Learning Correlations between Linguistic Indicators and Semantic Constraints: Reuse of Context-Dependent Descriptions of Entities

Learning Correlations between Linguistic Indicators and Semantic
Constraints:
Reuse of Context-Dependent Descriptions of Entities
Dragomir R. Radev
Department of Computer Science
Columbia University
New York, NY 10027
radev@cs.columbia.edu

Abstract
This paper presents the results of a study on the
semantic constraints imposed on lexical choice
by certain contextual indicators. We show how
such indicators are computed and how correlations
between them and the choice of a noun
phrase description of a named entity can be automatically
established using supervised learning.
Based on this correlation, we have developed
a technique for automatic lexical choice of
descriptions of entities in text generation. We
discuss the underlying relationship between the
pragmatics of choosing an appropriate description
that serves a specific purpose in the automatically
generated text and the semantics of
the description itself. We present our work in
the framework of the more general concept of
reuse of linguistic structures that are automatically
extracted from large corpora. We present
a formal evaluation of our approach and we conclude
with some thoughts on potential applications
of our method.
1 Introduction
Human writers constantly make deliberate decisions
about picking a particular way of expressing
a certain concept. These decisions are made
based on the topic of the text and the effect that
the writer wants to achieve. Such contextual
and pragmatic constraints are obvious to experienced
writers who produce context-specific
text without much effort. However, in order for
a computer to produce text in a similar way,
either these constraints have to be added manually
by an expert or the system must be able
to acquire them in an automatic way.
An example related to the lexical choice of
an appropriate nominal description of a person
should make the above clear. Even though it
seems intuitive that Bill Clinton should always
be described with the NP "U. S. president" or a
variation thereof, it turns out that many other
descriptions appear in on-line news stories that
characterize him in light of the topic of the article.
For example, an article from 1996 on elections
uses "Bill Clinton, the democratic presidential
candidate", while a 1997 article on a
false bomb alert in Little Rock, Ark. uses "Bill
Clinton, an Arkansas native".
This paper presents the results of a study of
the correlation between named entities (people,
places, or organizations) and noun phrases used
to describe them in a corpus.
Intuitively, the use of a description is based on
a deliberate decision on the part of the author
of a piece of text. A writer is likely to select a
description that puts the entity in the context
of the rest of the article.
It is known that the distribution of words in
a document is related to its topic (Salton and
McGill, 1983). We have developed related techniques
for approximating pragmatic constraints
using words that appear in the immediate context
of the entity.
We will show that context influences the
choice of a description, as do several other linguistic
indicators. Each of the indicators by itself
doesn't provide enough empirical data that
distinguishes among all descriptions that are related
to a an entity. However, a carefully selected
combination of such indicators provides
enough information in order pick an appropriate
description with more than 80% accuracy.
Section 2 describes how we can automatically
obtain enough constraints on the usage of descriptions.
In Section 3, we show how such constructions
are related to language reuse.
In Section 4 we describe our experimental
setup and the algorithms that we have designed.
Section 5 includes a description of our results.
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In Section 6 we discuss some possible extensions
to our study and we provide some thoughts
about possible uses of our framework.
2 Problem Description
Let's define the relation DescriptionOf(E) to
be the one between a named entity E and a
noun phrase, D, describing the named entity.
In the example shown in Table 1, there are two
entity-description pairs.
DescriptionOf ("Tareq Aziz") = "Iraq's
Deputy Prime Minister"
DescriptionOf ("Richard Butler") = "Chief
U.N. arms inspector"
Chief U.N. arms inspector Richard Butler
met Iraq's Deputy Prime Minister Tareq Aziz
Monday after rejecting Iraqi attempts to set
deadlines for finishing his work.
Figure 1: Sample sentence containing two
entity-description pairs.
Each entity appearing in a text can have multiple
descriptions (up to several dozen) associated
with it.
