ON REPRESENTING GOVERNED PREPOSITIONS AND 
HANDLING "INCORRECT" AND NOVEL PREPOSITIONS 
Hatte R. Blejer, Sharon Flank, and Andrew Kchler 
SRA Corporation 
2000 15th St. North 
Arlington, VA 22201, USA 
ABSTRACT 
NLP systems, in order to be robust, 
must handle novel and ill-formed input. 
One common type of error involves the use 
of non-standard prepositions to mark 
arguments. In this paper, we argue that 
such errors can be handled in a systematic 
fashion, and that a system designed to 
handle them offers other advantages. We 
offer a classification scheme for 
preposition usage errors. Further, we show 
how the knowledge representation 
employed in the SRA NLP system 
facilitates handling these data. 
1.0 INTRODUCTION 
It is well known that NLP systems, 
in order to be robust, must handle ill- 
formed input. One common type of error 
involves the use of non-standard 
prepositions to mark arguments. In this 
paper, we argue that such errors can be 
handled in a systematic fashion, and that a 
system designed to handle them offers 
other advantages. 
The examples of non-standard 
prepositions we present in the paper are 
taken from colloquial language, both 
written and oral. The type of error these 
examples represent is quite frequent in 
colloquial written language. The frequency 
of such examples rises sharply in evolving 
sub-languages and in oral colloquial 
language. In developing an NLP system to 
be used by various U.S. government 
customers, we have been sensitized to the 
need to handle variation and innovation in 
preposition usage. Handling this type of 
variation or innovation is part of our 
overall capability to handle novel 
predicates, which arc frequent in sub- 
language. Novel predicates created for sub- 
languages arc less "stable" in how they mark 
arguments (ARGUMENT MAPPING) than 
general English "core" predicates which 
speakers learn as children. It can be 
expected that the eventual advent of 
successful speech understanding systems 
will further emphasize the need to handle 
this and other variation. 
The NLP system under development 
at SRA incorporates a Natural Language 
Knowledge Base (NLKB), a major part of 
which consists of objects representing 
SEMANTIC PREDICATE CLASSES. The 
system uses hierarchical knowledge sources; 
all general "class-level" characteristics of a 
semantic predicate class, including the 
number, type, and marking of their 
arguments, are put in the NLKB. This 
leads to increased efficiency in a number 
of system aspects, e.g., the lexicon is more 
compact and easier to modify since it only 
contains idiosyncratic information. This 
representation allows us to distinguish 
between Icxically and semantically 
determined ARGUIVIENT MAPPING and to 
formulate general class-level constraint 
relaxation mechanisms. 
I.I CLASSIFYING PREPOSITION 
USAGE 
Preposition usage in English in 
positions governed by predicating elements, 
whether adjectival, verbal, or nominal, may 
be classified as (I) lexically determined, (2) 
syntactically determined, or (3) 
semantically determined. Examples are: 
LEXICALLY DETERMINED: 
laugh at, afraid of 
SYNTACTICALLY DETERMINED: 
by in passive sentences 
SEMANTICALLY DETERMINED: 
move to~from 
Preposition usage in idiomatic phrases is 
also considered to be lexically determined, 
e.g., ~ respect to. 
1.2 A TYPOLOGY OF ERRORS IN 
PREPOSITION USAGE 
We have classified our corpus of 
examples of the use of non-standard 
110 
prepositions into the following categories: 
(1) substitution of a semantically 
appropriate preposition -- either from the 
same class or another -- for a semantically 
determined one, (2) substitution of a 
semantically appropriate preposition for a 
lexically determined one, (3) false starts, 
(4) blends, and (5) substitution of a 
semantically appropriate preposition for a 
syntactically determined one. A small 
percentage of the non-standard use of 
prepositions appears to be random. 
1.3 COMPUTATIONAL APPLICATIONS 
OF THIS WORK 
In a theoretical linguistics forum 
(Blejcr and Flank 1988), we argued that 
these examples of the use of non-standard 
prepositions to mark arguments (1) 
represent the kind of principled variation 
that underlies language change, and (2) 
support a semantic analysis of government 
that utilizes thematic roles, citing other 
evidence for the semantic basis of 
prepositional case marking from studies of 
language dysfunction (Aitchison 1987:103), 
language acquisition (Pinker 1982:678; 
Mcnyuk 1969:56), and typological, cross- 
linguistic studies on case-marking systems. 
