A News Analysis System 
Robert J. KUHNS 
Artificial Intelligence Center 
Arthur D. Little, Inc. 
Cambridge, MA 02140 USA 
Abstract 
This paper describes a prototype news analysis 
system Which classifies and indexes news stories in 
real time. The system extracts stories from a 
newswire, parses the sentences of the story, and 
then maps the syntactic structures into a concept 
base. This process results in an index containing 
both ger~eral categories and specific details. 
Central to this system is a Government-Binding 
parser which processes each sentence of a news 
item. The system is completely modular and can be 
interfaced with different news feeds or concept 
bases. 
1,0 Introduction 
This paper reports on a prototype news 
analysis system (NAS) which classifies and indexes 
news stories in real time. That is, the system 
receiving reports from a newswire is capable of 
classifying the reports and constructing an index 
of them. Once a news item is classified, it can 
then be routed to users for whom the story is 
pertinent. This system, which runs as an 
independent background process, is automatic and 
greatly reduces the amount of irrelevant 
information users must cope with. 
From a theoretical view, one significant 
aspect of the system is that its parser is a 
deterministic princlple-based Government-Binding 
(GB) system. Basing NAS on such a processor 
demonstrates capabilities of a syntax-oriented 
natural %anguage parser and how linguistic and 
world knowledge (primarily financial) can be 
interfaced to provide a useful application. While 
a pre-prototype of NAS was written in ZetaLisp on 
Symbolics and later ported to the TI Explorer and 
Explorer II, the current version is implemented in 
Symbolics Cool,on Lisp. 
~round 
Much of the text processing work has focused 
on methods for obtaining information or retrieving 
texts from large databases. Approaches are wide 
and inc\]ude key wording, statistical analysis, 
pattern matching, and a method using lexical, 
syntactic, and semantic filters. However, there 
are other applications for which these techniques 
seem inadequate. (/Hayes et al. 1988/ does 
describe a strictly pattern matching approach to 
news categorization.) For instance, consider the 
news and financial industries where those who 
gather and report news or trade stocks and bonds 
must read, analyze, and react to current electronic 
information from many different sources almost 
instantly. This need to process information within 
seconds, coupled with the fact that there is 
already ~t overwhelming amount of information that 
individuals must sort through in order to find 
relevant news, clearly shows the need for rapid and 
accurate indexing and routing systems. 
It was in this context that NAS was developed. 
The goal was to build a system with the capability 
of processing news stories received from active 
newswires, i.e., to be able to categorize each 
story against a set of general topics and more 
detailed subtopics in a matter of seconds. These 
stories and their associated indexes can then be 
routed to those interested individuals, thereby 
helping to reduce the load of irrelevant 
information that they must see in order to find the 
items which are pertinent to them. 
Having identified this need, a pre-prototype 
was built around an existing parser based closely 
on the work of Marcus /Marcus 1980/. Stories in 
the pro-prototype were manually entered and were 
chosen so that the concepts do not directly appear 
in the story. The system was not using key words 
or phrases and was characterizing stories at a 
certain level of abstraction. While there were 
only about 12 different concepts from which this 
system could process, it was able to classify a 
story as a merger/acquisition, for example, and 
provide the companies involved, including the buyer 
and the acquired company, and tell whether the 
merger was successful or not. 
The present version incorporates a more 
powerful parser than the one in the previous 
version, and it can identify over 200 concepts 
ranging from specific (a name of a company or 
currency) to abstract (marketing reports vs. 
analysis for some industry). Since NAS processes 
unrestricted sets of stories, concepts can be 
either explicit (specific ones) or in, licit 
(abstract ones) and NAS will select those which are 
appropriate for each story. 
The remaining sections will discuss the major 
components of NAS, the way it handles errors, and 
future directions. Several example stories and 
their indexes are also provided. 
3,0 The Architecture of NAS 
NAS consists of four major subsystems, viz., a 
stream filter, a lexical scanner, a parser, and a 
semantic processor or filter working sequentially 
as listed. The stream filter is the component 
which scans the news stream received via a 
satellite dish and selects only the textual news 
reports. Once a story is identified, it enters a 
lexieal scanner resulting in sequences of words 
that are distinguished as sentences of the incoming 
report. These sentences are passed to the parser 
which constructs syntactic structures which are 
then used by the semantic processor in mapping 
these structures onto representations of the 
concepts. The story and index can then he directed 
to interested users and/or entered into a database 
for future consultations. 
