Exploiting Strong Syntactic Heuristics and Co-Training to Learn Semantic
Lexicons
William Phillips and Ellen Riloff
School of Computing
University of Utah
Salt Lake City, UT 84112 USA
fphillips,riloffg@cs.utah.edu
Abstract
We present a bootstrapping method that
uses strong syntactic heuristics to learn
semantic lexicons. The three sources
of information are appositives, compound
nouns, and ISA clauses. We apply heuris-
tics to these syntactic structures, embed
them in a bootstrapping architecture, and
combine them with co-training. Results
on WSJ articles and a pharmaceutical cor-
pus show that this method obtains high
precision and finds a large number of
terms.
1 Introduction
Syntactic structure helps us understand the seman-
tic relationships between words. Given a text cor-
pus, we can use knowledge about syntactic struc-
tures to obtain semantic knowledge. For example,
Hearst (Hearst, 1992) learned hyponymy relation-
ships by collecting words in lexico-syntactic expres-
sions, such as “NP, NP, and other NPs”, and Roark
and Charniak (Roark and Charniak, 1998) gener-
ated semantically related words by applying statisti-
cal measures to syntactic contexts involving apposi-
tives, lists, and conjunctions.
Exploiting syntactic structures to learn semantic
knowledge holds great promise, but can run into
problems. First, lexico-syntactic expressions that
explicitly indicate semantic relationships (e.g., “NP,
NP, and other NPs”) are reliable but a lot of semantic
information occurs outside these expressions. Sec-
ond, general syntactic structures (e.g., lists and con-
junctions) capture a wide range of semantic rela-
tionships. For example, conjunctions frequently
join items of the same semantic class (e.g., “cats
and dogs”), but they can also join different seman-
tic classes (e.g., “fire and ice”). Some researchers
(Roark and Charniak, 1998; Riloff and Shepherd,
1997) have applied statistical methods to identify
the strongest semantic associations. This approach
has produced reasonable results, but the accuracy of
these techniques still leaves much room for improve-
ment.
We adopt an intermediate approach that learns
semantic lexicons using strong syntactic heuristics,
which are both common and reliable. We have
identified certain types of appositives, compound
nouns, and identity (ISA) clauses that indicate spe-
cific semantic associations between words. We em-
bed syntactic heuristics in a bootstrapping process
and present empirical results demonstrating that this
bootstrapping process produces high-quality seman-
tic lexicons. In another set of experiments, we in-
corporate a co-training (Blum and Mitchell, 1998)
mechanism to combine the hypotheses generated by
different types of syntactic structures. Co-training
produces a synergistic effect across different heuris-
tics, substantially increasing the coverage of the lex-
icons while maintaining nearly the same level of ac-
curacy.
2 Semantic Lexicon Learning
The goal of our research is to automatically gener-
ate a semantic lexicon. For our purposes, we de-
fine a semantic lexicon to be a list of words with
semantic category labels. For example, the word
“bird” might be labeled as an ANIMAL and the word
“car” might be labeled as a VEHICLE. Semantic
lexicons have proven to be useful for many lan-
                                            Association for Computational Linguistics.
                    Language Processing (EMNLP), Philadelphia, July 2002, pp. 125-132.
                         Proceedings of the Conference on Empirical Methods in Natural
guage processing tasks, including anaphora resolu-
tion (Aone and Bennett, 1996; McCarthy and Lehn-
ert, 1995), prepositional phrase attachment (Brill
and Resnik, 1994), information extraction (Soder-
land et al., 1995; Riloff and Schmelzenbach, 1998),
and question answering (Harabagiu et al., 2000;
Hirschman et al., 1999).
Some general-purposes semantic dictionaries al-
ready exist, such as WordNet (Miller, 1990). Word-
Net has been used for many applications, but it may
not contain the vocabulary and jargon needed for
specialized domains. For example, WordNet does
not contain much of the vocabulary found in medical
texts. In previous research on semantic lexicon in-
duction, Roark and Charniak (Roark and Charniak,
1998) showed that 3 of every 5 words learned by
their system were not present in WordNet. Further-
more, they used relatively unspecialized text cor-
pora: Wall Street Journal articles and terrorism news
stories. Our goal is to develop techniques for seman-
tic lexicon induction that could be used to enhance
existing resources such as WordNet, or to create dic-
tionaries for specialized domains.
