models the target relationship, spu-
rious targets and background sequences, and it is
trained on a corpus where targets (positive exam-
ples) as well as spurious targets (negative examples)
are annotated.
Experimental Results
To test the performance of the different ex-
tractors, we collected a set of 152 semi-
structured mini-biographies from an online site
(www.infoplease.com), and used simple rules to
extract a biographic fact database of birthday and
month (henceforth birthday), birth year, occupation,
birth place, and year of death (when applicable).
An example of the data can be found in Table
1. In our system, we normalized birthdays, and
performed capitalization normalization for the
remaining fields. We did no further normalization,
such as normalizing state names to their two letter
acronyms (e.g., California → CA). Fifteen names
were set aside as training data, and the rest were
used for testing. For each name, 150 documents
were downloaded from Google to serve as the hook
corpus for either training or testing.9
In training, we automatically annotated docu-
ments using people in the training set as hooks, and
in testing, tried to get targets that exactly matched
what was present in the database. This is a very strict
method of evaluation for three reasons. First, since
the facts were automatically collected, they contain
9Name polyreference, along with ranking errors, result in
the retrieval of undesired documents.
Aaron Neville Frank Zappa
Birthday January 24 December 21
Birth year 1941 1940
Occupation Singer Musician
Birthplace New Orleans Baltimore,Maryland
Year of Death - 1993
Table 1: Two of 152 entries in the Biographic Database. Each
entry contains incomplete information about various celebrities.
Here, Aaron Neville’s birth state is missing, and Frank Zappa
could be equally well described as a guitarist or rock-star.
errors and thus the system is tested against wrong
answers.10 Second, the extractors might have re-
trieved information that was simply not present in
the database but nevertheless correct (e.g., some-
one’s occupation might be listed as writer and the
retrieved occupation might be novelist). Third, since
the retrieved targets were not normalized, there sys-
tem may have retrieved targets that were correct but
were not recognized (e.g., the database birthplace is
New York, and the system retrieves NY).
In testing, we rejected candidate targets that were
not present in our target set models Er. In some
cases, this resulted in the system being unable to find
the correct target for a particular relationship, since
it was not in the target set.
Before fusion (Section 3), we gathered all the
facts extracted by the system and graded them in iso-
lation. We present the per-extraction precision
Pre-Fusion Precision = # Correct Extracted Targets# Total Extracted Targets
We also present the pseudo-recall, which is the av-
erage number of times per person a correct target
was extracted. It is difficult to calculate true re-
call without manual annotation of the entire corpus,
since it cannot be known for certain how many times
the document set contains the desired information.11
Pre-Fusion Pseudo-Recall = # Correct Extracted Targets#People
The precision of each of the various extraction
methods is listed in Table 2. The data show that
on average the Rote method has the best precision,
10These deficiencies in testing also have implications for
training, since the models will be trained on annotated data that
has errors. The phenomenon of missing and inaccurate data
was most prevalent for occupation and birthplace relationships,
though it was observed for other relationships as well.
11It is insufficient to count all text matches as instances that
the system should extract. To obtain the true recall, it is nec-
essary to decide whether each sentence contains the desired re-
lationship, even in cases where the information is not what the
biographies have listed.
485
Birthday Birth year Occupation Birthplace Year of Death Avg.
Rote .789 .355 .305 .510 .527 .497
NB+E .423 .361 .255 .217 .088 .269
CRF .509 .342 .219 .139 .267 .295
CRF+E .680 .654 .246 .357 .314 .450
Table 2: Pre-Fusion Precision of extracted facts for various extraction systems, trained on 15 people each with 150 documents, and
tested on 137 people each with 150 documents.
Birthday Birth year Occupation Birthplace Year of Death Avg.
Rote 4.8 1.9 1.5 1.0 0.1 1.9
NB+E 9.6 11.5 20.3 11.3 0.7 10.9
CRF 3.0 16.3 31.1 10.7 3.2 12.9
CRF+E 6.8 9.9 3.2 3.6 1.4 5.0
Table 3: Pre-Fusion Pseudo-Recall of extract facts with the identical training/testing set-up as above.
while the NB+E extractor has the worst. Train-
ing the CRF with negative examples (CRF+E) gave
better precision in extracted information then train-
ing it without negative examples. Table 3 lists the
pseudo-recall or average number of correctly ex-
tracted targets per person. The results illustrate that
the Rote has the worst pseudo-recall, and the plain
CRF, trained without negative examples, has the best
pseudo-recall.
To test how the extraction precision changes as
more documents are retrieved from the ranked re-
sults from Google, we created retrieval sets of 1, 5,
15, 30, 75, and 150 documents per person and re-
peated the above experiments with the CRF+E ex-
tractor. The data in Figure 2 suggest that there is a
gradual drop in extraction precision throughout the
corpus, which may be caused by the fact that doc-
uments further down the retrieved list are less rele-
vant, and therefore less likely to contain the relevant
biographic data.
