NEW YORK UNIVERSITY DESCRIPTION OF THE PROTEUS SYSTEM AS USED FOR MUC- 4
 Ralph Grishman, Catherine Macleod, and John Sterling , The PROTEUS Project 
Computer Science Departmen t New York University 715 Broadway, 7th Floor New 
York, NY 1000 3 { grishman,macleod,sterling)@cs .nyu.edu HISTORY
 The PROTEUS system which we have used for MUC-4 is largely unchanged from that 
used for MUC-3 . It has three main components : a syntactic analyzer, a semantic 
analyzer, and a template generator . The PROTEUS Syntactic Analyzer was 
developed starting in the fall of 1984 as a common base for all th e 
applications of the PROTEUS Project. Many aspects of its design reflect its 
heritage in the Linguistic Strin g Parser, previously developed and still in use 
at New York University . The current system, including the Restriction Language 
compiler, the lexical analyzer, and the parser proper, comprise approximately 
4500 lines of Commo n Lisp. The Semantic Analyzer was initially developed in 
1987 for the MUCK-I (RAINFORMs) application , extended for the MUCK-II (OPREPs) 
application, and has been incrementally revised since . It currently consist s 
of about 3000 lines of Common Lisp (excluding the domain-specific information) . 
The Template Generator was written from scratch for the MUC-3 task and then 
revised for the MUC-4 templates; it is about 1200 lines of Common Lisp..
 
STAGES OF PROCESSING
 The text goes through the five major stages of processing : lexical analysis, 
syntactic analysis, semantic analysis, reference resolution, and template 
generation (see Figure 1) . In addition, some restructuring of the logical form 
is performed both after semantic analysis and after reference resolution (only 
the restructuring after referenc e resolution is shown in Figure 1) . Processing 
is basically sequential: each sentence goes through lexical, syntactic , and 
semantic analysis and reference resolution ; the logical form for the entire 
message is then fed to template generation . However, semantic (selectional) 
checking is performed during syntactic analysis, employing essentiall y the same 
code later used for semantic analysis . Each of these stages is described in a 
section which follows .
 
LEXICAL ANALYSI S Dictionary Format
 Our dictionaries contain only syntactic information : the parts of speech for 
each word, information about the complement structure of verbs, distributional 
information (e .g ., for adjectives and adverbs), etc . We follow closely the 
set of syntactic features established for the NYU Linguistic String Parser . 
This information is entered in LISP form using noun, verb, adjective, and adverb 
macros for the open-class words, and a word macro for other parts of speech : 
(ADVERB "ABRUPTLY" :ATTRIBUTES (DSA) ) (ADJECTIVE "ABRUPT" ) (NOUN :ROOT 
"ABSCESS" :ATTRIBUTES (NCOUNT) )
 
Knowledge Sources Dictionary
 
Text Lexical Analysi s
 
Grammar
 
Syntactic Analysi s
 
Semantic Models
 
Semantic Analysis
 
Concept Hier.
 
Reference Resolutio n
 
Mapping Rules
 
LF Transformatio n
 
Template Generatio n Template s
 
Figure 1 . Structure of the Proteus System as used for MUC-4
 
(VERB :ROOT "ABSCOND" :OBJLIST (NULLOBJ PN (PVAL (FROM WITH))) ) The noun and 
verb macros automatically generate the regular inflectional forms .
 
Dictionary Files
 The primary source of our dictionary information about open-class words (nouns, 
verbs, adjectives, an d adverbs) is the machine-readable version of the Oxford 
Advanced Learner's Dictionary ("OALD") . We have writ ten programs which take 
the SGML (Standard Generalized Markup Language) version of the dictionary, 
extrac t information on inflections, parts of speech, and verb subcategorization 
(including information on adverbial particles and prepositions gleaned from the 
examples), and generate the LISP-ified form shown above . This is supplemented 
by a manually-coded dictionary (about 1500 lines, 900 entries) for closed-class 
words, words no t
 
fiadequately defined in the OALD, and a few very common words . For MUC-4 we 
used several additional dictionaries . There was a dictionary (about 900 lines) 
for domain specific English words not defined in the OALD, or too richly defined 
there . In addition, we extracted from the tex t and templates lists of 
organizations, locations, and proper names, and prepared small dictionaries for 
each (abou t 2500 lines total) .
 
