Maytag: A multi-staged approach to identifying
complex events in textual data
Conrad Chang, Lisa Ferro, John Gibson, Janet Hitzeman, Suzi Lubar, Justin Palmer,
Sean Munson, Marc Vilain, and Benjamin Wellner
The MITRE Corporation
202 Burlington Rd.
Bedford, MA 01730 USA
contact: mbv@mitre.org (Vilain)
Abstract
We present a novel application of NLP
and text mining to the analysis of finan-
cial documents. In particular, we de-
scribe an implemented prototype, May-
tag, which combines information extrac-
tion and subject classification tools in an
interactive exploratory framework. We
present experimental results on their per-
formance, as tailored to the financial do-
main, and some forward-looking exten-
sions to the approach that enables users
to specify classifications on the fly.
1 Introduction
Our goal is to support the discovery of complex
events in text. By complex events, we mean
events that might be structured out of multiple
occurrences of other events, or that might occur
over a span of time. In financial analysis, the
domain that concerns us here, an example of
what we mean is the problem of understanding
corporate acquisition practices. To gauge a
company’s modus operandi in acquiring other
companies, it isn’t enough to know just that an
acquisition occurred, but it may also be impor-
tant to understand the degree to which it was
debt-leveraged, or whether it was performed
through reciprocal stock exchanges.
In other words, complex events are often
composed of multiple facets beyond the basic
event itself. One of our concerns is therefore to
enable end users to access complex events
through a combination of their possible facets.
Another key characteristic of rich domains
like financial analysis, is that facts and events are
subject to interpretation in context. To a finan-
cial analyst, it makes a difference whether a
multi-million-dollar loss occurs in the context of
recurring operations (a potentially chronic prob-
lem), or in the context of a one-time event, such
as a merger or layoff. A second concern is thus
to enable end users to interpret facts and events
through automated context assessment.
The route we have taken towards this end is to
model the domain of corporate finance through
an interactive suite of language processing tools.
Maytag, our prototype, makes the following
novel contribution. Rather than trying to model
complex events monolithically, we provide a
range of multi-purpose information extraction
and text classification methods, and allow the
end user to combine these interactively. Think
of it as Boolean queries where the query terms
are not keywords but extracted facts, events, en-
tities, and contextual text classifications.
2 The Maytag prototype
Figure 1, below, shows the Maytag prototype
in action. In this instance, the user is browsing a
particular document in the collection, the 2003
securities filings for 3M Corporation. The user
has imposed a context of interpretation by select-
ing the “Legal matters” subject code, which
causes the browser to only retrieve those portions
of the document that were statistically identified
as pertaining to law suits. The user has also se-
lected retrieval based on extracted facts, in this
case monetary expenses greater than $10 million.
This in turn causes the browser to further restrict
retrieval to those portions of the document that
contain the appropriate linguistic expressions,
e.g., “$73 million pre-tax charge.”
As the figure shows, the granularity of these
operations in our browser is that of the para-
graph, which strikes a reasonable compromise
between providing enough context to interpret
retrieval results, but not too much. It is also ef-
131
fective at enabling combination of query terms.
Whereas the original document contains 5161
paragraphs, the number of these that were tagged
with the “Legal matters” code is 27, or .5 percent
of the overall document. Likewise, the query for
expenses greater than $10 million restricts the
return set to 26 paragraphs (.5 percent). The
conjunction of both queries yields a common
intersection of only 4 paragraphs, thus precisely
targeting .07 percent of the overall document.
Under the hood, Maytag consists of both an
on-line component and an off-line one. The on-
line part is a web-based GUI that is connected to
a relational database via CGI scripts (html,
JavaScript, and Python). The off-line part of the
system hosts the bulk of the linguistic and statis-
tical processing that creates document meta-data:
name tagging, relationship extraction, subject
identification, and the like. These processes are
applied to documents entering the text collection,
and the results are stored as meta-data tables.
The tables link the results of the off-line process-
ing to the paragraphs in which they were found,
thereby supporting the kind of extraction- and
classification-based retrieval shown in Figure 1.