Several techniques have been developed to gen-
erate semantic knowledge using weakly supervised
learning techniques. Hearst (Hearst, 1992) ex-
tracted information from lexico-syntactic expres-
sions that explicitly indicate hyponymic relation-
ships. Hearst’s work is similar in spirit to our
work in that her system identified reliable syntac-
tic structures that explicitly reveal semantic associa-
tions. Meta-bootstrapping (Riloff and Jones, 1999)
is a semantic lexicon learning technique very differ-
ent from ours which utilizes information extraction
patterns to identify semantically related contexts.
Named entity recognizers (e.g., (Bikel et al., 1997;
Collins and Singer, 1999; Cucerzan and Yarowsky,
1999)) can be trained to recognize proper names
associated with semantic categories such as PER-
SON or ORGANIZATION, but they typically are not
aimed at learning common nouns such as “surgeon”
or “drugmaker”.
Several researchers have used some of the same
syntactic structures that we exploit in our research,
namely appositives and compound nouns. For ex-
ample, Riloff and Shepherd (Riloff and Shepherd,
1997) developed a statistical co-occurrence model
for semantic lexicon induction that was designed
with these structures in mind. Roark and Char-
niak (Roark and Charniak, 1998) followed up on
this work by using a parser to explicitly capture
these structures. Caraballo (Caraballo, 1999) also
exploited these syntactic structures and applied a co-
sine vector model to produce semantic groupings. In
our view, these previous systems used “weak” syn-
tactic models because the syntactic structures some-
times identified desirable semantic associations and
sometimes did not. To compensate, statistical mod-
els were used to separate the meaningful semantic
associations from the spurious ones. In contrast, our
work aims to identify “strong” syntactic heuristics
that can isolate instances of general structures that
reliably identify the desired semantic relations.
3 A Bootstrapping Model that Exploits
Strong Syntactic Heuristics
For the purposes of this research, we will define two
distinct types of lexicons. One lexicon will con-
sist of proper noun phrases, such as “Federal Avi-
ation Administration”. We will call this the PNP
(proper noun phrase) lexicon. The second lexicon
will consist of common (non-proper) nouns, such as
“airplane”. We will call this the GN (general noun)
lexicon. The reason for creating these distinct lexi-
cons is that our algorithm takes advantage of syntac-
tic relationships between proper nouns and general
nouns.
3.1 Syntactic Heuristics
Our goal is to build a semantic lexicon of words that
belong to the same semantic class. More specifi-
cally, we aim to find words that have the same hyper-
nym, for example “dog” and “frog” would both have
the hypernym ANIMAL.
1
We will refer to words that
have the same immediate hypernym as semantic sib-
lings.
We hypothesize that some syntactic structures can
be used to reliably identify semantic siblings. We
have identified three candidates: appositives, com-
pound nouns, and identity clauses whose main verb
is a form of “to be” (we will call these ISA clauses).
1
The appropriate granularity of a set of semantic classes,
or the organization of a semantic hierarchy, is always open to
debate. We chose categories that seem to represent important
and relatively general semantic distinctions.
While these structures often do capture semantic sib-
lings, they frequently capture other types of seman-
tic relationships as well. Therefore we use heuristics
to isolate subsets of these syntactic structures that
consistently contain semantic siblings. Our heuris-
tics are based on the observation that many of these
structures contain both a proper noun phrase and a
general noun phrase which are co-referent and usu-
ally belong to the same semantic class. In the fol-
lowing sections, we explain the heuristics that we
use for each syntactic structure, and how those struc-
tures are used to learn new lexicon entries.
3.1.1 Appositives
Appositives are commonly occurring syntactic
structures that contain pairs of semantically related
noun phrases. A simple appositive structure con-
sists of a noun phrase (NP), followed by a comma,
followed by another NP, where the two NPs are co-
referent. However, appositives often signify hyper-
nym relationships (e.g., “the dog, a carnivorous ani-
mal”).