The underlying design consideration of this 
architecture was to modularlze stream, linguistic, 
and application-specific semantic processing. In 
this way, new interfaces to different newswlres, 
enhancements to the parser, or changes of 
application or a different concept base can be 
completed without impacting the other components, 
thereby enabling easy modification to NAS. 
351 
3,1 ~h~ Stream Filter 
NAS is currently interfaeed with a news feed 
which transmits news related to financial and 
commodity markets. Since the textual reports are 
interspersed with various quotations which are not 
input to NAS, the stream filter screens the 
non-textual items and directs only the stories 
containing text to NAS. When a quote is prefaced 
with several lines of text, the stream filter will 
send the item for proeesslng of the text while the 
numerical quotes are ignored. 
The stream filter can be deactivated and 
stories that have been stored on text files are 
then used as the corpus. The effect of changes to 
the system can now be traced when using static 
input. 
3,2 The Lexical sca~ne~ 
The lexical scanner receives the news reports 
from the stream filter and provides the parser with 
processlble input. It decomposes the incoming 
stream into words, numbers, distinguished 
characters, e.g., $ (dollar sign) and % (percent 
sign), punctuation, and sentences, and it also 
equates abbreviations with its unabbreviated form. 
The scanner has access to the lexicon and when it 
recognizes a word, it associates all the lexlcal 
information with that word. 
As the scanner is analyzing the stream of 
characters, it is also determining the presence of 
sentences, i.e., sentence delimiters. Sentences 
ending in question or exclamation marks are easily 
detectable. Although news services differ, 
algorithms which rely on the formatting scheme of 
the news source have been developed which find 
declarative sentences even in the cases where they 
contain perlod-final abbreviations. The beginning 
and end of a news story are characterized by 
distinguishing features, so story identification is 
trivial. 
3,$ The Parser 
Central to NAS is the parser which provides 
syntactic structures that are eventually mapped 
onto concepts resulting in an index for a story. 
The parser is a prlnclple-based GB parser and is a 
substantially revised "version of /Kuhns 1986/. 
(See /Abney 1986/,/Berwlck 1987/, /Kashket 1987/, 
/Thiersch 1987/, and /Wehrll 1987/ for descriptions 
of prlnclple-based parsers.) The parsing strategy 
is deterministic in that no temporary Structures 
are built or information deleted during the course 
of a parse (/Berwlek 1987/ and /Marcus 1980/). It 
sh6uld be noted that in connection with this type 
of application, speed is crucial and although a 
deterministic parser is strict in that it cannot 
backtrack or produce alternate parses in ambiguous 
sentences, its speed of approximately 100 
words/second in linear time is essential. 
The parser has two main subsystems, vlz., the 
set of inthractlng GB-modules and the lexicon. 
These modules include principles and constraints 
from Case and bounding theories and, especially , 
X-bar, thematic or 0, trace, binding, and control 
theories. These latter subsystems have a 
particularly prominent role for the parser. 
Predlcate-argument relations or 8-role assignments 
to arguments of predicates are determined by 
0-theory. In the ease where movement has occurred, 
trace theory will relate an .argument which now must 
reside in a position which cannot receive a 0-role 
with its empty category or trace in a 8-marked 
position from which the constituent has moved. 
352 
This enables the 0-role of the argument to be 
determined. Possible eoreferentlal relations for 
pronomlnals and anaphors are identified with 
principles of binding and control theory. 
Moreover, the Extended Projection Principle, 
8-Criterlon, and Case filter are observed by the 
parser. (For a full discussion of these modules 
and principles see /Chomsky 1981/.) 
