Applying a Semantic Interpreter to a Knowledge

Extraction Task

Fernando Gomez

, Carlos Segami

Dept. of Computer Science, School of EECS

University of Central Florida, Orlando, Fl 32816

Dept. of Mathematics and Computer Science

Barry University, Miami Shores, FL 33161

Abstract. A sy

stem that extracts knowledge from encyclopedic texts is

presented. The knowledge extraction component is based on a semantic

interpreter of English based on an enhanced WordNet. The input to the

knowledge extraction component is the output of the semantic interpreter. The

extraction task was chosen in order to test the semantic interpreter. The

following aspects are described: the definition of verb predicates and semantic

roles, the organization of the inferences, an evaluation of the system, and a

session with the system.

1 Introduction

There could be little doubt that a knowledge extraction component (KE) should be

based on the output of a semantic interpreter. The more general the semantic

interpreter the easier it should be to build different knowledge extraction tasks for

different domains. This paper describes a KE that is fully based on the output of a

semantic interpreter. It is also shown that the inferences of the KE are organized on

the verb predicates used by the semantic interpreter to assign meaning to the

grammatical relations of the sentence. Moreover, the KE uses the same ontology as

that of the semantic interpreter. Because the KE component is grounded on the

semantic interpretation algorithm and shares the same ontology, the construction of

different KE extraction tasks reduces to building some inferences in the predicates

used by the semantic interpreter. Incompatibilities between ontologies used by diverse

components of the system do not exist. Furthermore, the KE designer does not have to

be concerned with defining ontological categories, because these have been built for

him/her in the semantic interpreter. This paper is organized as follows. Section 2)

explains the semantic interpreter briefly; Section 3) describes the knowledge

extraction task from the The World Book Encyclopedia (World Book, Inc.,

Chicago.1987). Sections 4 and 5 explain the organization of the inferences in the KE.

And sections 6, 7, 8 and 9 provide the testing, a sample session, related work, and

conclusions, respectively.

Gomez F. and Segami C. (2005).

Applying a Semantic Interpreter to a Knowledge Extraction Task.

In Proceedings of the 2nd International Workshop on Natural Language Understanding and Cognitive Science, pages 100-109

DOI: 10.5220/0002569201000109

 SciTePress

2 The Semantic Interpreter

We have defined verb predicates for WordNet verb classes [2], which have undergone

considerable reorganization and redefinition following the criteria imposed by the

interpretation algorithm. The WordNet upper-level ontology for nouns [11] has also

undergone reorganization and redefinition [5] based on the feedback that we have

obtained from the semantic interpreter.

The selectional restrictions in the predicates are linked to the WordNet ontology

for nouns. The predicates form a hierarchy in which semantic roles and inferences are

inherited by subpredicates from their superpredicates. For instance, the predicate

graduate-from has the following hierarchy:

GRADUATE-FROM Æ RECEIVE-AN-ACADEMIC-DEGREE Æ GET-AWARD Æ GET Æ

TRANSFER-OF-POSSESSION Æ ACTION

where the arrow represents the is-a relation.

The syntax for the semantic roles in the predicates is:

(role (<slr>)(<grs>) (<slr>)(<grs>) ... (<slr>)(<grs>))

Where <slr> stands for any number of selectional restrictions, and <grs> for any

number of grammatical relations. The grammatical relations for PPs are represented

by writing “prep” followed by the prepositions that realize the semantic role, e.g.

