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Using Predicate-Argument Structures for Information Extraction
Mihai Surdeanu and Sanda Harabagiu and John Williams and Paul Aarseth
Language Computer Corp.
Richardson, Texas 75080, USA
mihai,sanda@languagecomputer.com
Abstract
In this paper we present a novel, cus-
tomizable IE paradigm that takes advan-
tage of predicate-argument structures. We
also introduce a new way of automatically
identifying predicate argument structures,
which is central to our IE paradigm. It is
based on: (1) an extended set of features;
and (2) inductive decision tree learning.
The experimental results prove our claim
that accurate predicate-argument struc-
tures enable high quality IE results.
1 Introduction
The goal of recent Information Extraction (IE)
tasks was to provide event-level indexing into news
stories, including news wire, radio and television
sources. In this context, the purpose of the HUB
Event-99 evaluations (Hirschman et al., 1999) was
to capture information on some newsworthy classes
of events, e.g. natural disasters, deaths, bombings,
elections, financial fluctuations or illness outbreaks.
The identification and selective extraction of rele-
vant information is dictated by templettes. Event
templettes are frame-like structures with slots rep-
resenting the event basic information, such as main
event participants, event outcome, time and loca-
tion. For each type of event, a separate templette
is defined. The slots fills consist of excerpts from
text with pointers back into the original source mate-
rial. Templettes are designed to support event-based
browsing and search. Figure 1 illustrates a templette
defined for “market changes” as well as the source
of the slot fillers.
<MARKET_CHANGE_PRI199804281700.1717−1>:=
CURRENT_VALUE: $308.45
LOCATION: London
DATE: daily
INSTRUMENT: London [gold]
AMOUNT_CHANGE: fell [$4.70] cents
London gold fell $4.70 cents to $308.35
Time for our daily market report from NASDAQ.
Figure 1: Templette filled with information about a
market change event.
To date, some of the most successful IE tech-
niques are built around a set of domain relevant lin-
guistic patterns based on select verbs (e.g. fall, gain
or lose for the “market change” topic). These pat-
terns are matched against documents for identifying
and extracting domain-relevant information. Such
patterns are either handcrafted or acquired automat-
ically. A rich literature covers methods of automati-
cally acquiring IE patterns. Some of the most recent
methods were reported in (Riloff, 1996; Yangarber
et al., 2000).
To process texts efficiently and fast, domain pat-
terns are ideally implemented as finite state au-
tomata (FSAs), a methodology pioneered in the
FASTUS IE system (Hobbs et al., 1997). Although
this paradigm is simple and elegant, it has the dis-
advantage that it is not easily portable from one do-
main of interest to the next.
In contrast, a new, truly domain-independent IE
paradigm may be designed if we know (a) predicates
relevant to a domain; and (b) which of their argu-
ments fill templette slots. Central to this new way
of extracting information from texts are systems that
label predicate-argument structures on the output of
full parsers. One such augmented parser, trained on
data available from the PropBank project has been
recently presented in (Gildea and Palmer, 2002).
In this paper we describe a domain-independent IE
paradigm that is based on predicate-argument struc-
tures identified automatically by two different meth-
ods: (1) the statistical method reported in (Gildea
and Palmer, 2002); and (2) a new method based
on inductive learning which obtains 17% higher F-
score over the first method when tested on the same
data. The accuracy enhancement of predicate argu-
ment recognition determines up to 14% better IE re-
sults. These results enforce our claim that predicate
argument information for IE needs to be recognized
with high accuracy.
The remainder of this paper is organized as fol-
lows. Section 2 reports on the parser that produces
predicate-argument labels and compares it against
the parser introduced in (Gildea and Palmer, 2002).
Section 3 describes the pattern-free IE paradigm and
compares it against FSA-based IE methods. Section
4 describes the integration of predicate-argument
parsers into the IE paradigm and compares the re-
sults against a FSA-based IE system. Section 5 sum-
marizes the conclusions.