Looku p
 The text reader splits the input text into tokens and then attempts to assign to 
each token (or sequence o f tokens, in the case of an idiom) a definition (part 
of speech and syntactic attributes) . The matching process proceeds in four 
steps: dictionary lookup, lexical pattern matching, spelling correction, and 
prefix stripping . Dictionary lookup immediately retrieves definitions assigned 
by any of the dictionaries (including inflected forms) , while lexical pattern 
matching is used to identify a variety of specialized patterns, such as numbers, 
dates, times , and possessive forms . If neither dictionary lookup nor lexical 
pattern matching is successful, spelling correction and prefix strippin g are 
attempted. For words of any length, we identify an input token as a misspelled 
form of a dictionary entry if on e of the two has a single instance of a letter 
while the other has a doubled instance of the letter (e .g ., "mispelled" and 
"misspelled") . For words of 8 or more letters, we use a more general spelling 
corrector which allows for any singl e insertion, deletion, or substitution. [ 
The prefix stripper attempts to identify the token as a combination of a prefix 
and a word defined in the dictionary. We currently use a list of 17 prefixes, 
including standard English ones like "un" and MUC-3/MUC-4 specials like "narco-" 
. If all of these procedures fail, the word is tagged as a proper noun (name), 
since we found that most of ou r remaining undefined words were names . For 
MUC-4, we have incorporated the stochastic part-of-speech tagger from BBN in 
order to assign probabilities to each part-of-speech assigned by the lexical 
analyzer. The log probabilities are used as scores, and combined with other 
scores to determine the overall score of each parsing hypothesis .
 
Filtering
 In order to avoid full processing of sentences which would make no contribution 
to the templates, we per form a keyword-based filtering at the sentence level : 
if a sentence contains no key terms, it is skipped . This filtering is done 
after lexical analysis because the lexical analysis has identified the root form 
of all inflected words ; these root forms provide links into the semantic 
hierarchy . The filtering can therefore be specified in terms of a small number 
of word classes, one of which must be present for the sentence to be worth 
processing .
 
SYNTACTIC ANALYSI S
 Syntactic analysis involves two stages of processing : parsing and syntactic 
regularization . At the core of th e system is an active chart parser. The 
grammar is an augmented context-free grammar, consisting of BNF rules plu s 
procedural restrictions which check grammatical constraints not easily captured 
in the BNF rules . Most restrictions are stated in PROTEUS Restriction Language 
(a variant of the language developed for the Linguistic String Parser ) and 
translated into LISP ; a few are coded directly in LISP [1] . For example, the 
count noun restriction (that singular countable nouns have a determiner) is 
stated as WCOUNT = IN LNR AFTER NVAR : IF BOTH CORE Xcore IS NCOUNT AND Xcore IS 
SINGULA R THEN IN LN, TPOS IS NOT EMPTY . Associated with each BNF rule is a 
regularization rule, which computes the regularized form of each node i n the 
parse tree from the regularized forms of its immediate constituents . These 
regularization rules are based on lambda-reduction, as in GPSG . The primary 
function of syntactic regularization is to reduce all clauses to a standard form 
consisting of aspect and tense markers, the operator (verb or adjective), and 
syntactically marked cases . ' The minimum word length requirement is needed to 
avoid false hits where proper names are incorrectly identified as misspellings 
of words defined in the dictionary.
 