More theoretical aspects of our work 
(including diachroni¢ change and 
arguments for and against particular 
linguistic theories) were covered in that 
paper; here we concentrate on issues of 
interest to a computational linguistics 
forum. First, our natural language 
knowledge representation and processing 
strategies take into account the semantic 
basis of prepositional case marking, and 
thus facilitate handling non-standard and 
novel use of prepositions to mark 
arguments. The second contribution is our 
typology of errors in preposition usage. We 
claim that an NLP system which accepts 
naturally occurring input must recognize 
the type of the error to know how to 
compensate for it. Furthermore, the 
knowledge representation scheme we have 
implemented is an efficient representation 
for English and lends itself to adaptation to 
representing non-English case-marking as 
well. 
There is wide variation in 
computational strategies for mapping from 
the actual natural language expression to 
some sort of PREDICATE-ARGUMENT 
representation. At issue is how the system 
recognizes the arguments of the predicate. 
At one end of the spectrum is an approach 
which allows any marking of arguments if 
the type of the argument is correct for that 
predicate. This approach is inadequate 
because it ignores vital information carried 
by the preposition. At the other extreme is 
a semantically constrained syntactic parse, 
in many ways a highly desirable strategy. 
This latter method, however, constrains 
more strictly than what humans actually 
produce and understand. Our strategy has 
been to use the latter method, allowing 
relaxation of those constraints, under 
certain well-specified circumstances. 
Constraint relaxation has been 
recognized as a viable strategy for 
handling ill-formed input. Most discussion 
centers around orthographic errors and 
errors in subject-verb agreement. Jensen, 
Heidorn, Miller, and Ravin (1983:158) note 
the importance of "relaxing restrictions in 
the grammar rules in some principled way." 
Knowing which constraints to relax and 
avoiding a proliferation of incorrect parses 
however, is a non-trivial task. Weischedel 
and Sondheimer .(1983:163ff) offer 
cautionary advice on this subject. 
There has been some discussion of 
errors similar to those cited in our paper. 
Carbonell and Hayes (1983:132) observed 
that "problems created by the absence of 
expected case markers can be overcome by 
the application of domain knowledge" using 
case frame instantiation. We agree with 
these authors that the use of domain 
knowledge is an important element in 
understanding ill-formed input. However, 
in instances where the preposition is not 
omitted, but rather replaced by a non- 
standard preposition, we claim that an 
understanding of the linguistic principles 
involved in the substitution is necessary. 
To explain how constraint 
relaxation is accomplished, a brief system 
description is needed. Our system uses a 
parser based on Tomita (1986), with 
modifications to allow constraints and 
structure-building. It uses context-free 
phrase structure rules, augmented with 
morphological, contextual, and semantic 
constraints. Application of the phrase 
structure rules results in a parse tree, 
similar to a Lexical-Functional Grammar 
(LFG) "c-structure" (Bresnan 1982). The 
constraints are unified at parse time to 
produce a functionally labelled template 
(FLT). The FLT is then input to a semantic 
translation module. Using ARGUMENT 
111 
MAPPING rules and other operator- 
operand semantic rules, semantic 
translation creates situation frames (SF). 
SFs consist of a predicate and entity frames 
(EF), whose semantic roles in the situation 
are labeled. Other semantic objects are 
relational frames (e.g. prepositional 
phrases), property frames (e.g. adjective 
phrases), and unit frames (measure phrases). 
During the semantic interpretation and 
discourse analysis phase, the situation 
frame is interpreted, resulting in one or 
more instantiated knowledge base (KB) 
objects, which are state or event 
descriptions with entity participants. 
2.0 REPRESENTING ARGUMENT 
MAPPING IN AN NLP SYSTEM 
In our lexicons, verbs and adjectives 
are linked to one or more predicate classes 
which are defined in the Natural Language 
Knowledge Base (NLKB). Predicates 
typically govern one or more arguments or 
thematic roles. All general, class-level 
information about the thematic roles which 
a given predicate governs is represented at 
the highest possible level. Only 
idiosyncratic information is represented in 
the lexicon. When lexicons are loaded the 
idiosyncratic information in the lexicon is 
unified with the general information in the 
NLKB. Our representation scheme has 
certain implementational advantages: 
lexicons are less error-prone and easier to 
modify, the data are more compact, 
constraint relaxation is facilitated, etc. 
More importantly, we claim that such 
semantic classes are psychologically valid. 