2 Learning to Recognize
Predicate-Argument Structures
2.1 The Data
Proposition Bank or PropBank is a one mil-
lion word corpus annotated with predicate-
argument structures. The corpus consists of
the Penn Treebank 2 Wall Street Journal texts
(www.cis.upenn.edu/
treebank). The PropBank
annotations, performed at University of Pennsyl-
vania (www.cis.upenn.edu/ ace) were described
in (Kingsbury et al., 2002). To date PropBank has
addressed only predicates lexicalized by verbs,
proceeding from the most to the least common
verbs while annotating verb predicates in the
corpus. For any given predicate, a survey was made
to determine the predicate usage and if required, the
usages were divided in major senses. However, the
senses are divided more on syntactic grounds than
VPNP
S
VP
PP
NP
Big Board floor traders
ARG0
byassailed
P
wasThe futures halt
ARG1
Figure 2: Sentence with annotated arguments
semantic, under the fundamental assumption that
syntactic frames are direct reflections of underlying
semantics.
The set of syntactic frames are determined by
diathesis alternations, as defined in (Levin, 1993).
Each of these syntactic frames reflect underlying
semantic components that constrain allowable ar-
guments of predicates. The expected arguments
of each predicate are numbered sequentially from
Arg0 to Arg5. Regardless of the syntactic frame
or verb sense, the arguments are similarly labeled
to determine near-similarity of the predicates. The
general procedure was to select for each verb the
roles that seem to occur most frequently and use
these roles as mnemonics for the predicate argu-
ments. Generally, Arg0 would stand for agent,
Arg1 for direct object or theme whereas Arg2 rep-
resents indirect object, benefactive or instrument,
but mnemonics tend to be verb specific. For
example, when retrieving the argument structure
for the verb-predicate assail with the sense ”to
tear attack” from www.cis.upenn.edu/
cotton/cgi-
bin/pblex fmt.cgi, we find Arg0:agent, Arg1:entity
assailed and Arg2:assailed for. Additionally, the ar-
gument may include functional tags from Treebank,
e.g. ArgM-DIR indicates a directional, ArgM-LOC
indicates a locative, and ArgM-TMP stands for a
temporal.
2.2 The Model
In previous work using the PropBank corpus,
(Gildea and Palmer, 2002) proposed a model pre-
dicting argument roles using the same statistical
method as the one employed by (Gildea and Juraf-
sky, 2002) for predicting semantic roles based on the
FrameNet corpus (Baker et al., 1998). This statis-
tical technique of labeling predicate argument oper-
ates on the output of the probabilistic parser reported
in (Collins, 1997). It consists of two tasks: (1) iden-
tifying the parse tree constituents corresponding to
arguments of each predicate encoded in PropBank;
and (2) recognizing the role corresponding to each
argument. Each task can be cast a separate classifier.
For example, the result of the first classifier on the
sentence illustrated in Figure 2 is the identification
of the two NPs as arguments. The second classifier
assigns the specific roles ARG1 and ARG0 given the
predicate “assailed”.
− POSITION (pos) − Indicates if the constituent appears
before or after the the predicate in the sentence.
− VOICE (voice) − This feature distinguishes between
active or passive voice for the predicate phrase.
are preserved.
of the evaluated phrase. Case and morphological information
− HEAD WORD (hw) − This feature contains the head word
− PARSE TREE PATH (path): This feature contains the path
in the parse tree between the predicate phrase and the
argument phrase, expressed as a sequence of nonterminal
labels linked by direction symbols (up or down), e.g.
− PHRASE TYPE (pt): This feature indicates the syntactic
NP for ARG1 in Figure 2.
type of the phrase labeled as a predicate argument, e.g.
noun phrases only, and it indicates if the NP is dominated
by a sentence phrase (typical for subject arguments with
active−voice predicates), or by a verb phrase (typical
for object arguments).
− GOVERNING CATEGORY (gov) − This feature applies to
− PREDICATE WORD − In our implementation this feature
consists of two components: (1) VERB: the word itself with the
case and morphological information preserved; and
(2) LEMMA which represents the verb normalized to lower
case and infinitive form.
NP S VP VP for ARG1 in Figure 2.