Pre−Fusion Precision # Retrieved Documents per Person 80  160 140
 0.9 0.8 0.7
 0.6 0.5 0.4
 0.3 0.2
 1
 60 40 20  120 0  100
Birthday
BirthplaceBirthyearOccupation
Deathyear
Figure 2: As more documents are retrieved per person, pre-
fusion precision drops.
However, even though the extractor’s precision
drops, the data in Figure 3 indicate that there con-
tinue to be instances of the relevant biographic data.
# Retrieved Documents Per PersonPre−Fusion Pseudo−Recall
 1 2 3 4
 5 6 7 8
 9 10
 0 0 20 40 60 80 100 120 140 160
Figure 3: Pre-fusion pseudo-recall increases as more documents
are added.
3 Cross-Document Information Fusion
The per-extraction performance was presented in
Section 2, but the final task is to find the single cor-
rect target for each person.12 In this section, we ex-
amine two basic methodologies for combining can-
didate targets. Masterson and Kushmerick (2003)
propose Best which gives each candidate a
score equal to its highest confidence extraction:
Best(x) = argmax
x
C(x).13 We further consider
Voting, which counts the number of times each can-
didate x was extracted: Vote(x) = |C(x) > 0|.
Each of these methods ranks the candidate targets
by score and chooses the top-ranked one.
The experimental setup used in the fusion exper-
iments was the same as before: training on 15 peo-
ple, and testing on 137 people. However, the post-
fusion evaluation differs from the pre-fusion evalua-
tion. After fusion, the system returns one consensus
target for each person and thus the evaluation is on
the accuracy of those targets. That is, missing tar-
12This is a simplifying assumption, since there are many
cases where there might exist multiple possible values, e.g., a
person may be both a writer and a musician.
13C(x) is either the confidence estimate (NB+E) or the prob-
ability score (Rote,CRF,CRF+E).
486
Best Vote
Rote .364 .450
NB+E .385 .588
CRF .513 .624
CRF+E .650 .678
Table 4: Average Accuracy of the Highest Confidence (Best)
and Most Frequent (Vote) across five extraction fields.
gets are graded as wrong.14
Post-Fusion Accuracy = # People with Correct Target# People
Additionally, since the targets are ranked, we also
calculated the mean reciprocal rank (MRR).15 The
data in Table 4 show the average system perfor-
mance with the different fusion methods. Frequency
voting gave anywhere from a 2% to a 20% improve-
ment over picking the highest confidence candidate.
CRF+E (the CRF trained with negative examples)
was the highest performing system overall.
Birth Day
Fusion Accuracy Fusion MRR
Rote Vote .854 .877
NB+E Vote .854 .889
CRF Vote .650 .703
CRF+E Vote .883 .911
Birth year
Rote Vote .387 .497
NB+E Vote .778 .838
CRF Vote .796 .860
CRF+E Vote .869 .876
Occupation
Rote Vote .299 .405
NB+E Vote .642 .751
CRF Vote .606 .740
CRF+E Vote .423 .553
Birthplace
Rote Vote .321 .338
NB+E Vote .474 .586
CRF Vote .321 .476
CRF+E Vote .467 .560
Year of Death
Rote Vote .389 .389
NB+E Vote .194 .383
CRF .750 .840
CRF+E Vote .750 .827
Table 5: Voting for information fusion, evaluated per person.
CRF+E has best average performance (67.8%).
Table 5 shows the results of using each of these
extractors to extract correct relationships from the
top 150 ranked documents downloaded from the
14For year of death, we only graded cases where the person
had died.
15The reciprocal rank = 1 / the rank of the correct target.
Web. CRF+E was a top performer in 3/5 of the
cases. In the other 2 cases, the NB+E was the most
successful, perhaps because NB+E’s increased re-
call was more useful than CRF+E’s improved pre-
cision.
Retrieval Set Size and Performance
As with pre-fusion, we performed a set of exper-
iments with different retrieval set sizes and used
the CRF+E extraction system trained on 150 docu-
ments per person. The data in Figure 4 show that
performance improves as the retrieval set size in-
creases. Most of the gains come in the first 30 doc-
uments, where average performance increased from
14% (1 document) to 63% (30 documents). Increas-
ing the retrieval set size to 150 documents per person
yielded an additional 5% absolute improvement.
Occupation
BirthyearBirthdayDeathyear
Birthplace
Figure 4: Fusion accuracy increases with more documents per
person
Post-fusion errors come from two major sources.