The primary output of the parser is a set of 
licensing relations. "Licensing" is a cover term 
~for any of a number of possible relatlons b@tween_ 
projections. Nonmaximal projections are licensed 
by maximal projections via X-bar theory and these 
maximal projections are licensed by an argument.or 
a trace of an argument, a predicate, or an 
operator. Specifically, a predicate licenses its 
internal arguments or complements and its external 
argument or its subject. (Again for a more 
detailed discussion of these aspects of GB theory 
see /Chomsky 1986/.) 
In that the goal of the parser is to license 
projections of each element of a sentence, it can 
perform two basic operations. It can construct a 
projection of a lexical item in direct use of X-bar 
theory or it can establish or assert a licensing 
relation between two maximal projections with 
respect to other constraints of GB Theory. The 
parser proceeds by first building a maximal 
projection and then attempts to license it to 
another maximal projection or vice versa, i.e., 
another projection to it. 
Upon encountering a lexical item, the parser 
creates a maximal projection consisting of a set of 
features. Each node receives a type in terms of 
X-bar primitives (±N, +_V), an index, and its 
lexleal item from which it has projected. 
Relevant GB systems are invoked during the parse to 
determine binding relations and 8-role assignments. 
The proper index to encode binding or eoreferen~e 
will be incorporated in the projection and 
co-lndexed projections share all of their features. 
However, it is not always possible to assign an 
index or 0-role at the inception of a projection 
because of inadequate information. The parser will 
not commit itself and will only include the 
syntactie structure that it can derive at that 
stage of the parse. When the relevant information 
is available, the parser will incorporate it in the 
incomplete node which preserves the monotonicity of 
parsing information. This process is constrained 
to the current cyclic node which is the left 
bounded context of the parser. (/Knhns in 
preparation/ will discuss the specifics of this 
parser.) The parser produces a llst of lleenslng 
relations for each sentence of a news story. In 
turn it outputs an ordered llst of the relations 
corresponding to the sentences of a news report. 
This set is then passed to the semantic processor. 
The other component of the linguistic 
processor of NAS is the lexicon whi__ceh contains 
words and distinguished strings, together with 
their syntactic and subcategorlzatlon features 
including X-bar primitives (±~, iV), number, name 
or referential expressions, complement types, 
control features (for interpreting empty subjects 
(PRO) of infinitival complements), and @-grids or 
8-role assignments for predicates. An ambiguous 
lexleal entry has features for all of its potential 
types associated wlth that item and lexlcal 
ambiguity resolution procedures choose the 
appropriate features during the parse(/Milne 1983/ 
and /Milne 1986/). 
Morphology is minlmal~ reflecting :only 
r~atlon s bet,@on roots and th~i~. ~ri~a~onal 
forms and associations between words and affixes. 
Lexical redundancy rules for specifying 
correspol~dences between sets of features have been 
implemented. Since news reports frequently have 
abbreviations, lexlcal entries which have an 
abbreviated form will be marked as such, and when 
the abbreviation appears in a story, the lexical 
scanner ):etrieves the lexlcal information of the 
unabbreviated form. Relationships between lexical 
items and their extragrammatical features will be 
discussed below (Section 3.4). 
The lexicon consists of less than 15,000 
members and in building the lexicon the emphasis 
has been on the inclusion of verbs, adjectives, and 
prepositions. Names, espeelally of individuals, 
corporations, and geographical locations, not 
present In the lexicon are found in news reports 
regularly. While many familiar names are in the 
lexicon, unfamiliar nouns are handled by the error 
handling routines (Section 5.0). 
While the lexicon is updated as needed, the 
way it was originally constructed was to collect 
distinct "words" from stories received from a 
satellite feed. Numbers were disregarded but names 
and abbreviations were included. During several 
non-continuous weeks of scanning the stream for new 
words, the task of assigning syntactic features to 
each valid item began. While this is a laborious 
and time.consuming process, it was aided by a 
menu-drlw~n facility for feature assignment where 
typing wa!~ minimized and much time saved. 
Also~ during the time that previously unknown 
words were being "collected," a counter was 
indicatin~ the number of current words in 
increments of I00. When the llst was slightly over 
7,000, the nu,~er of new words being added to it 
slowed. Fur~,ermore, a point of convergence 
seemed to occur under 9,500 items. At this stage 
of lexicon development, a comparison of the 
existing words against a sample of over 50 words 
(mainly verbs and adjectives) taken from another 
news service suggested that the present llst was 
sufficient in that it contained every word taken 
from the news stories. This is significant because 
a system which is to parse sentences within a story 
must have the capability of recognizing each word. 