(prep about on ...). The list of selectional restrictions is a preference list [15]. Entry

slri is preferred over entry slri+1. Thus, if entry slri subsumes the ontological

category of the head noun of the grammatical relation in the sentence, the entry slri+1

is not tried [3]. However, the list of grammatical relations is an unordered list. The

entry for the predicate graduate-from is:

[GRADUATE-FROM (IS-A (RECEIVE-AN-ACADEMIC-DEGREE)) (WN-MAP (GRADUATE1))

(AGENT MAN) (SUBJ)) (THEME (ACADEMIC-DEGREE) ((PREP WITH))) (HU

(FROM-POSS (EDUCATIONAL-INSTITUTION ORGANIZATION)((PREP FROM)))]

The entry wn-map means that all the synsets of graduate1 and all the verbs that fall

under the class of graduate1 are mapped into the predicate graduate-from. The entry

for the theme is intended to interpret sentences such as “X graduated with a degree in

Physics from Y,” in which the theme is realized by [with NP] if the head noun of the

NP is an academic-degree. The PP [from NP] matches the from-poss if the head noun

of the NP is an educational-institution. This is the category preferred. However, if the

head noun of the NP is not an educational-institution, but it is an organization, the

from-poss role will also match. The default category, organization, is needed because

some educational institutions are not part of the WN noun ontology.

The semantic interpretation algorithm reported in [3] is activated by the parser after

parsing a clause. The parser does not resolve structural ambiguity, which is delayed

until semantic interpretation. The goals of the algorithm are to select one predicate

from the list of predicates for a verb form, attach PPs and identify semantic roles and

adjuncts. For each grammatical relation (GR) in the clause and for every predicate in

the list of predicates, the algorithm verifies if the predicate explains the GR. A

predicate explains an GR if there is a semantic role in the predicate realized by the

GR and the selectional restrictions of the semantic role subsume the ontological

category of the head noun of the grammatical relation. This process is repeated for

each GR in the clause and each predicate in the list of predicates. Then, the predicate

that explains the most GRs is selected as the meaning of the verb. The semantic roles

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of the predicate have been identified as a result of this process. In case of ties, the

predicate that has the greatest number of semantic roles realized is preferred. Every

grammatical relation that has not been mapped into a semantic role must be an

adjunct or an NP modifier. The entries for adjuncts are stored in the root node action

and are inherited by all predicates. Adjuncts are recognized after the meaning of the

verb has been determined because they are not part of the argument structure of the

predicate.

3 Description of the Task

Assume that one wants to extract knowledge from the Encyclopedia about the schools

attended by people as students. The system should build a template for each school

attended by the person as a student. Each template built by the system must contain

the following information if known: name of the school, school type, date of entrance,

date of graduation, location of the school, subject/degree of study, age of the student

at entrance, and age of student at graduation. When an extraction task begins, the

fillers of the template are all initialized to nil. Hence the entire template is constructed

from scratch by the semantic interpreter. The main relation to recognize is that of

attend school as a student, which can be expressed in many ways. For instance, the

text may say that person X entered school Y, that X was transferred to Y, that X

graduated from Y, that X was educated at Y, that X received/got/obtained a degree

from Y, that X studied at Y, that X was/became a student at Y, that X was an alumnus

of Y, that X's parents sent X to Y, that X withdrew from Y, that Y accepted/admitted

X, etc. Besides recognizing that all these verbs may imply attend-a-school, the

algorithm must identify all semantic roles of the sentence and map them into the

entries in the template. For instance, if the sentence says that “X graduated from Y in

1943,” the algorithm must recognize that “from Y” is the school attended by X and

that “in 1943” is a temporal adjunct expressing the date in which X graduated. But,

this mapping should not be from syntactic relations for the verb “graduate” to entries

in the template, but from semantic roles for the predicate graduate-from to semantic

roles for the predicate attend-a-school. There are several reasons for it, the most

important being that other verbs besides “graduate” may express the relation

“graduate-from,” such as “X received/obtained/got a degree from Y.” Another related

reason is that the same semantic role may be expressed by different grammatical

relations. Because the template is constructed from the semantic roles and from

temporal and locative adjuncts, correct identification of semantic roles and adjuncts

becomes critical for the precision and recall of the overall system.