Our representation scheme is based 
on the principle that ARGUMENT 
MAPPING is generally determined at the 
class-level, i.e., predicates group along 
semantic lines as to the type of 
ARGUMENT MAPPING they take. Our 
work draws from theoretical linguistic 
studies of thematic relations (e.g., Gruber 
1976, Jackendoff 1983, and Ostler 1980). 
We do not accept the "strong" version of 
localism, i.e., that all form mirrors function 
-- that ARGUMENT MAPPING classes 
arise from metaphors based on spatial 
relations. Unlike case grammar, we limit 
the number of cases or roles to a small set, 
based on how they are manifested in 
surface syntax. We subsequently "interpret" 
roles based on the semantic class of the 
predicate, e.g., the GOAL of an ATTITUDE 
is generally an animate "experiencer'. 
For example, in the NLKB the 
ARGUMENT MAPPING of predicates 
which denote a change in spatial relation 
specifies a GOAL argument, marked with 
prepositions which posit a GOAL relation (to, into, 
and onto) and a SOURCE 
argument, marked with prepositions which 
posit a SOURCE relation (from, out of, off 
of). A sub-class of these predicates, namely 
Vendler's (1967) achievements, mark the 
GOAL argument with prepositions which 
posit an OVERLAP relation (at, in). 
Compare: 
MOVE to/into/onto 
from/out of/off of 
ARRIVE at/in 
from 
The entries for these verbs in SRA's lexicon 
merely specify which semantic class they 
belong to (e.g., SPATIAL-RELATION), 
whether they are stative or dynamic, 
whether they allow an agent, and whether 
they denote an achievement. Their 
ARGUMENT MAPPING is not entered 
explicitly in the lexicon. The verb reach, 
on the other hand, which marks its GOAL 
idiosyncratically, as a direct object, would 
have this fact in its lexical entry. 
2.1 GROUPING SEMANTIC ROLES 
Both on implementational and on 
theoretical grounds, we have grouped 
certain semantic roles into superclasses. 
Such groupings arc common in the 
literature on case and valency (see Somers 
1987) and are also supported by cross- 
linguistic evidence. Our grouping of roles 
follows previous work. For example, the 
AGENT SUPERCLASS covers both animate 
agents as well as inanimate instruments. A 
GROUND SUPERCLASS (as discussed in 
Talmy 1985) includes both SOURCE and 
GOAL, and a GOAL SUPERCLASS 
includes GOAL, PURPOSE, an'd 
DIRECTION. 
Certain semantic roles, like GOAL 
and SOURCE, as well as being sisters are 
"privatives", that is, opposites semantically. 
Our representation scheme 
differentiates between lexically and 
semantically determined prepositions. We 
will show how this representation 
facilitates recognition of the type of error, 
and therefore principled relaxation of the 
constraints. Furthermore, a principled 
112 
relaxation of the constraints depends in 
many instances on knowing the relationship 
between the non-standard and the expected 
prepositions: are they sisters, privatives, or 
is the non-standard preposition a parent of 
the expected preposition. 
In the following section we present 
examples of the five types of preposition 
usage errors. In the subsequent section, we 
discuss how our system presently handles 
these errors, or how it might eventually 
handle them. 
3.0 THE DATA 
We have classified the variation 
data according to the type of substitution. 
The main types are: 
(1) semantic for semantic (Section 3.1), 
(2) semantic for lexical (Section 3.2), 
(3) blends (Section 3.3), 
(4) false starts (Section 3.4), and 
(5) semantic for syntactic (Section 3.5). 
The data presented below are a 
representative sample of a larger group of 
examples. The current paper covers the 
classifications which we have encountered 
so far; we expect that analysis of additional 
data will provide further types of 
substitutions within each class. 
3.1 SEMANTIC FOR SEMANTIC 
3.1.1 To/From 
The substitution of the goal marker 
for the source marker cross-linguistically is 
recognized in the case literature (e.g., 
lkegami 1987). In English, this appears to 
be more pronounced in certain regional 
dialects. Common source/goal alternations 
cited by Ikegami (1987:125) include: averse 
from/to, different from/to, immune 
from/to, and distinction from/to. The 
majority of examples involve to 
substituting for from in lexical items which 
incorporate a negation of the predicate; the 
standard marker of GROUND in this class 
of predicates is a SOURCE marker, e.g., 
different from. The "positive" counterparts 
mark the GROUND with GOAL, e.g., 
similar to, as discussed in detail in Gruber 
(1976). Variation between to and from can 
only occur with verbs which incorporate a 
negative, otherwise the semantic distinction 
which these prepositions denote is 
necessary. 