Since it appears that the vocabulary of reports is 
bounded (~ith the exception of names), rapid 
linguistic processing of news is realizable with 
respect to \].exical recognition. 
~_~/,. Tbe_j_e!,m_D_~c processor 
The semantic processor is an automatic pattern 
marcher which incorporates world knowledge that is 
used to determine the "meaning" of its linguistic 
input with respect to a set of topics and 
designators in its concept base. The term concept 
refers to a general notion such as merger/ 
acquisition, terrorism, currency report, or 
strikes and lockouts. Designators are subtopics 
which provide detail to an index. A story 
categorized as a merger/acqulsltlon could be 
further characterized by designators indicating 
specific tentacles involved or by the industries 
impacted. The existing system has the capability 
of processing over 200 concepts and designators. 
~e output of this processor (and NAS) is a 
classification or index of a story consisting of 
one or mor~) general concepts and their designators. 
%f no general concept is found, the system may 
still assign designators. In other words, a story 
may be ~,out Air France while the general 
classification is unknown. 
Structurally, the processor can be viewed as 
having a concept base and a #-relation interpreter 
which takes as input the predicate-argument 
structures denoted by #-relations and attempts to 
find matches with elements in the concept base. 
The concept base itself possesses an internal 
structure consisting of several levels of 
abstraction. The most concrete level consists of 
names which enter into an index whenever present in 
a story. This level primarily contains names of 
corporations, industries, corporate executives, 
government officials, and geographical locations. 
In order to keep linguistic and the application 
dependent concepts independent, pointers between 
the lowest level of the concept base and the 
lexicon are used. A change to the concept base or 
substitution of a new one will not affect the 
linguistic component. 
Representations at the next level reflect 
con~onality which the elements at the first level 
share and together they provlde desi~ators for a 
story. The objects at this more abstract level are 
called entity types and they further characterize 
the members of the first level. Two common entity 
types are industry type and company. The semantic 
processor can assign an industry designator to a 
story if either the industry is explicitly 
mentioned in the story or if companies or 
individuals mentioned in the story are related to a 
particular industry. So a news item about Swiss 
Air will have both the name Swiss Air and its 
associated industry, viz., Airline Industry, 
assigned to its index. 
The last and most abstract level is that of a 
general concept such as merger/acquisition, 
currency report, strikes and lockouts, and 
terrorism. These are represented by frames where 
there is one action slot and at least one entity 
type slot (determined from the previous level). 
Moreover, one concept may have several different 
representations. 1~e action slot is a list of one 
or more synonomous words or phrases that denote an 
action or the "doing" component of a concept. The 
members of the action slot are not semantic 
primitives but are actual words. Furthermore, they 
are word stems and not all of their morphological 
variants. The entity type slots contain types of 
entities which are found in the previously 
discussed level of the concept base. For example, 
a partial representation for merger/acquisition is: 
(i) Merger/Acquisition 
Action: buy, take over 
Agent: company 
Object: company 
where b~ or take over is the action and the entity 
type slots are labeled agent and object and their 
members must be of the type company. Details of 
this formalism are discussed below in connection 
with the #-relation interpreter. 
The other module of the semantic processor is 
a #-relation interpreter which maps #-relations of 
each sentence of a news story into the concept 
base, or, in other words, onto specific concepts 
and designators. This mapping is executed as 
follows. First, recall that the parser returns a 
set of licens'ing relations including #°relations 
for each story. Each member of this set is a llst 
of the relations for a sentence of the story. In 
examining the #-relations for a sentence, the 
interpreter attempts to establish general concepts 
by pairing the predicate and arbalests of a 
353 
#-relation with the action and entity type slots of 
a concept, respectively. For example, consider a 
merger/acquisition frame (i) and a g-relation which 
has boh~h_~t as a predicate with its agent being Acme 
Co_~. and its object as Software Inc. 