4 Using the Hierarchical Organization of Predicates to Establish

the Inferences

The hierarchical organization of predicates provided by the semantic interpreter does

permit already to establish the inference attend-a-school for many verbs, because

these are mapped into subpredicates of attend-a-school. For instance, the verb “enter”

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followed by a post-verbal NP whose head noun is an educational-institution is

recognized by the interpreter as the predicate enter-a-school whose superconcepts are

given by:

ENTER-A-SCHOOL Æ ATTEND-A-SCHOOL Æ ATTEND-AN-EVENT-ORGANIZATION Æ

INTERACT Æ ACTION

The verb “transfer” followed by [to NP], where the head noun of the NP is an

educational-institution is recognized as transfer-to-school which is also a

subpredicate of attend-a-school. In these cases, the designer of the KE has to do

nothing because the predicate attend-a-school already exists, and the integrator is

going to integrate this predicate and its semantic roles onto the template. The

hierarchies of predicates in the interpreter have been designed to maximize the

inferences that can be established by inheritance and to anchor the inferences into a

generic predicate rather than on individual senses of verb forms, which would lead to

a proliferation of inference rules. However, inference rules connecting generic

predicates will be needed as explained in the next section. These observations apply

to every class of predicates constructed by the interpreter. For instance, if one wants

to extract knowledge about the things people value/respect/appreciate, etc. the

interpreter has already the predicate value-something whose hierarchy is:

VALUE-SOMETHING Æ RESPECT-VALUE-SOMETHING Æ JUDGE Æ ACTION

This predicate does not only include one of the senses of “value,” but all WordNet

verbs under the class respect1 (see below), and treasure, appreciate, and one of the

senses of “recognize.”

respect, esteem, value, prize, prise

=> think the world of

=> reverence, fear, revere, venerate

=> enshrine, saint

=> worship

=> admire, look up to

If one wants to build a template for each of the jobs somebody had, their location,

time, and duration, the interpreter already provides a hierarchy of subpredicates of

work-be-employed. For instance, the predicate do-service, encompassing such usages

as “serve as ambassador/teacher/etc,” has the hierarchy:

DO-SERVICE Æ WORK-BE-EMPLOYEED Æ TRANSFER-OF-POSSESSION Æ ACTION

Some inference rules connecting a few other predicates to work-be-employed

would be the only things that the KE designer would need to do.

5 Lateral Inferences

However, not all inferences can be established from the hierarchical organization of

the predicates. Besides linking the predicates strictly up the hierarchy, predicates need

to be connected laterally. This is done by defining inference rules. These rules infer

predicates and map semantic roles from the inferring predicate into roles of the

inferred predicate. For instance, the predicates graduate-from, study-a-subject, and

others are not classified as subpredicates of attend-a-school. However, that relation

needs to be inferred if the sentence is “X graduated from Y,” or “X studied at Y,”

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where Y is an educational-institution. The hierarchy for receive-an-academic-degree

is:

RECEIVE-AN-ACADEMIC-DEGREE Æ GET-AWARD Æ GET Æ

TRANSFER-OF-POSSESSION Æ ACTION

The output of the semantic interpreter for a sentence of the form “X received a

Ph.D from Y” is: “X” is the agent, “a Ph.D” is the theme, and “from Y” is the from-

poss. In this case, a rule needs to be defined in the predicate receive-an-academic-

degree, which infers attend-a-school and maps semantic roles from receive-an-

academic-degree to attend-a-school. This is the rule:

((if% x-is-a $from-poss educational-institution)

(add-inference

(((pr (attend-a-school)) (agent ($agent)) (to-loc ($from-poss))

(end-time ($at-time)) (degree-of-study ($theme))

(graduate-at-age ($at-the-age))))))

The rule says that if the from-poss role of the predicate receive-an-academic-

degree is a subconcept of educational-institution, then infer the predicate attend-a-

school with agent the agent of the predicate receive-an-academic-degree, with to-loc

the from-poss of receive-an-academic-degree, etc. In general, the syntax for role

mapping in the inference rules is:

(<role>(<$role>)

where <role> is the role in the predicate being inferred, and <$role> is the role of

the predicate in which the inference rule is anchored. If a <role> does not exist in the

output of the interpreter, then the <$role> in the inferred predicate is nil.