(1) The way that he came on to that bereaved 
brother completely alienated me TO Mr. Bush. 
9/26/88 MCS 
(2) At this moment I'm different TO 
primitive man. 10/12/88 The Mind, PBS 
3.1.2 To/With 
Communication and transfer of 
knowledge can be expressed either as a 
process with multiple, equally involved 
participants, or as an asymmetric process 
with one of the participants as the "agent" 
of the transfer of information. Our data 
document the substitution of the GOAL 
marker for the CO-THEME marker; this 
may reflect the tendency of English to 
prefer "agent" focussing. The participants 
in a COMMUNICATION situation are 
similar in their semantic roles, the only 
difference being one of "viewpoint." By no 
means all communication predicates operate 
in this way: e.g., EXPLANATION, 
TRANSFER OF KNOWLEDGE are more 
clearly asymmetric. The system 
differentiates between "mutual" and 
"asymmetric" communication predicates. 
(3) The only reason they'll chat TO you is, 
you're either pretty, or they need something 
from your husband. 9/30/88 MCS 
(4) 171 have to sit down and explore this TO 
you. 10/16/88 
3.2 SEMANTIC FOR LEXICAL 
3.2.1 Goal Superclass (Goal/ 
Purpose/Direction) 
Goal and purpose are frequently 
expressed by the same case-marking, with 
the DIRECTION marker alternating with 
these at times. The standard preposition in 
these examples is lexically determined. In 
example (6), instead of the lexically 
determined to, which also marks the 
semantic role GOAL, another preposition 
within the same superclass is chosen. In 
example (5) the phrasally determined for is 
replaced by the GOAL marker. There is 
abundant cross-linguistic evidence for a 
GOAL SUPERCLASS which includes 
GOAL and PURPOSE; to a lesser extent 
DIRECTION also patterns with these cross- 
linguistically. 
(5) It's changing TO the better. 8/3/88 MCS 
(6) Mr. Raspberry is almost 200 years behind 
Washingtonians aspiring FOR full citizenship. 
10/13/88 WP 
113 
3.2.2 On/Of 
Several examples involve lexical 
items expressing knowledge or cognition, 
for which the standard preposition is 
lexically determined. This preposition is 
uniformly replaced by on, also a marker of 
the semantic role of REFERENT. 
Examples include abreast of, grasp of, an 
idea of, and knowledge of. We claim that 
the association of the role REFERENT 
with knowledge and cognition (as well as 
with transfer-of-information predicates) is 
among the more salient associations that 
language learners encounter. 
(7) Terry Brown, 47, a truck driver, agreed; 
"with eight years in the White House," he said, 
"Bush ought to have a better grasp ON the 
details." 9/27/88 NYT p. B8 
(8) I did get an idea ON the importance of 
consistency as far as reward and penalty are 
concerned. 11/88 ETM journal 
3.2.3 With/From/To 
In this class, we believe that "mutual 
action verbs" such as marry and divorce 
routinely show a CO-THEME marker with 
being substituted for either to or from. 
Such predicates have a SECONDARY- 
MAPPING of PLURAL-THEME in the 
NLKB. Communication predicates are 
another class which allows a PLURAL- 
THEME and show alternation of GOAL 
and CO-THEME (Section 3.1.2). 
(9) Today Robin Givens said she won't ask 
for any money in her divorce WITH Mike 
Tyson. 10/19/88 ATC 
3.3 FALSE STARTS 
The next set of examples suggests 
that the speaker has "retrieved" a 
preposition from a different ARGUbIENT 
MAPPING for the verb or for a different 
argument than the one which is eventually 
produced. For example, confused with 
replaces confused by in (10), and say to 
replaces say about in (11). Such examples 
are more prevalent in oral language. 
Handling these examples is difficult since 
all sorts of contextual information -- 
linguistic and non-linguistic -- goes into 
detecting the error. 
(10) They didn't want to be confused WITH 
the facts. 11/14/88 DRS 
(11) The memorial service was really well 
done. The rabbi did a good job. What do 
you say TO a kid who died fike that? 