The 0-relatlon interpreter first determines that 
is related to ~ and that ~ is a member 
of the action slot. Since this comparison is 
successful, the interpreter then derives the entity 
types of Acme ~ and Software Inc from the 
abbreviations ~ and ~ Both have an entity 
type of company, and the interpreter can match the 
argument structure of the #-relation with the 
entity type slots of (I), resulting in a merger/ 
acquisition classification being assigned to the 
story. 
In attempting to determine a general 
categorization, the interpreter is encountering 
specific company names and, perhaps, their 
associated industry names. If these are contained 
in the concept base, they are also entered into the 
index. In this hypothetical example, if Software 
Inc. was listed in the concept base and related to 
the computer industry, then independent of the 
general classification, the final index wou\]d 
contain both the name of the company and its 
industry. In this way, a user can specify a 
particular company and receive all stories 
mentioning it, although there may not be any 
further index. 
Since the mapping of the interpreter between 
the #-relations of the parser and the concepts in 
the concept base is structure preserving, the items 
within indexes can also exhibit certain 
relationships. Arg~llents which are either an agent 
or object in a @-relation will correspond to 
entity slots marked agent and object in a concept, 
respectively. Thus, the index will reflect the 
roles in which the participants are engaged, e.g., 
in a merger/acqulsitlon the buyer and the acquired 
could be distinguished. 
The next section provides several examples. 
4.0 Examp__~ 
This section illustrates the type of indexes 
which NAS produces. The stories are from Reuters 
and the results are actual outputs from NAS. 
Story__!l 
Montreal, Nov 3 Air Canada's 8,500 
groundworkers plan rotating strikes in the next few 
days following a collapse in contract talks with 
the government-owned airline earlier today, a union 
spokesman said. 
Chief union negotiator Ron Fontaine said the 
workers will give 24 hours notice of a walkout but 
only two hours notice of which airports or 
maintenance centres they will strike. 
The airline has warned that it will lock out 
any workers participating in rotating strikes until 
a new contract agreement is reached. The union 
last went on strike in 1978, shutting down the 
airline for two weeks. 
Indexes: 
Strikes and Lockouts 
Industry - Airlines 
The system has the concepts of strikes and 
lockouts and airlines industry in its concept base. 
The designator Airlines Industry is arrived at by a 
relation between Air Canada and its industry. The 
more general notion of Strikes and Lockouts appears 
as a frame in the concept base of the form: 
(2) Strikes and Lockouts 
Action: plan, participate 
Agent: employee 
Object: strike 
where the action slot consists of Rlan and 
i~ and the agent slot is of type employee 
of which Kroundworkers is so marked. The word 
strike is simply marked as strike. The parser 
returns a #-relation for the first sentence with 
as a predicate, grQundworkers as the agent, 
and strikes as the object. The g-interpreter 
operates as described in the previous section and 
the Strikes and Lockouts frame is satisfied. Other 
typical results of processing by NAS are stories 2 
and 3. Only the first sentence of each are 
provided since the remaining sentences of these 
news reports did not add any new information to the 
index. 
StorM_/2 
Valley Forge, Pa, November 3 o Alco Standard 
Corp. said it sold two of its gift and glassware 
companies for an undisclosed amount of cash to 
management groups in leveraged huyouts. 
Indexes: 
Divestment 
Industry - Giftware 
Trade - Glass 
Story.3 
Kuwait, November 3 - A booby-trapped car bomb 
exploded in Kuwait City on Tuesday morning, the 
official Kuwait news agency Knna report. 
Indexes: 
Terrorism 
Location - Kuwait 
Instrument - Bombings 
Since the details of indexing are identical to 
those above, they will be omitted here. However, 
it is noteworthy to indieat@ that the word 
divestment does not appear anywhere in Story 2. 
(Clearly, the verb sold alone could not trigger a 
divestment.) Similarly, in Story 3 terrorism is 
never used, yet NAS correctly indexes the story and 
also identifies the location and the weapon or 
instrument used. 