For example, the definition of assertion, the basic S structure in our grammar, 
i s <assertion> . ._ <sa> <subject> <sa> <verb> <sa> <object> <sa > :(s 
!(<object> <subject> <verb> <sa*>)) . Here the portion after the single colon 
defines the regularized structure. Coordinate conjunction is introduced by a 
metarule (as in GPSG), which is applied to the context-free components of the 
grammar prior to parsing . The regularization procedure expands any conjunction 
into a conjuntio n of clauses or of noun phrases . The output of the parser for 
the first sentence of TST2-0048, "SALVADORAN PRESIDENT-ELEC T ALFREDO CRISTIANI 
CONDEMNED THE TERRORIST KILLING OF ATTORNEY GENERAL ROBERTO GARCIA ALVARADO AND 
ACCUSED THE FARABUNDO MARTI NATIONAL LIBERATION FRON T (FMLN) OF THE CRIME.", i 
s (SENTENC E (CENTERS (CENTE R (ASSERTIO N (ASSERTIO N (SUBJEC T (NSTG (LNR (LN 
(NPOS (NPOSVAR (LCDN (ADJ "SALVADORAN")) (N "PRESIDENT" "-" "ELECT"))) ) (NVAR 
(NAMESTG (LNAMER (N "ALFREDO") (MORENAME (N "CRISTIANI"))))))) ) (VERB (LTVR (TV 
"CONDEMNED")) ) (OBJECT (NSTGO (NSTG (LNR (LN (TPOS (LTR (T "THE"))) (NPOS 
(NPOSVAR (N "TERRORIST"))) ) (NVAR (N "KILLING") ) (RN (RN-VA L (PN (P "OF" ) 
(NSTGO (NST G (LNR (LN (NPOS (NPOSVAR (N "ATTORNEY" "GENERAL"))) ) (NVA R 
(NAMEST G (LNAMER (N "ROBERTO" ) (MORENAME (N "GARCIA") (MORENAME (N 
"ALVARADO")))))))))))))))) ) (CONJ-WORD ("AND" "AND") ) (ASSERTION (SUBJEC T 
(NSTG (LN R (LN (NPOS (NPOSVAR (LCDN (ADJ "SALVADORAN")) (N "PRESIDENT" "-" 
"ELECT"))) ) (NVAR (NAMESTG (LNAMER (N "ALFRED O " ) (MORENAME (N 
"CRISTIANI"))))))) ) (VERB (LTVR (TV "ACCUSED")) ) (OBJEC T (NPN (NSTG O (NST G 
(LNR (LN (TPOS (LTR (T "THE"))) ) (NVAR (NAMESTG (LNAMER (N "FARABUNDO" "MARTI" 
"NATIONAL" "LIBERATION" "FRONT") )
 
fi(NAME-APPOS ("(" "(" ) (NSTG (LNR (NVAR (NAMESTG (LNAMER (N "FMLN")))))) (' 
.')" ")")))))) ) (PN (P "OF" ) (NSTGO (NSTG (LNR (LN (TPOS (LTR (T "THE")))) 
(NVAR (N "CRIME"))))))))))) ) (ENDMARK (" ." " .")) ) and the corresponding 
regularized structure is (AND (S CONDEMN (VTENSE PAST ) (SUBJEC T (NP A-NAME 
SINGULAR (NAMES (ALFREDO CRISTIANI)) (SN NP1499 ) (N-POS (NP PRESIDENT-ELECT 
SINGULAR (SN NP1489) (A-POS SALVADORAN)))) ) (OBJEC T (NP KILLING SINGULAR (SN 
NP1532) (T-POS THE ) (N-POS (NP TERRORIST SINGULAR (SN NP1504)) ) (OF (NP A-NAME 
SINGULAR (NAMES (ROBERTO GARCIA ALVARADO)) (SN NP1531 ) (N-POS (NP 'ATTORNEY 
GENERAL' SINGULAR (SN NP1506))))))) ) (S ACCUSE (VTENSE PAST ) (SUBJECT (NP 
A-NAME SINGULAR (NAMES (ALFREDO CRISTIANI)) (SN NP1499 ) (N-POS (NP 
PRESIDENT-ELECT SINGULAR (SN NP1489) (A-POS SALVADORAN)))) ) (OBJEC T (NP FMLN 
SINGULAR (RN-APPOS (NP FMLN SINGULAR (SN NP1539))) (SN NP1544 ) (T-POS THE)) ) 
(OF (NP CRIME SINGULAR (SN NP1543) (T-POS THE)))) ) The system uses a chart 
parser operating top-down, left-to-right . As edges are completed (i.e., as 
nodes of the parse tree are built), restrictions associated with those 
productions are invoked to assign and test features of th e parse tree nodes . 
If a restriction fails, that edge is not added to the chart . When certain 
levels of the tree are complete (those producing noun phrase and clause 
structures), the regularization rules are invoked to compute a regularized 
structure for the partial parse, and selection is invoked to verify the semantic 
well-formedness of the structure (as noted earlier, selection uses the same 
"semantic analysis" code subsequently employed to translate the tre e into 
logical form). One unusual feature of the parser is its weighting capability . 
Restrictions may assign scores to nodes ; th e parser will perform a best-first 
search for the parse tree with the highest score . This scoring is used to 
implement various preference mechanisms: · closest attachment of modifiers (we 
penalize each modifier by the number of words separating it from it s head) · 
preferred narrow conjoining for clauses (we penalize a conjoined clause 
structure by the number of words i t subsumes) · preference semantics (selection 
does not reject a structure, but imposes a heavy penalty if the structure doe s 
not match any lexico-semantic model, and a lesser penalty if the structure 
matches a model but with som e operands or modifiers left over) [2,3 ] · 
relaxation of certain syntactic constraints, such as the count noun constraint, 
adverb position constraints, and comma constraint s · disfavoring (penalizing) 
headless noun phrases and headless relatives (this is important for parsing 
efficiency) The grammar is based on Harris's Linguistic String Theory and 
adapted from the larger Linguistic Strin g Project (LSP) grammar developed by 
Naomi Sager at NYU [4] . The grammar is gradually being enlarged to cove r more 
of the LSP grammar. The current grammar is 1600 lines of BNF and Restriction 
Language plus 300 lines o f Lisp; it includes 186 non-terminals, 464 
productions, and 132 restrictions . Over the course of the MUCs we have added 
several mechanisms for recovering from sentences the gram mar cannot fully parse 
:
 