Here again, the hierarchies of predicates minimize the need of inference rules since

inference rules are inherited by subpredicates from superpredicates. For instance,

graduate-from inherits the rule from receive-an-academic-degree. The inference rules

can be viewed as a semantic network of predicates connected by conditional links.

Besides connecting the predicates, the network maps semantic roles from predicate X

into roles of predicate Y. Predicate Y may connect to other predicates, or infer other

predicates as you prefer to express it, by means of these conditional links. Predicate X

may infer Y, and Y may infer X. That is to say the link connecting X to Y may be

bidirectional. For instance, one may want to infer that “X attended Y” from “X

studied at Y,” and that “X studied at Y” from “X attended Y.” In fact, there is an

inference rule on attend-a-school that infers study-a-subject and vice versa. The

algorithm that fires the rules is not caught in a circularity because it keeps track of all

predicates that have been inferred. The algorithm is:

• Let A be the interpretation structure built by the interpreter. Initialize the list

Exclude to nil. Initialize the list Inferences to nil. After applying this algorithm,

this list will contain the inferences obtained from the predicate in A.

1. Let pr be the predicate in A. Add pr to the list Exclude.

2. Let SI be the list of structures obtained from firing the inference rules

associated with pr.

3. For each structure a1 in SI

a) If the predicate of a1 is not in the list Exclude,

b) Add a1 to the list Inferences.

c) Apply steps 1, 2, and 3 with A replaced by a1.

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These rules are easy to understand and can be easily written by someone with very

little knowledge of natural language processing. For this application, we defined 35

rules anchored on 18 predicates. Space limitations impede us from illustrating this

algorithm with some examples.

6 Testing

In order to test the system, we selected 50 articles at random from over 5,000

biographical articles in The World Book Encyclopedia. For each article selected, the

template built by a human was compared with the template built by the system. We

counted the number of slots in the template that were filled correctly by the system,

the number that were filled incorrectly, and the number that were missed. We let C be

the total number of correct slots for all articles, I the total number of incorrect slots,

and M the total number of missed slots. Then, the measure of recall is given by

C/(C+I+M), and the measure of precision is C/(C+I). The results obtained were 87%

recall and 97% precision.

Many of the articles selected contained only two or three sentences relevant to the

task. This is just the nature of the biographical articles in the World Book

Encyclopedia. A lower number of articles contained between four and 10 relevant

sentences, and a few others more than ten. The system gets very few incorrect slots,

and therefore very high precision, because of the accuracy of the semantic interpreter.

The system fails to interpret some adverbial clauses with an elliptical verb, e.g.,

“After a few months at Oxford University, Brummell was left ....” This is a problem

that has recurred several times, and which we plan to solve in a general way. In the

Carter and Eisenhower articles, the system fails to infer that “to receive an

appointment to the US Naval Academy” means to be admitted to the US Naval

Academy as student, e.g., “In 1942, .... Carter received an appointment to the US

Naval Academy.” Other failures are due to some discourse problems, which in

general are not acute in the Encyclopedia. We use a centering model [6] with specific

knowledge based on the rhetorical structure of the encyclopedic articles. In the

sentence, “When Dutch was 9 years old, he and ... settled in Dixon, Ill, where the boy

finished high school,” the system does not resolve the definite reference “the boy.”

7 Sample Session

In this example we illustrate the performance of the system when reading the John F.