To identify semantic siblings, we only use appos-
itives that contain one proper noun phrase and one
general noun phrase. For example, “George Bush,
the president” or “the president, George Bush”. The-
oretically, such appositives could also indicate a hy-
pernym relationship (e.g., “George Bush, a mam-
mal”), but we have found that this rarely happens
in practice.
3.1.2 Compound Nouns
Compound nouns are extremely common but they
can represent a staggering variety of semantic rela-
tionships. We have found one type of compound
noun that can be reliably used to harvest seman-
tic siblings. We loosely define these compounds
as “GN
+
PNP” noun phrases, where the compound
noun ends with a proper name but is modified with
one or more general nouns. Examples of such com-
pounds are “violinist James Braum” or “software
maker Microsoft”. One of the difficulties with rec-
ognizing these constructs, however, is resolving the
ambiguity between adjectives and nouns among the
modifiers (e.g., “violinist” is a noun). We only use
constructs in which the GN modifier is unambigu-
ously a noun.
3.1.3 ISA Clauses
Certain “to be” clauses can also be harvested to
extract semantic siblings. We define an ISA clause
as an NP followed by a VP that is a form of “to be”,
followed by another NP. These identity clauses also
exhibit a wide range of semantic relationships, but
harvesting clauses which contain one proper NP and
one general NP can reliably identify noun phrases
of the same semantic class. We found that this struc-
ture yields semantic siblings when the subject NP is
constrained to be a proper NP and the object NP is
constrained to be a general NP (e.g., “Jing Lee is the
president of the company”).
3.2 The Bootstrapping Model
Figure 1 illustrates the bootstrapping model for each
of the three syntactic structures. Initially, the lex-
icons contain only a few manually defined seed
words: some proper noun phrases and some general
nouns. The syntactic heuristics are then applied to
the text corpus to collect potentially “harvestable”
structures. Each heuristic identifies structures with
one proper NP and one general NP, where one of
them is already present in the lexicon as a member
of a desired semantic class. The other NP is then as-
sumed to belong to the same semantic class and is
added to a prospective word list. Finally, statistical
filtering is used to divide the prospective word lists
into exclusive and non-exclusive subsets. We will
describe the motivation for this in Section 3.2.2. The
exclusive words are added to the lexicon, and the
bootstrapping process repeats. In the remainder of
this section, we explain how the bootstrapping pro-
cess works in more detail.
3.2.1 Bootstrapping Procedure
The input to our system is a small set of seed
words for the semantic categories of interest. To
identify good seed words, we sorted all nouns in
the corpus by frequency and manually identified the
most frequent nouns that belong to each targeted se-
mantic category.
Each bootstrapping iteration alternates between
using either the PNP lexicon or the GN lexicon to
grow the lexicons. As a motivating example, as-
sume that (1) appositives are the targeted syntactic
structure (2) bootstrapping begins by using the PNP
lexicon, and (3) PEOPLE is the semantic category of
Proper NP
Lexicon
Syntactic Heuristics
Text Corpus
Prospective
Proper NPs
Seed Words
Exclusive Non−Exclusive Non−Exclusive Exclusive
Lexicon
General Noun
Prospective
General Nouns
Figure 1: Bootstrapping Model
interest. The system will then collect all appositives
that contain a proper noun phrase known to be a per-
son. So if “Mary Smith” belongs to the PNP lexicon
and the appositive “Mary Smith, the analyst” is en-
countered, the head noun “analyst” will be learned
as a person.
The next bootstrapping iteration uses the GN lex-
icon, so the system will collect all appositives that
contain a general noun phrase known to be a person.
If the appositive “John Seng, the financial analyst”
is encountered, then “John Seng” will be learned as
a person because the word “analyst” is known to
be a person from the previous iteration. The boot-
strapping process will continue, alternately using the
PNP lexicon and the GN lexicon, until no new words
can be learned.
We treat proper noun phrases and general noun
phrases differently during learning. When a proper
noun phrase is learned, the full noun phrase is added
to the lexicon. But when a general noun phrase is
learned, only the head noun is added to the lexi-
con. This approach gives us generality because head
nouns are usually (though not always) sufficient to
associate a common noun phrase with a semantic
class. Proper names, however, often do not exhibit
this generality (e.g., “Saint Louis” is a location but
“Louis” is not).