We call the set of all descriptions related to
the same entity in a corpus, a profile of that
entity. Profiles for a large number of entities
were compiled using our earlier system, PROFILE
(Radev and McKeown, 1997). It turns
out that there is a large variety in the size of
the profile (number of distinct descriptions) for
different entities. Table 1 shows a subset of the
profile for Ung Huot, the former foreign minister
of Cambodia, who was elected prime minister at
some point of time during the run of our experiment.
A few sample semantic features of the
descriptions in Table 1 are shown as separate
columns.
We used information extraction techniques to
collect entities and descriptions from a corpus
and analyzed their lexical and semantic properties.
We have processed 178 MB 1 of newswire
and analyzed the use of descriptions related
to 11,504 entities. Even though PROFILE extracts
other entities in addition to people (e.g.,
1The corpus contains 19,473 news stories that cover
the period October 1, 1997 - January 9, 1998 that were
available through PROFILE.
places and organizations), we have restricted
our analysis to names of people only. We claim,
however, that a large portion of our findings relate
to the other types of entities as well.
We have investigated 35,206 tuples, consisting
of an entity, a description, an article ID,
and the position (sentence number) in the article
in which the entity-description pair occurs.
Since there are 11,504 distinct entities, we had
on average 3.06 distinct descriptions per entity
(DDPE). Table 2 shows the distribution of
DDPE values across the corpus. Notice that a
large number of entities (9,053 out of the 11,504)
have a single description. These are not as interesting
for our analysis as the remaining 2,451
entities that have DDPE values between 2 and
24.
10' : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : !F!il i!i i i!i!i
i if! ii !iiii!i
'o' a!!!!!~!~iii!iiii!!~i!!!~!i:i i!~i!~!!i~!~i!iiii~ii~.!.!: i~iiiiiiii~i!~!i!iiiii~ii~l! !!~i!i!iiii ~!i ]i
,o ° ,o' ,o'
X - Number of d~inct ~l~crlpl~ne per ent Ily (DDPE)
Figure 2: Number of distinct descriptions per
entity (log-log scale)
3 Language Reuse in Text
Generation
Text generation usually involves lexical choice -
that is, choosing one way of referring to an entity
over another. Lexical choice refers to a variety
of decisions that have to made in text generation.
For example, picking one among several
equivalent (or nearly equivalent) constructions
is a form of lexical choice (e.g., "The Utah Jazz
handed the Boston Celtics a de fear' vs. "The
Utah Jazz defeated the Boston Celtics" (Robin,
1994)). We are interested in a different aspect
of the problem: namely learning the rules that
can be used for automatically selecting an appropriate
description of an entity in a specific
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Description
a senior member
Cambodia's
Cambodian foreign minister
co-premier
first prime minister
foreign minister
His Excellency
Mr.
new co-premier
new first prime minister
newly-appointed first prime minister
premier
prime minister
addressing
X
Semantic categories
country male new political post
X
X
X
Table 1: Profile of Ung Huot
cou at
~6
12
lO
8
2
3
8
9
10
11
12
13
14
9,053
1,481
472
182
112
74
31
X
X
X
seniority
X
X
X
X
X
X
X
X
X
X
DDPE ~ 4
16
17
18
19
24
Table 2: Number of distinct descriptions per entity (DDPE).
context.
To be feasible and scaleable, a technique for
solving a particular case of the problem of lexicai
choice must involve automated learning. It
is also useful if the technique can specify enough
constraints on the text to be generated so that
the number of possible surface realizations that
match the semantic constraints is reduced significantly.
The easiest case in which lexical
choice can be made is when the full surface
structure can be used, and when it has been automatically
extracted from a corpus. Of course,
the constraints on the use of the structure in the
generated text have to be reasonably similar to
the ones in the source text.
We have found that a natural application for
the analysis of entity-description pairs is language
reuse, which includes techniques of extracting
shallow structure from a corpus and
applying that structure to computer-generated
texts.
Language reuse involves two components: a
source text written by a human and a target
text, that is to be automatically generated by
a computer, partially making use of structures
reused from the source text. The source text
is the one from which particular surface structures
are extracted automatically, along with
the appropriate syntactic, semantic, and pragmatic
constraints under which they are used.