3 Extraction in Maytag
As is common practice, Maytag approaches
extraction in stages. We begin with atomic
named entities, and then detect structured
entities, relationships, and events. To do so, we
rely on both rule-based and statistical means.
3.1 Named entities
In Maytag, we currently extract named entities
with a tried-but-true rule-based tagger based on
the legacy Alembic system (Vilain, 1999). Al-
though we’ve also developed more modern sta-
tistical methods (Burger et al, 1999, Wellner &
Vilain, 2006), we do not currently have adequate
amounts of hand-marked financial data to train
these systems. We therefore found it more con-
venient to adapt the Alembic name tagger by
manual hill climbing. Because this tagger was
originally designed for a similar newswire task,
we were able to make the port using relatively
small amounts of training data. We relied on two
100+ page-long Securities filings (singly anno-
tated), one for training, and the other for test, on
which we achieve an accuracy of F=94.
We found several characteristics of our finan-
cial data to be especially challenging. The first is
the widespread presence of company name look-
alikes, by which we mean phrases like “Health
Care Markets” or “Business Services” that may
look like company names, but in fact denote
business segments or the like. To circumvent
this, we had to explicitly model non-names, in
effect creating a business segment tagger that
captures company name look-alikes and prevents
them from being tagged as companies.
Another challenging characteristic of these fi-
nancial reports is their length, commonly reach-
ing hundreds of pages. This poses a quandary
Figure 1: The Maytag interface
132
for the way we handle discourse effects. As with
most name taggers, we keep a “found names” list
to compensate for the fact that a name may not
be clearly identified throughout the entire span of
the input text. This list allows the tagger to
propagate a name from clear identifying contexts
to non-identified occurrences elsewhere in the
discourse. In newswire, this strategy boosts re-
call at very little cost to precision, but the sheer
length of financial reports creates a dispropor-
tionate opportunity for found name lists to intro-
duce precision errors, and then propagate them.
3.2 Structured entities, relations, and events
Another way in which financial writing differs
from general news stories is the prevalence of
what we’ve called structured entities, i.e., name-
like entities that have key structural attributes.
The most common of these relate to money. In
financial writing, one doesn’t simply talk of
money: one talks of a loss, gain or expense, of
the business purpose associated therewith, and of
the time period in which it is incurred. Consider:
Worldwide expenses for environmental
compliance [were] $163 million in 2003.
To capture such cases as this, we’ve defined a
repertoire of structured entities. Fine-grained
distinctions about money are encoded as color of
money entities, with such attributes as their color
(in this case, an operating expense), time stamp,
and so forth. We also have structured entities for
expressions of stock shares, assets, and debt.
Finally, we’ve included a number of constructs
that are more properly understood as relations
(job title) or events (acquisitions).
3.3 Statistical training
Because we had no existing methods to address
financial events or relations, we took this oppor-
tunity to develop a trainable approach. Recent
work has begun to address relation and event
extraction through trainable means, chiefly SVM
classification (Zelenko et al, 2003, Zhou et al,
2005). The approach we’ve used here is classi-
fier-based as well, but relies on maximum en-
tropy modeling instead.
Most trainable approaches to event extraction
are entity-anchored: given a pair of relevant enti-
ties (e.g., a pair of companies), the object of the
endeavor is to identify the relation that holds be-
tween them (e.g., acquisition or subsidiary). We
turn this around: starting with the head of the
relation, we try to find the entities that fill its
constituent roles. This is, unavoidably, a
strongly lexicalized approach. To detect an
event such as a merger or acquisition, we start
from indicative head words, e.g., “acquire,”
“purchases,” “acquisition,” and the like.
The process proceeds in two stages. Once
we’ve scanned a text to find instances of our in-
dicator heads, we classify the heads to determine
whether their embedding sentence represents a
valid instance of the target concept. In the case
of acquisitions, this filtering stage eliminates
such non-acquisitions as the use of the word
“purchases” in “the company purchases raw ma-
terials.” If a head passes this filter, we find the
fillers of its constituent roles through a second
classification stage
The role stage uses a shallow parser to chunk
the sentence, and considers the nominal chunks
and named entities as candidate role fillers. For
acquisition events, for example, these roles in-
clude the object of the acquisition, the buying
agent, the bought assets, the date of acquisition,
and so forth (a total of six roles). E.g.