allowing the grammar to skip a single word, or a series of words enclosed in 
parentheses or dashes, with a large score penalty if no parse is obtained for 
the entire sentence, taking the analysis which, starting at the first word, 
subsumes the most word s optionally, taking the remainder of the sentence and 
"covering" it with noun phrases and clauses, preferrin g the longest noun 
phrases or clauses which can be identifie d
 
SEMANTIC ANALYSIS AND REFERENCE RESOLUTIO N
 The output of syntactic analysis goes through semantic analysis and reference 
resolution and is then added t o the accumulating logical form for the message . 
Following both semantic analysis and reference resolution certai n 
transformations are performed to simplify the logical form . All of this 
processing makes use of a concept hierarch y which captures the 
class/subclass/instance relations in the domain. Semantic analysis uses a set of 
lexico-semantic models to map the regularized syntactic analysis into a semantic 
representation. Each model specifies a class of verbs, adjectives, or nouns and 
a set of operands ; for eac h operand it indicates the possible syntactic case 
markers, the semantic class of the operand, whether or not th e operand is 
required, and the semantic case to be assigned to the operand in the output 
representation . For example, the model for "<explosive-object> damages 
<target>" i s (add-clause-model :id 'clause-damage- 3 :parent 'clause-an y 
:constraint 'damag e :operands (list (make-specifie r :marker 'subjec t :class 
'explosive-objec t :case :instrument ) (make-specifie r :marker 'objec t :class 
'target-entit y :case :patien t :essential-required 'required)) ) The models are 
arranged in a shallow hierarchy with inheritance, so that arguments and 
modifiers which are shared by a class of verbs need only be stated once. The 
model above inherits only from the most general clause model , clause--any, 
which includes general clausal modifiers such as negation, time, tense, 
modality, etc . The evaluated MUC-4 system had 124 clause models, 21 
nominalization models, and 39 other noun phrase models, a total of about 2500 
lines . The class explosive--object in the clause model refers to the concept in 
the concept hierarchy, whose entries have the form : (defconcept 
explosive-object :typeof instrument-type ) (defconcept explosive :typeof 
explosive-objec t :muctype explosive ) (defconcept grenade :typeof explosive ) 
(defconcept explosive-charge :typeof explosiv e :alias (dynamite-charge) ) 
(defconcept bomb :typeof explosive-objec t :muctype bomb ) (defconcept (VEHICLE 
BOMB( :typeof explosive-objec t :muctype (VEHICLE BOMB( ) (defconcept car-bomb 
:typeof VEHICLE BOMB( ) (defconcept bus-bomb :typeof IVEHICLE BOMB( ) 
(defconcept dynamite :typeof explosive-objec t :alias tnt :muctype DYNAMITE ) 
There are currently a total of 2474 concepts in the hierarchy, of which 1734 are 
place names . The output of semantic analysis is a nested set of entity and 
event structures, with arguments labeled by key words primarily designating 
semantic roles . For the first sentence of TST2-0048, the output i s
 