11/14/88 
3.4 BLENDS 
Here, a lexically or phrasally 
determined preposition is replaced by a 
preposition associated with a semantically 
similar lexical item. In (12) Quayle says he 
was smitten about Marilyn, possibly 
thinking of crazy about. In (13) he may be 
thinking of on the subject/topic of. The 
questioner in (14) may have in 
support/favor of in mind. In (15) Quayle 
may have meant we learn by making 
mistakes. In (16), the idiomatic phrase in 
support of is confused with the 
ARGUlVlENT MAPPING of the noun 
support, e.g., "he showed his support for the 
president'. 
(12) I was very smitten ABOUT her... I saw 
a good thing and I responded rather quickly 
and she did too. 10/20/88 WP, p. C8 
(13) ON the area of the federal budget 
deficit .... 10/5/88 Sen. Quayle in vp debate 
(& NYT 10/7/88 p. B6) 
(14) You made one of the most eloquent 
speeches IN behalf of contra aid. 10/5/88 
Questioner in VP debate (& NYT 10/7/88 
p.B6) 
(15) We learn BYour mistakes. 10/5/88 Sen. 
Quayle in vp debate (& NYT 10/7/88 p. 
B6) 
(16) We testified in support FOR medical 
leave. 10/22/88 FFS 
3.5 SEMANTIC FOR SYNTACTIC -- 
WITH/BY 
In the majority of the following 
examples, the syntactically governed by 
marking passives is replaced by WITH. 
This alternation of with and by in passives 
has been attested for hundreds of years, 
and we hypothesize that English may be in 
the process of reinterpreting by, as well as 
replacing it with with in certain contexts. 
On the one hand, by is being reinterpreted 
as a marker of "archetypal" agents, i.e, those 
high on the scale of AGENTIVITY (i.e., 
speaker • human • animate • potent • non- 
animate, non-potent). On the other hand, 
a semantically appropriate marker is being 
114 
substituted for by. 
We analyze the WITH in these 
examples either as the less agentive 
AGENT (namely the INSTRUlVlENT) in 
example (18), or the less agentive CO- 
THEME in example (17). The substitutions 
are semantically appropriate and the 
substitutes are semantically related to 
AGENT. • 
(17) All of Russian Hfe was accompanied 
WITH some kind of singing. 8/5/88 ATC 
(18) Audiences here are especially enthused 
WITH Dukakis's description of the 
Reagan-Bush economic policies. 11/5/88 ATC 
4.0 THE COMPUTATIONAL 
IMPLEMENTATION 
Of the five types of errors cited in 
Section 3, substitutions of semantic for 
semantic (Section 3.1), semantic for lexical 
(Section 3.2), and semantic for syntactic 
(Section 3.5) are the simplest to handle 
computationally. 
4.1 SEMANTIC FOR SEMANTIC OR 
LEXICAL 
The representation scheme 
described above (Section 2) facilitates 
handling the semantic for semantic and 
semantic for lexical substitutions. 
Semantic for semantic substitutions 
are allowed if 
(i) the predicate belongs to the 
communication class and the standard CO- 
THEME marker is replaced by a GOAL 
marker, or 
(ii) the predicate incorporates a negative 
and GOAL is substituted for a standard 
SOURCE, or vice versa. 
Semantic for lexical substitutions 
are allowed if 
(iii) the non-standard preposition is a non- 
privative sister of the standard preposition 
(e.g., in the GOAL SUPERCLASS), 
(iv) "the non-standard preposition is the 
NLKB-inherited, "default" preposition for 
the predicate (e.g., REFERENT for 
predicates of cognition and knowledge), or 
(v) in the NLKB the predicate allows a 
SECONDARY-MAPPING of PLURAL- 
THElvIE (e.g., marital predicates as in the 
divorce with example). 
Handling the use of a non-standard 
preposition marking an argument crucially 
involves "type-checking', wherein the "type" 
of the noun phrase is checked, e.g. for 
membership in an NLKB class such as 
animate-creature, time, etc. Type-checking 
is also used to narrow the possible senses of 
the preposition in a prepositional phrase, 
as well as to prefer certain modifier 
attachments. 
Prepositional phrases can have two 
relations to predicating expressions, i.e., a 
governed argument (PREP-ARG) or an 
ADJUNCT. During parsing, the system 
accesses the ARGUMENT MAPPING for 
the predicate; once the preposition is 
recognized as the standard marker of an 
argument, an ADJUNCT reading is 
disallowed. The rule for PREP-ARG is a 
separate rule in the grammar. When the 
preposition does not match the expected 
preposition, the system checks whether any 
of the above conditions (i-v) hold; if so, the 
parse is accepted, but is assigned a lower 
likelihood. If a parse of the PP as an 
ADJUNCT is also accepted, it will be 
preferred over the ill-formed PREP-ARG. 