Figure 3: Feature Set 1
Statistical methods in general are hindered by the
data sparsity problem. To achieve high accuracy
and resolve the data sparsity problem the method
reported in (Gildea and Palmer, 2002; Gildea and
Jurafsky, 2002) employed a backoff solution based
on a lattice that combines the model features. For
practical reasons, this solution restricts the size of
the feature sets. For example, the backoff lattice
in (Gildea and Palmer, 2002) consists of eight con-
nected nodes for a five-feature set. A larger set of
features will determine a very complex backoff lat-
tice. Consequently, no new intuitions may be tested
as no new features can be easily added to the model.
In our studies we found that inductive learning
through decision trees enabled us to easily test large
sets of features and study the impact of each feature
BOOLEAN NAMED ENTITY FLAGS − A feature set comprising:
PHRASAL VERB COLOCATIONS − Comprises two features:
− pvcSum: the frequency with which a verb is immediately followed by
− pvcMax: the frequency with which a verb is followed by its
any preposition or particle.
predominant preposition or particle.
− neOrganization: set to 1 if an organization is recognized in the phrase
− neLocation: set to 1 a location is recognized in the phrase
− nePerson: set to 1 if a person name is recognized in the phrase
− neMoney: set to 1 if a currency expression is recognized in the phrase
− nePercent: set to 1 if a percentage expression is recognized in the phrase
− neTime: set to 1 if a time of day expression is recognized in the phrase
− neDate: set to 1 if a date temporal expression is recognized in the phrase
word from the constituent, different from the head word.
− CONTENT WORD (cw) − Lexicalized feature that selects an informative
PART OF SPEECH OF HEAD WORD (hPos) − The part of speech tag of
the head word.
PART OF SPEECH OF CONTENT WORD (cPos) −The part of speech
tag of the content word.
NAMED ENTITY CLASS OF CONTENT WORD (cNE) − The class of
the named entity that includes the content word
Figure 4: Feature Set 2
in NP
last June
PP
to
VP
be VP
declared
VP
SBAR
S
that
VP
occurred
NP
yesterday(a) (b) (c)
Figure 5: Sample phrases with the content word dif-
ferent than the head word. The head words are indi-
cated by the dashed arrows. The content words are
indicated by the continuous arrows.
on the augmented parser that outputs predicate ar-
gument structures. For this reason we used the C5
inductive decision tree learning algorithm (Quinlan,
2002) to implement both the classifier that identifies
argument constituents and the classifier that labels
arguments with their roles.
Our model considers two sets of features: Feature
Set 1 (FS1): features used in the work reported in
(Gildea and Palmer, 2002) and (Gildea and Juraf-
sky, 2002) ; and Feature Set 2 (FS2): a novel set of
features introduced in this paper. FS1 is illustrated
in Figure 3 and FS2 is illustrated in Figure 4.
In developing FS2 we used the following obser-
vations:
Observation 1:
Because most of the predicate arguments are
prepositional attachments (PP) or relative clauses
(SBAR), often the head word (hw) feature from
FS1 is not in fact the most informative word in
H1: if phrase type is PP then
select the right−most child
Example: phrase = "in Texas", cw = "Texas"
ifH2: phrase type is SBAR then
select the left−most sentence (S*) clause
Example: phrase = "that occurred yesterday", cw = "occurred"
if thenH3: phrase type is VP
if there is a VP child then
else select the head word
select the left−most VP child
Example: phrase = "had placed", cw = "placed"
ifH4: phrase type is ADVP then
select the right−most child not IN or TO
Example: phrase = "more than", cw = "more"
ifH5: phrase type is ADJP then
select the right−most adjective, verb,
noun, or ADJP
Example: phrase = "61 years old", cw = "old"
H6: for for all other phrase types do
select the head word
Example: phrase = "red house", cw = "house"
Figure 6: Heuristics for the detection of content
words
the phrase. Figure 5 illustrates three examples of
this situation. In Figure 5(a), the head word of
the PP phrase is the preposition in, but June is at
least as informative as the head word. Similarly,
in Figure 5(b), the relative clause is featured only
by the relative pronoun that, whereas the verb oc-
curred should also be taken into account. Figure 5(c)
shows another example of an infinitive verb phrase,
in which the head word is to, whereas the verb de-
clared should also be considered. Based on these
observations, we introduced in FS2 the CONTENT
WORD (cw), which adds a new lexicalization from
the argument constituent for better content repre-
sentation. To select the content words we used the
heuristics illustrated in Figure 6.