Reference Resolution
 Reference resolution is applied to the output of semantic analysis in order to 
replace anaphoric noun phrase s (representing either events or entities) by 
appropriate antecedents . Each potential anaphor is compared to prior entities 
or events, looking for a suitable antecedent such that the class of the anaphor 
(in the concept hierarchy) i s equal to or more general than that of the 
antecedent, the anaphor and antecedent match in number, the restrictiv e 
modifiers in the anaphor have corresponding arguments in the antecedent, and the 
non-restrictive modifiers (e .g . , apposition) of the anaphor are not 
inconsistent with those of the antecedent . Special tests are provided for names 
(people may be referred to a subset of the ir names) and for referring to groups 
by typical members ("terrorist force"
 
Logical Form Transformation s
 The transformations which are applied after semantic analysis and after 
reference resolution simplify an d regularize the logical form in various ways . 
For example, if a verb governs an argument of a nominalization, th e argument is 
inserted into the event created from the nominalization : "x conducts the 
attack", "x claims responsibility for the attack", "x was accused of the attack" 
etc . are all mapped to "x attacks" (with appropriate settings of th e 
confidence slot) . For example, the rule to take "X was accused of Y" and make X 
the agent of Y i s (((event :predicate accusation-even t :agent ?agent- 1 :event 
(event :identifier ?id-1 . ?R2 ) . ?R1 ) (event :identifier ?id-1 . ?R4) ) -> 
((modify 2 '( :agent ?agent-1 :confidence 'SUSPECTED OR ACCUSEDI) ) (delete 1)) 
) Transformations are also used to expand conjoined structures . For example, 
there is a rule to expand "the towns o f x and y " into "the town of x and the 
town of y", and there is a rule to expand "event at location-1 and location-2 " 
into "event at location-1 and event at location-2" . There are currently 32 such 
rules. These transformations are written as productions and applied using a 
simple data-driven production system interpreter which is part of the PROTEUS 
system .
 
TEMPLATE GENERATO R
 Once all the sentences in an article have been processed through syntactic and 
semantic analysis, the resulting logical forms are sent to the template 
generator . The template generator operates in four stages . First, a frame 
structure resembling a simplified template (with incident-type, perpetrator, 
physical-target, human-target, date , location, instrument, physical-effect, and 
human-effect slots) is generated for each event . Date and locatio n expressions 
are reduced to a normalized form at this point . In particular, date expressions 
such as "tonight", "last month", " last April", "a year ago", etc. are replaced 
by explicit dates or date ranges, based on the dateline of th e article. Second, 
a series of heuristics attempt to merge these frames, mergin g · frames 
referring to a common target · frames arising from the same sentenc e · an 
effect frame following an attack frame (e.g ., "The FMLN attacked the town . 
Seven civilians died ." ) This merging is blocked if the dates or locations are 
different, the incident types are incompatible, or the perpetrators are 
incompatible . Third, a series of filters removes frames involving only military 
targets and those involvin g events more than two months old . Finally, MUC 
templates are generated from these frames .
 
fiSPONSORSHIP
 The development of the entire PROTEUS system has been sponsored primarily by the 
Defense Advanced Research Projects Agency as part of the Strategic Computing 
Program, under Contract N00014-85-K-0163 an d Grant N00014-90-J-1851 from the 
Office of Naval Research . Additional support has been received from th e 
National Science Foundation under grant DCR-85-01843 for work on enhancing 
system robustness .

REFERENCES
 
[1] Grishman, R . PROTEUS Parser Reference Manual . PROTEUS Project Memorandum #4-C, Computer Science Department, New York University, May 1990. 

[2] Grishman, R., and Sterling, J . Preference Semantics for Message Understanding. Proc . DARPA Speech and Natural Language Workshop, Morgan Kaufman, 1990 (proceedings of the conference at Harwich Port, MA, Oct . 15-18, 1989) . 

[3] Grishman, R., and Sterling, J . Information Extraction and Semantic Constraints . Proc . 13th Int' I Conf Computational Linguistics (COLING 90), Helsinki, August 20-25, 1990 . 

[4] Sager, N . Natural Language Information Processing, Addison-Wesley, 1981 .