Kennedy biographical article from the World Book Encyclopedia, and extracting

knowledge concerning the educational institutions he attended. For each institution

attended, the system fills a template consisting of the following slots:

[ (ATTENDED ()) (LOCATION ()) (TIME ()) (FROM-TIME ()) (END-TIME ())

(AT-THE-AGE ()) (ENTER-AT-AGE ()) (GRADUATE-AT-AGE ()) (SUBJECT ()) ]

The "attended" slot indicates the institution attended, "location" is the location of

the institution, "time" indicates when the institution was attended ("he attended

Harvard in 1950"), "from-time" and "end-time" are the starting time and end time of

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attendance, "at-the-age," "enter-at-age" and "graduate-at-age" indicate attendance in

terms of the age of the individual ("he entered Harvard at the age of 20"), and

"subject" refers to the field of study.

The Kennedy article is fairly long, containing over 300 sentences, most of which

are not related to the educational institutions attended by Kennedy. A "skimmer"

module first selects the sentences deemed relevant to the knowledge extraction

problem at hand, and only these sentences are interpreted. The sentences selected

from the Kennedy article include the following:

John Kennedy attended elementary schools in Brookline and Riverdale. In 1930,

when he was 13 years old, his father sent him to the Canterbury School in New

Milford, Conn. The next year, he transferred to Choate Academy in Wallingford,

Conn. Kennedy was graduated from Choate in 1935 at the age of 18. He enrolled at

Princeton University that fall, but he developed jaundice and left school after

Christmas. He entered Harvard University in 1936. There he majored in government

and international relations. Kennedy was graduated cum laude in 1940. He then

enrolled in the Stanford University graduate business school, but dropped out six

months later.

The system parses, interprets and builds representation structures for each sentence

in the input. Here we show the parser output for the first sentence:

G0 (SUBJ ((PN JOHN KENNEDY))

VERB ((MAIN-VERB ATTEND ATTENDED) (TENSE SP))

OBJ ((ADJ ELEMENTARY) (NOUN SCHOOLS))

PREP (AND ((IN ((PN BROOKLINE)))) ((IN ((PN RIVERDALE))))))

The parser output becomes the input to the interpreter, which produces the

following interpretation:

Clause CL1

SUBJ : (PERSON JOHN_FITZGERALD_KENNEDY) <(AGENT)>

VERB : ATTENDED <ATTEND-A-SCHOOL-I:NIL supported by 2 GRs>

OBJ : (GRADE_SCHOOL1 ELEMENTARY_SCHOOL1) <(TO-LOC)>

PREP : AND

IN PREP-NP: (LOCATION BROOKLINE) <(AT-LOC)>

Attach: Verb Confidence: WEAK

IN PREP-NP: (LOCATION RIVERDALE) <(AT-LOC)>

Attach: Verb Confidence: WEAK

The verbal concept is identified as "attend-a-school." The "agent" of the action is

Kennedy, a subconcept of "person." The "to-loc" role is "elementary school," a

subconcept of "grade-school." Brookline and Riverdale are subconcepts of "location"

and fill the "at-loc" semantic role.

The interpreter output is then transformed into the following set of knowledge

representation structures:

RIVERDALE

(instance-of (location)) (related-to (@a9) (@a10) (@a11))

BROOKLINE

(instance-of (location)) (related-to (@a9) (@a10) (@a11))

ELEMENTARY_SCHOOL1

(is-a (grade_school1)) (related-to (@a9) (@a10) (@a11))

JOHN_FITZGERALD_KENNEDY

(is-a (person)) (attend-a-school ($null ($more (@a9) (@a11))))

(study-a-subject ($null ($more (@a10))))

106

@A9

(args (john_fitzgerald_kennedy) (elementary_school1) (brookline)

(riverdale))

(pr (attend-a-school))

(agent (john_fitzgerald_kennedy (q (constant))))

(to-loc (elementary_school1 (q (constant))))

(at-loc (brookline (q (constant))) (riverdale (q (constant))))

(instance-of (action)) (time (past))

ATTEND-A-SCHOOL

(related-to (@a9) (@a11))

@A10

(instance-of (inference (@a9))) (pr (study-a-subject))

(agent (john_fitzgerald_kennedy (q (constant))))