Observation 2:
After implementing FS1, we noticed that the hw
feature was rarely used, and we believe that this hap-
pens because of data sparsity. The same was noticed
for the cw feature from FS2. Therefore we decided
to add two new features, namely the parts of speech
of the head word and the content word respectively.
These features are called hPos and cPos and are
illustrated in Figure 4. Both these features generate
an implicit yet simple backoff solution for the lexi-
calized features HEAD WORD (hw) and CONTENT
WORD (cw).
Observation 3:
Predicate arguments often contain names or other
expressions identified by named entity (NE) recog-
nizers, e.g. dates, prices. Thus we believe that
this form of semantic information should be intro-
duced in the learning model. In FS2 we added the
following features: (a) the named entity class of
the content word (cNE); and (b) a set of NE fea-
tures that can take only Boolean values grouped as
BOOLEAN NAMED ENTITY FEATURES and defined
in Figure 4. The cNE feature helps recognize the ar-
gument roles, e.g. ARGM-LOC and ARGM-TMP,
when location or temporal expressions are identi-
fied. The Boolean NE flags provide information
useful in processing complex nominals occurring in
argument constituents. For example, in Figure 2
ARG0 is featured not only by the word traders but
also by ORGANIZATION, the semantic class of the
name Big Board.
Observation 4:
Predicate argument structures are recognized accu-
rately when both predicates and arguments are cor-
rectly identified. Often, predicates are lexicalized by
phrasal verbs, e.g. put up, put off. To identify cor-
rectly the verb particle and capture it in the structure
of predicates instead of the argument structure, we
introduced two collocation features that measure the
frequency with which verbs and succeeding prepo-
sitions cooccurr in the corpus. The features are pvc-
Sum and pvcMax and are defined in Figure 4.
2.3 The Experiments
The results presented in this paper were obtained
by training on Proposition Bank (PropBank) release
2002/7/15 (Kingsbury et al., 2002). Syntactic infor-
mation was extracted from the gold-standard parses
in TreeBank Release 2. As named entity information
is not available in PropBank/TreeBank we tagged
the training corpus with NE information using an
open-domain NE recognizer, having 96% F-measure
on the MUC6
1
data. We reserved section 23 of Prop-
Bank/TreeBank for testing, and we trained on the
rest. Due to memory limitations on our hardware,
for the argument finding task we trained on the first
150 KB of TreeBank (about 11% of TreeBank), and
1
The Message Understanding Conferences (MUC) were IE
evaluation exercises in the 90s. Starting with MUC6 named
entity data was available.
for the role assignment task on the first 75 KB of
argument constituents (about 60% of PropBank an-
notations).
Table 1 shows the results obtained by our induc-
tive learning approach. The first column describes
the feature sets used in each of the 7 experiments
performed. The following three columns indicate
the precision (P), recall (R), and F-measure ( )
2
obtained for the task of identifying argument con-
stituents. The last column shows the accuracy (A)
for the role assignment task using known argument
constituents. The first row in Table 1 lists the re-
sults obtained when using only the FS1 features.
The next five lines list the individual contributions
of each of the newly added features when combined
with the FS1 features. The last line shows the re-
sults obtained when all features from FS1 and FS2
were used.
Table 1 shows that the new features increase the
argument identification F-measure by 3.61%, and
the role assignment accuracy with 4.29%. For the
argument identification task, the head and content
word features have a significant contribution for the
task precision, whereas NE features contribute sig-
nificantly to the task recall. For the role assignment
task the best features from the feature set FS2 are
the content word features (cw and cPos) and the
Boolean NE flags, which show that semantic infor-
mation, even if minimal, is important for role clas-
sification. Surprisingly, the phrasal verb collocation
features did not help for any of the tasks, but they
were useful for boosting the decision trees. Deci-
sion tree learning provided by C5 (Quinlan, 2002)
has built in support for boosting. We used it and
obtained improvements for both tasks. The best F-
measure obtained for argument constituent identifi-
cation was 88.98% in the fifth iteration (a 0.76% im-
provement). The best accuracy for role assignment
was 83.74% in the eight iteration (a 0.69% improve-
ment)
3
. We further analyzed the boosted trees and
noticed that phrasal verb collocation features were
mainly responsible for the improvements. This is
the rationale for including them in the FS2 set.