4.2 SEMANTIC FOR SYNTACTIC 
The substitution of semantic 
marking for syntactic (WITH for BY) is 
easily handled: during semantic mapping 
by phrases in the ADJUNCTS are mapped 
to the role of the active subject, assuming 
that "type checking" allows that 
interpretation of the noun phrase. It is also 
possible for such a sentence to be 
ambiguous, e.g., "he was seated by the 
man'. We treat with phrases similarly, 
except that ambiguity between CO-THEME 
and PASSIVE SUBJECT is not allowed, 
based on our observation that with for by 
is used for noun phrases low on the 
animacy scale. Thus, only the CO-THEME 
interpretation is valid if the noun phrase is 
animate. 
4.3 FALSE STARTS AND BLENDS 
False starts are more difficult, 
requiring an approach similar to that of 
case grammar. In these examples, the 
preposition is acceptable with the verb, but 
not to mark that particular argument. The 
115 
type of the argument marked with the 
"incorrect" preposition must be quite 
inconsistent with that sense of the 
predicate for the error even to be noticed, 
since the preposition is acceptable with 
some other sense. We are assessing the 
frequency of false starts in the various 
genres in which our system is being used, 
to determine whether we need to implement 
a strategy to handle these examples. We 
predict that future systems for 
understanding spoken language will need to 
accomodate this phenomenon. 
We do not handle blends currently. 
They involve a form of analogy, i.e., 
smitten is like mad, syntactically, 
semantically, and even stylistically; they 
may shed some light on language storage 
and retrieval. Recognizing the similarity in 
order to allow a principled handling seems 
very difficult. 
In addition, blends may provide 
evidence for a "top down" language 
production strategy, in which the argument 
structure is determined before the lexieai 
items are chosen/inserted. Our data 
suggest that some people may be more 
prone to making this type of error than are 
others. Finally, blends are more frequent 
in genres in which people attempt to use a 
style that they do not command (e.g., 
student papers, radio talk shows). 
5.0 DIRECTIONS FOR FUTURE WORK 
In this paper we have described a 
frequent type of ill-formed input which 
NLP systems must handle, involving the use 
of non-standard prepositions to mark 
arguments. We presented a classification of 
these errors and described our algorithm 
for handling some of these error types. The 
importance of handling such non-standard 
input will increase as speech recognition 
becomes more reliable, because spoken 
input is less formal. 
In the near term, planned 
enhancements include adjusting the 
weighting scheme to more accurately 
reflect the empirical data. A frequency- 
based model of preposition usage, based on 
a much larger and broader sampling of text 
will improve system handling of those 
errors. 
ACKNOWLEDGEMENTS 
We would like to express our 
appreciation of our colleagues' 
contributions to the SRA NLP system: 
Gayle Aycrs, Andrew FanG, Ben Fine, 
Karyn German, Mary Dee Harris, David 
Reel, and Robert M. Simmons. 
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26. Talmy, Leonard. 1985. Lexicalization 
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Forms. In Shopen III:57-149. 
27. Tomita, Masuru. 1986. Efficient Parsing 
for Natural Language. Kluwer, Boston. 
28. Vendler, Zeno. 1967. Linguistics in 
Philosophy. Cornell University Press, 
Ithaca. 
29. Weischedel, Ralph and Norman 
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Processing Ill-Formed Input. American 
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4):161-177. 
APPENDIX A. DATA SOURCES 
ATC: National Public Radio news 
program, "All Things Considered" 
ME: National Public Radio news 
program, "Morning Edition" 
WE: National Public Radio news 
program, "Weekend Edition" 
MCS: WAMU radio, Washington D.C., 
interview program, "The Mike Cuthbert 
Show" 
DRS: WAMU radio, Washington D.C., 
interview program, "Diane Rehm Show" 
FFS: WAMU radio, Washington D.C., 
interview program, "Fred Fiske Saturday" 
AIH: Canadian Broadcasting Company 
radio news program, "As It Happens" 
NYT: The New York Times 
WP: The Washington Post 
ETM_: Student journal for "Effective 
Teaching Methods," a junior undergraduate 
course 
117 