The first source is the misranking of correct relation-
ships. The second is the case where relevant infor-
mation is not retrieved at all, which we measure as
Post-Fusion Missing = # Missing Targets# People
The data in Figure 5 suggest that the decrease in
missing targets is a significant contributing factor
to the improvement in performance with increased
document size. Missing targets were a major prob-
lem for Birthplace, constituting more than half the
errors (32% at 150 documents).
4 Cross-Field Bootstrapping
Sections 2 and 3 presented methods for training sep-
arate extractors for particular relationships and for
doing fusion across multiple documents. In this sec-
tion, we leverage data interdependencies to improve
performance.
The method we propose is to bootstrap across
fields and use knowledge of one relationship to im-
prove performance on the extraction of another. For
487
# Retrieved Documents Per Person
Post−Fusion Missing Targets
 0 0.1 0.2 0.3
 0.4 0.5 0.6 0.7
 0.8 0.9 1
 20 0  40 60 80 100 120 140 160
BirthplaceOccupation
DeathyearBirthday
Birthyear
Figure 5: Additional documents decrease the number of post-
fusion missing targets, targets which are never extracted in any
document.
Birth year
Extraction Precision Fusion Accuracy
CRF .342 .797
+ birthday .472 .861
CRF+E .654 .869
+ birthday .809 .891
Occupation
Extraction Precision Fusion Accuracy
CRF .219 .606
+ birthday .217 .569
+ birth year(f) 21.9 .599
+ all .214 .591
CRF+E .246 .423
+ birthday .325 .577
+ birth year(f) .387 .672
+ all .382 .642
Birthplace
Extraction Precision Fusion Accuracy
CRF .139 .321
+ birthday .158 .372
+ birth year(f) .156 .350
CRF+E .357 .467
+ birthday .350 .474
+ birth year(f) .294 .350
+ occupation(f) .314 .354
+ all .362 .532
Table 6: Performance of Cross-Field Bootstrapping Models.
(f) indicates that the best fused result was taken. birth year(f)
means birth years were annotated using the system that discov-
ered the most accurate birth years.
example, to extract birth year given knowledge of
the birthday, in training we mark up each hook cor-
pus Dx with the known birthday b : birthday(x,b)
and the target birth year y : birthyear(x,y) and
add an additional feature to the CRF that indicates
whether the birthday has been seen in the sentence.16
In testing, for each hook, we first find the birthday
using the methods presented in the previous sec-
tions, annotate the corpus with the extracted birth-
day, and then apply the birth year CRF (see Figure 6
next page).
16The CRF state model doesn’t change. When bootstrapping
from multiple fields, we add the conjunctions of the fields as
features.
Table 6 shows the effect of using this bootstrapped
data to estimate other fields. Based on the relative
performance of each of the individual extraction sys-
tems, we chose the following schedule for perform-
ing the bootstrapping: 1) Birthday, 2) Birth year, 3)
Occupation, 4) Birthplace. We tried adding in all
knowledge available to the system at each point in
the schedule.17 There are gains in accuracy for birth
year, occupation and birthplace by using cross-field
bootstrapping. The performance of the plain CRF+E
averaged across all five fields is 67.4%, while for the
best bootstrapped system it is 74.6%, a gain of 7%.
Doing bootstrapping in this way improves for
people whose information is already partially cor-
rect. As a result, the percentage of people who
have completely correct information improves to
37% from 13.8%, a gain of 24% over the non-
bootstrapped CRF+E system. Additionally, erro-
neous extractions do not hurt accuracy on extraction
of other fields. Performance in the bootstrapped sys-
tem for birthyear, occupation and birth place when
the birthday is wrong is almost the same as perfor-
mance in the non-bootstrapped system.
5 Training Set Size Reduction
One of the results from Section 2 is that lower
ranked documents are less likely to contain the rel-
evant biographic information. While this does not
have an dramatic effect on the post-fusion accuracy
(which improves with more documents), it suggests
that training on a smaller corpus, with more relevant
documents and more sentences with the desired in-
formation, might lead to equivalent or improved per-
formance. In a final set of experiments we looked at
system performance when the extractor is trained on
fewer than 150 documents per person.
The data in Figure 7 show that training on 30 doc-
uments per person yields around the same perfor-
mance as training on 150 documents per person. Av-
erage performance when the system was trained on
30 documents per person is 70%, while average per-
formance when trained on 150 documents per per-
son is 68%. Most of this loss in performance comes
from losses in occupation, but the other relationships
17This system has the extra knowledge of which fused
method is the best for each relationship. This was assessed by
inspection.
488
Frank Zappa was born on December 21.
1. BirthdayZappa : December 21, 1940.
2. Birthyear1. Birthday
2. Birthyear 3. Birthplace
Zappa was born in 1940 in Baltimore. 