We also were interested in comparing the results
2
3
These results, listed also on the last line of Table 2, dif-
fer from those in Table 1 because they were produced after the
boosting took place.
Features Arg P Arg R Arg Role A
FS1 84.96 84.26 84.61 78.76
FS1 + hPos 92.24 84.50 88.20 79.04
FS1 + cw, cPos 92.19 84.67 88.27 80.80
FS1 + cNE 83.93 85.69 84.80 79.85
FS1 + NE flags 87.78 85.71 86.73 81.28
FS1 + pvcSum + 84.88 82.77 83.81 78.62
pvcMax
FS1 + FS2 91.62 85.06 88.22 83.05
Table 1: Inductive learning results for argument
identification and role assignment
Model Implementation Arg Role A
Statistical (Gildea and Palmer) - 82.8
This study 71.86 78.87
Decision Trees FS1 84.61 78.76
FS1 + FS2 88.98 83.74
Table 2: Comparison of statistical and decision tree
learning models
of the decision-tree-based method against the re-
sults obtained by the statistical approach reported
in (Gildea and Palmer, 2002). Table 2 summarizes
the results. (Gildea and Palmer, 2002) report the re-
sults listed on the first line of Table 2. Because no F-
scores were reported for the argument identification
task, we re-implemented the model and obtained the
results listed on the second line. It looks like we
had some implementation differences, and our re-
sults for the argument role classification task were
slightly worse. However, we used our results for the
statistical model for comparing with the inductive
learning model because we used the same feature ex-
traction code for both models. Lines 3 and 4 list the
results of the inductive learning model with boosting
enabled, when the features were only from FS1, and
from FS1 and FS2 respectively. When comparing
the results obtained for both models when using only
features from FS1, we find that almost the same re-
sults were obtained for role classification, but an en-
hancement of almost 13% was obtained when recog-
nizing argument constituents. When comparing the
statistical model with the inductive model that uses
all features, there is an enhancement of 17.12% for
argument identification and 4.87% for argument role
recognition.
Another significant advantage of our inductive
learning approach is that it scales better to un-
Document(s)
POS
Tagger
NPB
Identifier
Dependency
Parser
Named Entity Recognizer
Entity
Coreference
Document(s)
Named Entity
Recognizer
Phrasal
Parser (FSA)
Combiner (FSA)
Entity
Coreference
Event
Recognizer (FSA)
Event
Coreference
Event
Merging
Template(s)
Pred/Arg
Identification
Predicate Arguments
Mapping
into Template Slots
Event
Coreference
Event
Merging
Template(s)
Full Parser
(b)
(a)
Figure 7: IE architectures: (a) Architecture based on predicate/argument relations; (b) FSA-based IE system
known predicates. The statistical model introduced
in Gildea and Jurafsky (2002) uses predicate lex-
ical information at most levels in the probability
lattice, hence its scalability to unknown predicates
is limited. In contrast, the decision tree approach
uses predicate lexical information only for 5% of the
branching decisions recorded when testing the role
assignment task, and only for 0.01% of the branch-
ing decisions seen during the argument constituent
identification evaluation.
3 The IE Paradigm
Figure 7(a) illustrates an IE architecture that em-
ploys predicate argument structures. Documents are
processed in parallel to: (1) parse them syntactically,
and (2) recognize the NEs. The full parser first per-
forms part-of-speech (POS) tagging using transfor-
mation based learning (TBL) (Brill, 1995). Then
non-recursive, or basic, noun phrases (NPB) are
identified using the TBL method reported in (Ngai
and Florian, 2001). At last, the dependency parser
presented in (Collins, 1997) is used to generate the
full parse. This approach allows us to parse the sen-
tences with less than 40 words from TreeBank sec-
tion 23 with an F-measure slightly over 85% at an
average of 0.12 seconds/sentence on a 2GHz Pen-
tium IV computer.