However, using full proper noun phrases can limit
the ability of the bootstrapping process to acquire
new terms because exact matches are relatively rare.
To compensate, head nouns and modifying nouns of
proper NPs are used as predictor terms to recognize
new proper NPs that belong to the same semantic
class. We identify reliable predictor terms using the
evidence and exclusivity measures that we will de-
fine in the next section. For example, the word “Mr.”
is learned as a good predictor term for the person cat-
egory. These predictor terms are only used to clas-
sify noun phrases during bootstrapping and are not
themselves added to the lexicon.
3.2.2 Exclusivity Filtering
Our syntactic heuristics were designed to reliably
identify words belonging to the same semantic class,
but some erroneous terms still slip through for vari-
ous reasons, such as parser errors and idiomatic ex-
pressions. Perhaps the biggest problem comes from
ambiguous terms that can belong to several seman-
tic classes. For instance, in the financial domain
“leader” can refer to both people and corporations.
If “leader” is added to the person lexicon, then it
will pull corporation terms into the lexicon during
subsequent bootstrapping iterations and the person
lexicon will be compromised.
To address this problem, we classify all candidate
wordsasbeingexclusive to the semantic category or
non-exclusive. For example, the word “president”
nearly always refers to a person so it is exclusive to
the person category, but the word “leader” is non-
exclusive. Only the exclusive terms are added to
the semantic lexicon during bootstrapping to keep
the lexicon as pure (unambiguous) as possible. The
non-exclusive terms can be added to the final lexicon
when bootstrapping is finished if polysemous terms
are acceptable to have in the dictionary.
Exclusivity filtering is the only step that uses
statistics. Two measures determine whether a word
is exclusive to a semantic category. First, we use an
evidence measure:
Evidence(w;c)=
S
w;c
S
w
where S
w
is the number of times word w was found
in the syntactic structure, and S
w;c
is the number of
times word w was found in the syntactic structure
collocated with a member of category c.Theevi-
dence measure is the maximum likelihood estimate
that a word belongs to a semantic category given that
it appears in the targeted syntactic structure (a word
is assumed to belong to the category if it is collo-
cated with another category member). Since few
words are known category members initially, we use
a low threshold value (.25) which simply ensures
that a non-trivial proportion of instances are collo-
cated with category members.
The second measure that we use, exclusivity,is
the number of occurrences found in the given cate-
gory’s prospective list divided by the number of oc-
currences found in all other categories’ prospective
lists.
Exclusivity(w;c)=
S
w;c
S
w;:c
where S
w;c
is the number of times word w was found
in the syntactic structure collocated with a member
of category c,andS
w;:c
is the number of times word
w was found in the syntactic structure collocated
with a member of a different semantic class. We
apply a threshold to this ratio to ensure that the term
is exclusive to the targeted semantic category.
3.3 Experimental Results
We evaluated our system on several semantic cate-
gories in two domains. In one set of experiments,
we generated lexicons for PEOPLE and ORGANIZA-
TIONS using 2500 Wall Street Journal articles from
the Penn Treebank (Marcus et al., 1993). In the sec-
ond set of experiments, we generated lexicons for
PEOPLE, ORGANIZATIONS,andPRODUCTS using
approximately 1350 press releases from pharmaceu-
tical companies.
2
Our seeding consisted of 5 proper nouns and 5
general nouns for each semantic category. We used a
threshold of 25% for the evidence measure and 5 for
the exclusivity ratio. We ran the bootstrapping pro-
cess until no new words were learned, which ranged
from 6-14 iterations depending on the category and
syntactic structure.
Table 1 shows 10 examples of words learned for
each semantic category in each domain. The people
and organization lists illustrate (1) how dramatically
the vocabulary can differ across domains, and (2)
that the lexicons may include domain-specific word
meanings that are not the most common meaning
2
We found these texts using Yahoo’s financial industry pages
at http://biz.yahoo.com/news/medical.html.