(args (john_fitzgerald_kennedy) (elementary_school1) (brookline)

(riverdale))

(at-educational-institution (elementary_school1 (q (constant))))

(at-loc (brookline (q (constant))) (riverdale (q (constant))))

(time (past))

STUDY-A-SUBJECT

(related-to (@a10))

The knowledge in these structures is used to fill the predefined knowledge

extraction template, yielding the entry:

JOHN_FITZGERALD_KENNEDY

(ATTEND-A-SCHOOL (ELEMENTARY_SCHOOL1))

(LOCATION (BROOKLINE) (RIVERDALE))

After reading all the relevant sentences, the output produced is:

JOHN_FITZGERALD_KENNEDY

(ATTEND-A-SCHOOL (ELEMENTARY_SCHOOL1))

(LOCATION (BROOKLINE) (RIVERDALE))

(ATTEND-A-SCHOOL (CANTERBURY_SCHOOL))

(LOCATION (NEW_MILFORD)) (TIME (1930))

(TRANSFER-TO-SCHOOL (CHOATE_ACADEMY))

(LOCATION (WALLINGFORD)) (TIME (NEXT_YEAR))

(FROM-TIME (NEXT_YEAR)) (GRADUATE-AT-AGE (18)) (END-TIME (1935))

(ATTEND-A-SCHOOL (PRINCETON_UNIVERSITY1))

(FROM-TIME (THAT_FALL))

(ENTER-A-SCHOOL (HARVARD_UNIVERSITY1))

(TIME (1936)) (FROM-TIME (1936))

(SUBJECT (GOVERNMENT1) (INTERNATIONAL_RELATION))

(ATTEND-A-SCHOOL (STANFORD_UNIVERSITY_GRADUATE_BUSINESS_SCHOOL))

(FROM-TIME (THEN1))

Some time references still need to be solved, such as “next-year,” “that-fall,” and

“then,” which can be done by accessing the other entries in the frame. However, these

temporal references have not been implemented. These results show the system's high

degree of precision. Recall is also high, having missed Kennedy's age when entering

Canterbury School, the end time for Princeton, the graduation date for Harvard, and

the end time for Stanford.

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8 Related Work

This work is related to that described in [7] in which the acquisition of knowledge is

closely connected to the semantic interpretation process. A paper that deals with the

issue of inferences using WN is [8]. The authors implement a marker propagation

algorithm that uses the verb entailment, the glosses and the concept hierarchy in WN.

As the authors observe, the lack of semantic relations for the verbs and the few

number of entailments that WN provides are some of the serious limitations with their

approach.

There have been several systems in relation to the MUC project [12] that extract

patterns from texts. These systems rely on the user to identify the relevant patterns, or

on annotated corpora. None of these systems approach the semantic interpretation of

complete sentences. In some of these systems, the user identifies the patterns of

interest and the system uses WN for the generalization process. Riloff [13] generates

extraction patterns from annotated texts. Other systems require pre-constructed

templates [1]. However, a semi-automated system that does not require annotated

texts is [14] that constructs a domain lexicon by using a bootstrapping algorithm that

starts with a set of seed words, and adds new words belonging to a semantic category.

The enhanced list of seed words is then reviewed by a human who selects the words

that should be added to the domain lexicon from those proposed by the algorithm.

This system may be very useful for building lexicons for specialized domains, but not

for acquiring knowledge from encyclopedic texts which deal with general domain

knowledge. Moreover, because the system does not address the issues of semantic

interpretation in a general context, its scope of applications will be limited to the

extraction of some well-defined patterns. Similar remarks apply to the work on

acquiring hyponyms from patterns that originated in [9]. This work does not assign

meaning to the constituents of the sentence.

This work also differs from work reported in [10] in that the knowledge acquisition

designer does not have to be concerned with defining ontological categories, or

semantic interpretation rules because they are already part of the semantic interpreter.