The parse texts marked with NE tags are passed to
a module that identifies entity coreference in docu-
ments, resolving pronominal and nominal anaphors
and normalizing coreferring expressions. The parses
are also used by a module that recognizes predi-
cate argument structures with any of the methods
described in Section 2.
For each templette modeling a different do-
main a mapping between predicate arguments and
templette slots is produced. Figure 8 illus-
trates the mapping produced for two Event99 do-
INSTRUMENTARG1 and MARKET_CHANGE_VERB
ARG2 and (MONEY or PERCENT or NUMBER or QUANTITY) and
MARKET_CHANGE_VERB AMOUNT_CHANGE
MARKET_CHANGE_VERB CURRENT_VALUE
(PERSON and ARG0 and DIE_VERB) or
(PERSON and ARG1 and KILL_VERB) DECEASED
(ARG0 and KILL_VERB) or
(ARG1 and DIE_VERB) AGENT_OF_DEATH
(ARGM−TMP and ILNESS_NOUN) or
KILL_VERB or DIE_VERB MANNER_OF_DEATH
ARGM−TMP and DATE DATE
(ARGM−LOC or ARGM−TMP) and
LOCATION LOCATION
(a)
(b)
(ARG4 or ARGM_DIR) and NUMBER and
Figure 8: Mapping rules between predicate ar-
guments and templette slots for: (a) the “market
change” domain, and (b) the “death” domain
mains. The “market change” domain monitors
changes (AMOUNT CHANGE) and current values
(CURRENT VALUE) for financial instruments (IN-
STRUMENT). The “death” domain extracts the de-
scription of the person deceased (DECEASED), the
manner of death (MANNER OF DEATH), and, if ap-
plicable, the person to whom the death is attributed
(AGENT OF DEATH).
To produce the mappings we used training data
that consists of: (1) texts, and (2) their correspond-
ing filled templettes. Each templette has pointers
back to the source text similarly to the example pre-
sented in Figure 1. When the predicate argument
structures were identified, the mappings were col-
lected as illustrated in Figure 9. Figure 9(a) shows
an interesting aspect of the mappings. Although the
role classification of the last argument is incorrect (it
should have been identified as ARG4), it is mapped
into the CURRENT-VALUE slot. This shows how the
mappings resolve incorrect but consistent classifica-
tions. Figure 9(b) shows the flexibility of the system
to identify and classify constituents that are not close
to the predicate phrase (ARG0). This is a clear ad-
5 1/4
ARG2
34 1/2to
ARGM−DIR
flewThe space shuttle Challenger apart over Florida like a billion−dollar confetti killing six astronauts
NP VP
S
NP
PP
NP
fellNorwalk−based Micro Warehouse
ARG1
NP
ADVP PP PP S
VP
VPNP
S
ARG0 P ARG1
INSTRUMENT AMOUNT_CHANGE CURRENT_VALUE
AGENT_OF_DEATH MANNER_OF_DEATH DECEASED
Mappings
(a) (b)
Figure 9: Predicate argument mapping examples for: (a) the “market change” domain, and (b) the “death”
domain
vantage over the FSA-based system, which in fact
missed the AGENT-OF-DEATH in this sentence. Be-
cause several templettes might describe the same
event, event coreference is processed and, based on
the results, templettes are merged when necessary.