Some examples of language reuse include collocation
analysis (Smadja, 1993), the use of
entire factual sentences extracted from corpora
(e.g., "'Toy Story' is the Academy Award
winning animated film developed by Pixar~'),
and summarization using sentence extraction
(Paice, 1990; Kupiec et al., 1995). In the case
of summarization through sentence extraction,
the target text has the additional property of
being a subtext of the source text. Other techniques
that can be broadly categorized as language
reuse are learning relations from on-line
texts (Mitchell, 1997) and answering natural
language questions using an on-line encyclopedia
(Kupiec, 1993).
Stydying the concept of language reuse is rewarding
because it allows generation systems to
leverage on texts written by humans and their
deliberate choice of words, facts, structure.
We mentioned that for language reuse to take
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place, the generation system has to use the same
surface structure in the same syntactic, semantic,
and pragmatic context as the source text
from which it was extracted. Obviously, all of
this information is typically not available to a
generation system. There are some special cases
in which most of it can be automatically computed.
Descriptions of entities are a particular instance
of a surface structure that can be reused
relatively easily. Syntactic constraints related
to the use of descriptions are modest - since descriptions
are always noun phrases that appear
as either pre-modifiers or appositions 2, they are
quite flexibly usable in any generated text in
which an entity can be modified with an appropriate
description. We will show in the rest
of the paper how the requisite semantic (i.e.,
"what is the meaning of the description to pick")
and pragmatic constraints (i.e., "what purpose
does using the description achieve ?') can be extracted
automatically.
Given a profile like the one shown in Table 1,
and an appropriate set of semantic constraints
(columns 2-7 of the table), the generation component
needs to perform a profile lookup and
select a row (description) that satisfies most or
all semantic constraints. For example, if the semantic
constraints specify that the description
has to include the country and the political position
of Ung Huot, the most appropriate description
is "Cambodian foreign minister".
4 Experimental Setup
In our experiments, we have used two widely
available tools - WordNet and Ripper.
WordNet (Miller et al., 1990) is an on-line
hierarchical lexical database which contains semantic
information about English words (including
hypernymy relations which we use in
our system). We use chains of hypernyms when
we need to approximate the usage of a particular
word in a description using its ancestor and
sibling nodes in WordNet. Particularly useful
for our application are the synset offsets of the
words in a description. The synset offset is a
number that uniquely identifies a concept node
(synset) in the WordNet hierarchy. Figure 3
shows that the synset offset for the concept "administrator,
decision maker" is "(07063507}',
2We haven't included relative clauses in our study.
while its hypernym, "head, chief, top dog" has
a synset offset of "~07311393} ".
Ripper (Cohen, 1995) is an algorithm that
learns rules from example tuples in a relation.
Attributes in the tuples can be integers (e.g.,
length of an article, in words), sets (e.g., semantic
features), or bags (e.g., words that appear
in a sentence or document). We use Ripper
to learn rules that correlate context and
other linguistic indicators with the semantics
of the description being extracted and subsequently
reused. It is important to notice that
Ripper is designed to learn rules that classify
data into atomic classes (e.g., "good", "average",
and "bad"). We had to modify its algorithm
in order to classify data into sets of
atoms. For example, a rule can have the form
"if CONDITION then [( 07063762} { 02864326}
{ 0001795~}] "3 . This rule states that if a certain
"CONDITION" (which is a function of the indicators
related to the description) is met, then
the description is likely to contain words that
are semantically related to the three WordNet
nodes [{07063762} {02864326} {00017954}].
The stages of our experiments are described
in detail in the remainder of this section.
4.1 Semantic tagging of descriptions
Our system, PROFILE, processes WWWaccessible
newswire on a round-the-clock basis
and extracts entities (people, places, and organizations)
along with related descriptions. The
extraction grammar, developed in CREP (Duford,
1993), covers a variety of pre-modifier and
appositional noun phrases.
For each word wi in a description, we use a
version of WordNet to extract the synset offset
of the immediate parent of wi.