People (WSJ): adman, co-chairman, head,
economist, shareholder, AMR Chairman Robert
Crandall, Assistant Secretary David Mullins,
Deng Xiaoping, Abby Joseph Cohen, C. Everett
Koop
Organization (WSJ): parent, subsidiary, dis-
tiller, arm, suitor, AMR Corp., ABB ASEA
Brown Boveri, J.P. Morgan, James River, Federal
Reserve Board
People (Pharm): surgeon, executive, recipient,
co-author, pioneer, Amgen Chief Executive Of-
ficer, Barbara Ryan, Chief Scientific Officer Nor-
bert Riedel, Dr. Cole, Analyst Mark Augustine
Organization (Pharm): device-maker, drug-
maker, licensee, organization, venture, ALR
Technologies, Aventis Pharmaceuticals, Bayer
AG, FDA Advisory Panel, Hadassah University
Hospital
Product (Pharm): compound, stent, platform,
blocker, antibiotic, Bexxar, Viratrol, MBX-102,
Apothesys Decision Support System, AERx Pain
Management System
Table 1: Examples of Learned Words
of a word in general. For example, the word “par-
ent” generally refers to a person, but in a financial
domain it nearly always refers to an organization.
The pharmaceutical product category contains many
nouns (e.g., drug names) that may not be in a general
purpose lexicon such as WordNet.
Tables 2 and 3 show the results of our evaluation.
We ran the bootstrapping algorithm on each type of
syntactic structure independently. The Total column
shows the total number of lexicon entries generated
by each syntactic structure. The Correct column
contains two accuracy numbers: X/Y. The first value
(X) is the percentage of entries that were judged to
be correct, and the second value (Y) is the accuracy
after removing entries resulting from parser errors.
3
The PNP lexicons were substantially larger than
the GN lexicons, in part because we saved full noun
3
For example, our parser frequently mistags adjectives as
nouns, so many adjectives were hypothesized to be people. If
the parser had tagged them correctly, they would not have been
allowed in the lexicon.
Appositives Compounds ISA Union
Category Total Correct Total Correct Total Correct Total Correct
People (WSJ) 1826 .97/.97 2026 .99/.99 113 .94/.94 3543 1.0/1.0
Orgs (WSJ) 674 .87/.94 3770 .77/.78 54 .93/.96 4191 .79/.79
People (Pharm) 280 .86/.87 1723 .87/.88 39 1.0/1.0 1872 .85/.91
Orgs (Pharm) 205 .85/.88 1128 .85/.92 248 .85/.91 1399 .78/.84
Products (Pharm) 64 .94/.95 223 .77/.79 64 .84/.84 330 .83/.85
Table 2: Proper Noun Phrase Lexicon Results
Appositives Compounds ISA Union
Category Total Correct Total Correct Total Correct Total Correct
People (WSJ) 159 .91/.95 60 .30/.56 41 .85/.97 229 .73/.88
Orgs (WSJ) 84 .69/.75 54 .26/.47 6 1.0/1.0 134 .52/.66
People (Pharm) 34 .91/.91 32 .66/.75 18 1.0/1.0 66 .79/.84
Orgs (Pharm) 36 .58/.60 29 .35/.46 41 .51/.66 95 .45/.54
Products (Pharm) 8 .75/1.0 11 .09/.33 13 .54/1.0 32 .50/.89
Table 3: General Noun Lexicon Results
phrases in the PNP lexicon but only head nouns in
the GN lexicon. Probably the main reason, however,
is that there are many more proper names associated
with most semantic categories than there are general
nouns. Consequently, we evaluated the PNP and GN
lexicons differently. For the GN lexicons, a volun-
teer (not one of the authors) labeled every word as
correct or incorrect. Due to the large size of the PNP
lexicons, we randomly sampled 100 words for each
syntactic structure and semantic category and asked
volunteers to label these samples. Consequently, the
PNP evaluation numbers are estimates of the true ac-
curacy.
The Union column tabulates the results obtained
from unioning the lexicons produced by the three
syntactic structures independently.
4
Although there
is some overlap in their lexicons, we found that
many different words are being learned. This indi-
cates that the three syntactic structures are tapping
into different parts of the search space, which sug-
gests that combining them in a co-training model
could be beneficial.