Moreover, the ontological categories, namely those of WordNet, are of a general

nature and have received a wide acceptance in the natural language processing

community.

A critique that can be leveled against our approach could be that it needs the hand-

crafted construction of verb predicates, which is a rather difficult and time-consuming

job. The reply to this is that once the verb predicates are defined, they are defined for

every natural language application. This is so because their definitions are not tied to

any given application, and their selectional restrictions are based on a general

ontology of English. In [4], the reader may find a progress to date of the goal of

building predicates for English verbs regardless of domain. In this paper, we have

shown that the predicates can be applied to a knowledge extraction task from an

encyclopedia of intermediate complexity.

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9 Conclusions

We have described a knowledge extraction system that acquires knowledge from

encyclopedic texts. The system is based on a general semantic interpreter of English

that uses the WordNet ontology for nouns and verb predicates constructed for

WordNet verb classes. Because the knowledge extraction system and the semantic

interpreter share the same ontology and because the inferences of the KE are based on

the structure and organization of the predicates used by the semantic interpreter, the

definition of new extraction tasks is relatively easy. The system has been tested in the

The World Book Encyclopedia producing very solid results.

References

1. M. E. Califf and R.J. Mooney. Relational learning of pattern match rules for information

extraction. In Proc. of the ACL Workshop on Natural Language Learning, pages 9-15,

Stanford, California, 1997.

2. C. Fellbaum. A semantic network of english verbs. In C. Fellbaum, editor, WordNet: An

electronic Lexical Database and some of its applications, page chapter 3. MIT Press, 1998,

Cambridge, Mass, 1998.

3. F. Gomez. An algorithm for aspects of semantic interpretation using an enhanced wordnet.

In Proccedings of the 2nd North American Meeting of the North American Association for

Computational Linguistics, NAACL-2001, pages 87-94, 2001.

4. F. Gomez. Building verb predicates: A computational view. In Proceedings of the 42nd

Annual Meeting of the Association for Computational Linguistics, ACL-04, pages 351- 358,

Barcelona, 2004.

5. F. Gomez. Grounding the ontology on the semantic interpretation algorithm. In

Proceedings of the Second International WordNet Conference, pages 124-129, Masaryk

University, Brno, 2004.

6. B. J. Grosz, A. K. Joshi, and S. Weinstein. Centering: A framework for modeling the local

coherence of discourse. Computational Linguistics, 21(2):201-225, 1995.

7. U. Hahn and K. Schnattinger. A text understander that learns. In COLING-ACL, pages 476-

482, Montreal, Quebec, 1998.

8. S. Harabagiu and D. Moldovan. Knowledge processing on extended wordnet. In C.

Fellbaum, editor, WordNet: An electronic Lexical Database and some of its applications,

pages 379-405. MIT Press, 1998, Cambridge, Mass, 1998.

9. M.A. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proc. of

COLING-92, pages 530-545, Nantes, France, 1992.

10. R.D. Hull and F. Gomez. Automatic acquisition of biographic knowledge from

encyclopedic texts. Expert Systems with Applications, 16:261-270, 1999.

11. George Miller. Nouns in wordnet. In C. Fellbaum, editor, WordNet: An electronic Lexical

Database and some of its applications, page chapter 1. MIT Press, 1998, Cambridge, Mass,

1998.

12. MUC-4. Proc. of the Fourth Message Understanding Conference (MUC-4). Morgan

Kaufmann, San Mateo, California, 1992.

13. E. Riloff. An empirical study of automated dictionary construction for information

extraction in three domains. Artificial Intelligence, 85:101-134, 1996.

14. E. Riloff and M. Schmelzenbach. An empirical approach to conceptual case frame

acquisition. In Proc. of the Sixth Workshop on Very Large Corpora, pages 0-0, 1998.

15. Y.A. Wilks. Preference semantics. In E.L. Keenan, editor, Formal Semantics of Natural

Language. Cambridge University Press, Cambridge, UK, 1975.

109