4.2 Finding linguistic cues
Initially, we were interested in discovering rules
manually and then validating them using the
learning algorithm. However, the task proved
(nearly) impossible considering the sheer size
of the corpus. One possible rule that we hypothesized
and wanted to verify empirically at
this stage was parallelism. This linguisticallymotivated
rule states that in a sentence with
a parallel structure (consider, for instance, the
3These offsets correspond to the WordNet nodes
"manager", "internetn, and "group"
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DIRECTOR: {07063762} director, manager, managing director
=~ {07063507} administrator, decision maker
=~ {07311393} head, chief, top dog
=~ {06950891} leader
=~ {00004123} person, individual, someone, somebody, mortal, human, soul
=~ {00002086} life form, organism, being, living thing
=~ {00001740} entity, something
Figure 3: Hypernym chain of "director" in WordNet, showing synset offsets.
sentence fragment "... Alija Izetbegovic, a Muslim,
Kresimir Zubak, a Croat, and Momcilo
Krajisnik, a Serb... ") all entities involved have
similar descriptions. However, rules at such a
detailed syntactic level take too long to process
on a 180 MB corpus and, further, no more than
a handful of such rules can be discovered manually.
As a result, we made a decision to extract
all indicators automatically. We would also like
to note that using syntactic information on such
a large corpus doesn't appear particularly feasible.
We limited therefore our investigation
to lexicai, semantic, and contextual indicators
only. The following subsection describes the attributes
used.
4.3 Extracting linguistic cues
automatically
The list of indicators that we use in our system
are the following:
• Context: (using a window of size 4, excluding
the actual description used, but
not the entity itself) - e.g., "['clinton'
'clinton' 'counsel' 'counsel' 'decision' 'decision'
'gore' 'gore' 'ind' 'ind' 'index' 'news'
'november' 'wednesday']" is a bag of words
found near the description of Bill Clinton
in the training corpus.
• Length of the article: - an integer.
• Name of the entity: - e.g., "Bill Clinton".
• Profile: The entire profile related to a person
(all descriptions of that person that are
found in the training corpus).
• Synset Offsets: - the WordNet node numbers
of all words (and their parents)) that
appear in the profile associated with the
entity that we want to describe.
4.4 Applying machine learning method
To learn rules, we ran Ripper on 90% (10,353)
of the entities in the entire corpus. We kept the
remaining 10% (or 1,151 entities) for evaluation.
Sample rules discovered by the system are
shown in Table 3.
5 Results and Evaluation
We have performed a standard evaluation of the
precision and recall that our system achieves in
selecting a description. Table 4 shows our results
under two sets of parameters.
Precision and recall are based on how well the
system predicts a set of semantic constraints.
Precision (or P) is defined to be the number of
matches divided by the number of elements in
the predicted set. Recall (or R) is the number
of matches divided by the number of elements
in the correct set. If, for example, the system
predicts [A] [B] [C], but the set of constraints
on the actual description is [B] [D], we would
compute that P = 33.3% and R --- 50.0%. Table
4 reports the average values of P and R for
all training examples 4.
Selecting appropriate descriptions based on
our algorithm is feasible even though the values
of precision and recall obtained may seem
only moderately high. The reason for this is
that the problem that we axe trying to solve is
underspecified. That is, in the same context,
more than one description can be potentially
used. Mutually interchangeable descriptions include
synonyms and near synonyms ("leader"
vs. "chief) or pairs of descriptions of different
generality (U.S. president vs. president). This
4We run Ripper in a so-called "noise-free mode",
which causes the condition parts of the rules it discovers
to be mutually exclusive and therefore, the values of P
and R on the training data are both 100~.
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Rule Decision
IF CONTEXT - inflation {09613349} (politician)
IF PROFILES " detective AND CONTEXT " agency {07485319} (policeman)
IF CONTEXT - celine {07032298} (north_american)
Table 3" Sample rules discovered by the system.