The IE architecture in Figure 7(a) may be com-
pared with the IE architecture with cascaded FSA
represented in Figure 7(b) and reported in (Sur-
deanu and Harabagiu, 2002). Both architectures
share the same NER, coreference and merging
modules. Specific to the FSA-based architec-
ture are the phrasal parser, which identifies simple
phrases such as basic noun or verb phrases (some
of them domain specific), the combiner, which
builds domain-dependent complex phrases, and the
event recognizer, which detects the domain-specific
Subject-Verb-Object (SVO) patterns. An example
of a pattern used by the FSA-based architecture
is:
DEATH-CAUSE KILL-VERB PERSON , where
DEATH-CAUSE may identify more than 20 lexemes,
e.g. wreck, catastrophe, malpractice, and more than
20 verbs are KILL-VERBS, e.g. murder, execute, be-
head, slay. Most importantly, each pattern must rec-
ognize up to 26 syntactic variations, e.g. determined
by the active or passive form of the verb, relative
subjects or objects etc. Predicate argument struc-
tures offer the great advantage that syntactic vari-
ations do not need to be accounted by IE systems
anymore.
Because entity and event coreference, as well as
templette merging will attempt to recover from par-
tial patterns or predicate argument recognitions, and
our goal is to compare the usage of FSA patterns
versus predicate argument structures, we decided to
disable the coreference and merging modules. This
explains why in Figure 7 these modules are repre-
System Market Change Death
Pred/Args Statistical 68.9% 58.4%
Pred/Args Inductive 82.8% 67.0%
FSA 91.3% 72.7%
Table 3: Templette F-measure ( ) scores for the
two domains investigated
System Correct Missed Incorrect
Pred/Args Statistical 26 16 3
Pred/Args Inductive 33 9 2
FSA 38 4 2
Table 4: Number of event structures (FSA patterns
or predicate argument structures) matched
sented with dashed lines.
4 Experiments with The Integration of
Predicate Argument Structures in IE
To evaluate the proposed IE paradigm we selected
two Event99 domains: “market change”, which
tracks changes in stock indexes, and “death”, which
extracts all manners of human deaths. These do-
mains were selected because most of the domain in-
formation can be processed without needing entity
or event coreference. Moreover, one of the domains
(market change) uses verbs commonly used in Prop-
Bank/TreeBank, while the other (death) uses rela-
tively unknown verbs, so we can also evaluate how
well the system scales to verbs unseen in training.
Table 3 lists the F-scores for the two domains.
The first line of the Table lists the results obtained
by the IE architecture illustrated in Figure 7(a) when
the predicate argument structures were identified by
the statistical model. The next line shows the same
results for the inductive learning model. The last
line shows the results for the IE architecture in Fig-
ure 7(b). The results obtained by the FSA-based IE
were the best, but they were made possible by hand-
crafted patterns requiring an effort of 10 person days
per domain. The only human effort necessary in
the new IE paradigm was imposed by the genera-
tion of mappings between arguments and templette
slots, accomplished in less than 2 hours per domain,
given that the training templettes are known. Addi-
tionally, it is easier to automatically learn these map-
pings than to acquire FSA patterns.
Table 3 also shows that the new IE paradigm per-
forms better when the predicate argument structures
are recognized with the inductive learning model.
The cause is the substantial difference in quality
of the argument identification task between the two
models. The Table shows that the new IE paradigm
with the inductive learning model achieves about
90% of the performance of the FSA-based system
for both domains, even though one of the domains
uses mainly verbs rarely seen in training (e.g. “die”
appears 5 times in PropBank).
Another way of evaluating the integration of pred-
icate argument structures in IE is by comparing the
number of events identified by each architecture. Ta-
ble 4 shows the results. Once again, the new IE
paradigm performs better when the predicate argu-
ment structures are recognized with the inductive
learning model. More events are missed by the sta-
tistical model which does not recognize argument
constituents as well the inductive learning model.
5 Conclusion
This paper reports on a novel inductive learning
method for identifying predicate argument struc-
tures in text. The proposed approach achieves over
88% F-measure for the problem of identifying argu-
ment constituents, and over 83% accuracy for the
task of assigning roles to pre-identified argument
constituents. Because predicate lexical information
is used for less than 5% of the branching decisions,
the generated classifier scales better than the statisti-
cal method from (Gildea and Palmer, 2002) to un-
known predicates. This way of identifying pred-
icate argument structures is a central piece of an
IE paradigm easily customizable to new domains.
The performance degradation of this paradigm when
compared to IE systems based on hand-crafted pat-
terns is only 10%.
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