Figure 6: Cross-Field Bootstrapping: In step (1) The birthday,
December 21, is extracted and the text marked. In step 2, cooc-
currences with the discovered birthday make 1940 a better can-
didate for birthyear. In step (3), the discovered birthyear ap-
pears in contexts where the discovered birthday does not and
improves extraction of birth place.
Post−Fusion Accuracy # Training Documents Per Person 0.2
 0.3 0.4 0.5
 0.6 0.7 0.8
 0.9
 0 20 40 60 80 100 120 140 160
BirthdayBirthyear
DeathyearOccupation
Birthplace
Figure 7: Fusion accuracy doesn’t improve with more than 30
training documents per person.
have either little or no gain from training on addi-
tional documents. There are two possible reasons
why more training data may not help, and even may
hurt performance.
One possibility is that higher ranked retrieved
documents are more likely to contain biographical
facts, while in later documents it is more likely that
automatically annotated training instances are in fact
false positives. That is, higher ranked documents are
cleaner training data. Pre-Fusion precision results
(Figure 8) support this hypothesis since it appears
that later instances are often contaminating earlier
models.
Pre−Fusion Precision # Training Documents Per Person 0
 0.1 0.2 0.3
 0.4 0.5 0.6
 0.7 0.8
 0 20 40 60 80 100 120 140 160
BirthdayBirthyear
Birthplace
OccupationDeathyear
Figure 8: Pre-Fusion precision shows slight drops with in-
creased training documents.
The data in Figure 9 suggest an alternate possibil-
ity that later documents also shift the prior toward
a model where it is less likely that a relationship is
observed as fewer targets are extracted.
Pre−Fusion Pseudo−Recall # Training Documents Per Person
 0 1 2 3
 4 5 6 7
 8 9 10 11
 0 20 40 60 80 100 120 140 160
Birthday
BirthplaceDeathyear
Birthyear
Occupation
Figure 9: Pre-Fusion Pseudo-Recall also drops with increased
training documents.
6 Related Work
The closest related work to the task of biographic
fact extraction was done by Cowie et al. (2000) and
Schiffman et al. (2001), who explore the problem of
biographic summarization.
There has been rather limited published
work in multi-document information extrac-
tion. The closest work to what we present here is
Masterson and Kushmerick (2003), who perform
multi-document information extraction trained on
manually annotated training data and use Best
Confidence to resolve each particular template slot.
In summarizarion, many systems have examined
the multi-document case. Notable systems are
SUMMONS (Radev and McKeown, 1998) and
RIPTIDE (White et al., 2001), which assume per-
fect extracted information and then perform closed
domain summarization. Barzilay et al. (1999) does
not explicitly extract facts, but instead picks out
relevant repeated elements and combines them to
obtain a summary which retains the semantics of
the original.
In recent question answering research, informa-
tion fusion has been used to combine multiple
candidate answers to form a consensus answer.
Clarke et al. (2001) use frequency of n-gram occur-
rence to pick answers for particular questions. An-
other example of answer fusion comes in (Brill et
al., 2001) which combines the output of multiple
question answering systems in order to rank an-
swers. Dalmas and Webber (2004) use a WordNet
cover heuristic to choose an appropriate location
from a large candidate set of answers.
There has been a considerable amount of work in
training information extraction systems from anno-
tated data since the mid-90s. The initial work in the
field used lexico-syntactic template patterns learned
using a variety of different empirical approaches
(Riloff and Schmelzenbach, 1998; Huffman, 1995;
489
Soderland et al., 1995). Seymore et al. (1999) use
HMMs for information extraction and explore ways
to improve the learning process.
Nahm and Mooney (2002) suggest a method to
learn word-to-word relationships across fields by do-
ing data mining on information extraction results.
Prager et al. (2004) uses knowledge of birth year to
weed out candidate years of death that are impos-
sible. Using the CRF extractors in our data set,
this heuristic did not yield any improvement. More
distantly related work for multi-field extraction sug-
gests methods for combining information in graphi-
cal models across multiple extraction instances (Sut-
ton et al., 2004; Bunescu and Mooney, 2004) .
7 Conclusion
This paper has presented new experimental method-
ologies and results for cross-document information
fusion, focusing on the task of biographic fact ex-
traction and has proposed a new method for cross-
field bootstrapping. In particular, we have shown
that automatic annotation can be used effectively
to train statistical information extractors such Na¨ıve
Bayes and CRFs, and that CRF extraction accuracy
can be improved by 5% with a negative example
model. We looked at cross-document fusion and
demonstrated that voting outperforms choosing the
highest confidence extracted information by 2% to
20%. Finally, we introduced a cross-field bootstrap-
ping method that improved average accuracy by 7%.
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