Training set size
500
1,000
2,000
5,000
10,000
15,000
20,000
25,000
30,000
50,000
word nodes only
Precision Recall
64.29% 2.86%
71.43% 2.86%
42.86% 40.71%
59.33% 48.40%
69.72% 45.04%
76.24% 44.02%
76.25% 49.91%
83.37% 52.26%
80.14% 50.55%
83.13% 58.54%
word and parent nodes
Precision
78.57%
85.71%
67.86%
64.67%
74.44%
73.39%
79.08%
82.39%
82.77%
88.87%
Recall
2.86%
2.86%
62.14%
53.73%
59.32%
53.17%
58.70%
57.49%
57.66%
63.39%
Table 4: Values for precision and recall using word nodes only (left) and both word and parent
nodes (right).
type of evaluation requires the availability of human
judges.
There are two parts to the evaluation: how
well does the system performs in selecting semantic
features (WordNet nodes) and how well
it works in constraining the choice of a description.
To select a description, our system does a
lookup in the profile for a possible description
that satisfies most semantic constraints (e.g., we
select a row in Table 1 based on constraints on
the columns).
Our system depends crucially on the multiple
components that we use. For example, the shallow
CREP grammar that is used in extracting
entities and descriptions often fails to extract
good descriptions, mostly due to incorrect PP
attachment. We have also had problems from
the part-of-speech tagger and, as a result, we
occasionally incorrectly extract word sequences
that do not represent descriptions.
6 Applications and Future Work
We should note that PROFILE is part of a
large system for information retrieval and summarization
of news through information extraction
and symbolic text generation (McKeown
and Radev, 1995). We intend to use PROFILE
to improve lexical choice in the summary generation
component, especially when producing
user-centered summaries or summary updates
(Radev and McKeown, 1998 to appear). There
are two particularly appealing cases - (1) when
the extraction component has failed to extract a
description and (2) when the user model (user's
interests, knowledge of the entity and personal
preferences for sources of information and for either
conciseness or verbosity) dictates that a description
should be used even when one doesn't
appear in the texts being summarized.
A second potentially interesting application
involves using the data and rules extracted by
PROFILE for language regeneration. In (Radev
and McKeown, 1998 to appear) we show how
the conversion of extracted descriptions into
components of a generation grammar allows for
flexible (re)generation of new descriptions that
don't appear in the source text. For example,
a description can be replaced by a more general
one, two descriptions can be combined to form
a single one, or one long description can be deconstructed
into its components, some of which
can be reused as new descriptions.
We are also interested in investigating another
idea - that of predicting the use of a description
of an entity even when the corresponding
profile doesn't contain any description at all,
or when it contains only descriptions that contain
words that are not directly related to the
words predicted by the rules of PROFILE. In
this case, if the system predicts a semantic cat-
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egory that doesn't match any of the descriptions
in a specific profile, two things can be done: (1)
if there is a single description in the profile, to
pick that one, and (2) if there is more than one
description, pick the one whose semantic vector
is closest to the predicted semantic vector.
Finally, the profile extractor will be used as
part of a large-scale, automatically generated
Who's who site which will be accessible both
by users through a Web interface and by NLP
systems through a client-server API.
7 Conclusion
In this paper, we showed that context and other
linguistic indicators correlate with the choice
of a particular noun phrase to describe an entity.
Using machine learning techniques from a
very large corpus, we automatically extracted
a large set of rules that predict the choice of a
description out of an entity profile. We showed
that high-precision automatic prediction of an
appropriate description in a specific context is
possible.
8 Acknowledgments
This material is based upon work supported by
the National Science Foundation under Grants
No. IRI-96-19124, IRI-96-18797, and CDA-96-
25374, as well as a grant from Columbia University's
Strategic Initiative Fund sponsored by
the Provost's Office. Any opinions, findings,
and conclusions or recommendations expressed
in this material are those of the author(s) and
do not necessarily reflect the views of the National
Science Foundation.
The author is grateful to the following
people for their comments and suggestions:
Kathy McKeown, Vasileios Hatzivassiloglou,
and Hongyan Jing.
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