354 
,9. 1_,~~:~ ~ U<~,~t(:! i n~.~ 
')?h~.,:e ~!.re ,qevera! ways in which NAS ca~ fail 
to p~_~:i,~£m an a~ta\].ysi.'.~. %f the seamier i.£::~ds an 
o.~tkuown word, it will tl'Igger procedures in an 
attempt to ~nfer its category. For I.astanee~ it 
~.~i\]% look ahead for abbreviations such as i_~!nq, 
~ozp., o:\[ co. and if any of the striugs are preseut, 
the sca~m.er will a~:sign name features with the 
hmned~ ately preceding unidentified words, 
(%deal.ly, in a fully deployed applieatJou, NAS 
would h:.ve interfaces to specialized databa~:es of 
names, ~;ay, of compsnies. ) Also, the lexleal 
~.;canner, in fail:Lug to find a word In the lexicon 
~l!~d ~-~ t.\]~ a}~su\[tce of t:ertain triggeft\] (e.g., iloe), 
~.y~\[\].\] \]o\[el the unknown word a noun and pass the 
word to the parser in the sentence. This method 
for hand.ilng unknown words works well only if 
verbs, z~dj natives, and prepositions used in news 
reports are nearly exhaustively contained in the 
lexicen~ and NAS has been extremely successful by 
using Eh is technique. 
Another potent:ial problem for NAS is an 
~lleomplei:e or incorrect parse. Both eases often 
indicat:e insufficient info 13nat ion of a lexica\] 
item. }~owever, during execution of NAS, if the 
parser c~nnot final a licensing relationship for a 
projeatilm of an item in its input ~,;tream, it will\[ 
move t:o the next word. This projection will remain 
tmlieen.,:ed o~. uninterpreted. \]:f the word has a 
semantic mark that may trigger a designator, the 
semantic processor will use it for constructing an 
index, \].'or example, Yen is a low-level designator 
word and it is also semantically marked as 
currency .. If the parser cannot license a 
projection c~entaining this word to a verb or a 
preposition o~', perhaps, misassign~; a relation, the 
index wJT.\], still contain ~9\[~ and currency report. 
What may be missing is a general, cat~.gorization, 
In add.iticn to ext:end\]ng and enhancing the 
eomponenL~ oi the se~llalltic processor and parser, 
~he lleaY t~erm nilerrs will focus on establishing 
quantitative benchma~lks for both speed and accuracy 
t~sing stories from an active newswire. While a 
pre-protatype o_~ NAS with a different and 
\]ess- .qoph \[stlcated scanner and less-developed 
parsei? a~d semantic processor relied on stories 
f\[:om flol,py disks or manual entry, the current 
versioll \[s linked te a live feed. A rough 
performance measure of the pre~prototype on a very 
.~al\[l sample of less than 50 sterles showed that it 
<,~as eompletel 2 correct for over 70% of the stories. 
The pres~nt semantic processor operates on a 
much i_a~ get conceptual base and while it is 
premattzz',~ to make assessments, the system has 
indexed (~le day of news stor.le.~ front Reuters and 
the results were independently examined by a group 
of p~efe~slonal indexers. The indexers who had 
manually l~dexed the stories snpplied over 400 
topics for inclusion in the concept base of NAS, 
some of which were OOt relewmt to any of the 
~'4to~':i.e:~. The:re was no communication with these 
indexers before or during tile process and while 
there ,:;ei:e "no formal criteria previously specified, 
the inde~;:e ~s found the results very promising. 
Gui'y.'ent\].y, a precise evaluation Iaetrie for NAS Is 
bc~ing formulated with these indexers. 
Long-term work will include ~nha~lceme.nt to the 
semantic proeessor and a refinement of its 
classlfic~tion scheme. Inferencing across 
classified stories is also an option as we\]\]. as the 
eapabl lity of allowing the user to query those 
processed stori~s (using the same p~r s;or) • 
Automatin stumnarization of stories is also a fut:ur~ 
possibility. 

J\] ,0 Acknowledgf~\[mp~t~s 
Steve Gushing made va\]uabl.~ ~;omments o~ a~ 
earlier draft of this p~per. Oa~ Su!\].ivau was ~ 
co-developer and implementer of th¢~ pre-prototype. 
On the present version of NAS, Steve Gander ha;; 
made significant contributions to its des:\[gll and 
implementation. 

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