4
Since the number of words contributed by each syntac-
tic structure varied greatly, we evaluated the Union results for
the PNP lexicon by randomly sampling 100 words from the
unioned lexicons regardless of which structure generated them.
This maintained the same distribution in our evaluation set as
exists in the lexicon as a whole. However, this sampling strat-
egy means that the evaluation results in the Union column are
not simply the sum of the results in the preceding columns.
Seed Words
Syntactic Structures
Lexicons for All 3
Appositive
Bootstrapping
Process
Compound Noun
Bootstrapping 
Process
ISA Clause
Bootstrapping
Process
Figure 2: Co-Training Model
3.4 Co-Training
Co-training (Blum and Mitchell, 1998) is a learn-
ing technique which combines classifiers that sup-
port different views of the data in a single learning
mechanism. The co-training model allows examples
learned by one classifier to be used by the other clas-
sifiers, producing a synergistic effect. The three syn-
tactic structures that we have discussed provide three
different ways to harvest semantically related noun
phrases.
Figure 2 shows our co-training model, with each
syntactic structure serving as an independent classi-
fier. The words hypothesized by each classifier are
put into a single PNP lexicon and a single GN lex-
icon, which are shared by all three classifiers. We
used an aggressive form of co-training, where all
terms hypothesized by a syntactic structure with fre-
quency   are added to the shared lexicon. The
threshold ensures some confidence in a term before
it is allowed to be used by the other learners. We
used a threshold of  =3 for the WSJ corpus and
 =2 for the pharmaceutical corpus since it is sub-
stantially smaller. We ran the bootstrapping process
until no new words were learned, which was 12 it-
erations for the WSJ corpus and 10 iterations for the
pharmaceutical corpus.
5
PNP PNP GN GN
Category cotrn w/o cotrn w/o
People (WSJ) 5414 3543 347 229
Orgs (WSJ) 4227 4191 213 134
People (Pharm) 2217 1872 84 66
Orgs (Pharm) 4068 1399 196 95
Products (Pharm) 309 330 38 32
Table 4: Lexicon sizes with and w/o co-training
Table 4 shows the size of the learned lexicons with
co-training and without co-training (i.e., running the
classifiers separately). In almost all cases, many ad-
ditional words were learned using the co-training
model. Tables 5 and 6 show the evaluation results
for the lexicons produced by co-training. The co-
training model produced substantially better cover-
age, while achieving nearly the same accuracy. One
exception was organizations in the pharmaceutical
domain, which suffered a sizeable loss in precision.
This is most likely due to the co-training loop be-
ing too aggressive. If one classifier produces a lot
of mistakes (in this case, the compound noun classi-
fier), then those mistakes can drag down the overall
accuracy of the lexicon.
4 Conclusions
We have presented a method for learning seman-
tic lexicons that uses strong syntactic heuristics in
a bootstrapping algorithm. We exploited three types
of syntactic structures (appositives, compound NPs,
5
After co-training finished, we also added terms to the lexi-
con that were hypothesized by an individual classifier with fre-
quency < if they had not previously been labeled.
and ISA clauses) in combination with heuristics to
identify instances of these structures that contain
both a proper and general noun phrase. Each syntac-
tic structure generated many lexicon entries, in most
cases with high accuracy. We also combined the
three classifiers using co-training. The co-training
model increased the number of learned lexicon en-
tries, while maintaining nearly the same level of ac-
curacy. One limitation of this work is that it can only
learn semantic categories that are commonly found
as proper nouns and general nouns.
This research illustrates that common syntactic
structures can be combined with heuristics to iden-
tify specific semantic relationships. So far we have
experimented with three structures and one type of
heuristic (proper NP/general NP collocations), but
we believe that this approach holds promise for other
semantic learning tasks as well. In future work, we
hope to investigate other types of syntactic struc-
tures that may be used to identify semantically re-
lated terms, and other types of heuristics that can
reveal specific semantic relationships.
5 Acknowledgements
This research was supported by the National Science
Foundation under award IRI-9704240. Thanks to
Erin Davies, Brijesh Garabadu, Dominic Jones, and
Henry Longmore for labeling data.

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