In the fourth quarter of 2000 (WHEN), 3M
[AGENT] also acquired the multi-layer inte-
grated circuit packaging line [ASSETS] of
W.L. Gore and Associates [OBJECT].
The maximum entropy role classifier relies on
a range of feature types: the semantic type of the
phrase (for named entities), the phrase vocabu-
lary, the distance to the target head, and local
context (words and phrases).
Our initial evaluation of this approach has
given us encouraging first results. Based on a
hand-annotated corpus of acquisition events,
we’ve measured filtering performance at F=79,
and role assignment at F=84 for the critical case
of the object role. A more recent round of ex-
periments has produced considerably higher per-
formance, which we will report on later this year.
4 Subject Classification
Financial events with similar descriptions can
mean different things depending on where these
events appear in a document or in what context
they appear. We attempt to extract this important
contextual information using text classification
methods. We also use text classification methods
to help users to more quickly focus on an area
where interesting transactions exist in an interac-
tive environment. Specifically, we classify each
paragraph in our document collection into one of
several interested financial areas. Examples in-
clude: Accounting Rule Change, Acquisitions
and Mergers, Debt, Derivatives, Legal, etc.
133
4.1 Experiments
In our experiments, we picked 3 corporate an-
nual reports as the training and test document set.
Paragraphs from these 3 documents, which are
from 50 to 150 pages long, were annotated with
the types of financial transactions they are most
related to. Paragraphs that did not fall into a
category of interest were classified as “other”.
The annotated paragraphs were divided into ran-
dom 4x4 test/training splits for this test. The
“other” category, due to its size, was sub-
sampled to the size of the next-largest category.
As in the work of Nigam et al (2002) or Lodhi
et al (2002), we performed a series of experi-
ments using maximum entropy and support vec-
tor machines. Besides including the words that
appeared in the paragraphs as features, we also
experimented with adding named entity expres-
sions (money, date, location, and organization),
removal of stop words, and stemming. In gen-
eral, each of these variations resulted in little dif-
ference compared with the baseline features con-
sisting of only the words in the paragraphs.
Overall results ranged from F-measures of 70-75
for more frequent categories down to above 30-
40 for categories appearing less frequently.
4.2 Online Learning
We have embedded our text classification
method into an online learning framework that
allows users to select text segments, specify
categories for those segments and subsequently
receive automatically classified paragraphs simi-
lar to those already identified. The highest con-
fidence paragraphs, as determined by the classi-
fier, are presented to the user for verification and
possible re-classification.
Figure 1, at the start of this paper, shows the
way this is implemented in the Maytag interface.
Checkboxes labeled pos and neg are provided
next to each displayed paragraph: by selecting
one or the other of these checkboxes, users indi-
cate whether the paragraph is to be treated as a
positive or a negative example of the category
they are elaborating. In our preliminary studies,
we were able to achieve the peak performance
(the highest F1 score) within the first 20 training
examples using 4 different categories.
5 Discussion and future work
The ability to combine a range of analytic
processing tools, and the ability to explore their
results interactively are the backbone of our ap-
proach. In this paper, we’ve covered the frame-
work of our Maytag prototype, and have looked
under its hood at our extraction and classification
methods, especially as they apply to financial
texts. Much new work is in the offing.
Many experiments are in progress now to as-
sess performance on other text types (financial
news), and to pin down performance on a wider
range of events, relations, and structured entities.
Another question we would like to address is
how best to manage the interaction between clas-
sification and extraction: a mutual feedback
process may well exist here.
We are also concerned with supporting finan-
cial analysis across multiple documents. This
has implications in the area of cross-document
coreference, and is also leading us to investigate
visual ways to define queries that go beyond the
paragraph and span many texts over many years.
Finally, we are hoping to conduct user studies
to validate our fundamental assumption. Indeed,
this work presupposes that interactive application
of multi-purpose classification and extraction
techniques can model complex events as well as
monolithic extraction tools à la MUC.
Acknowledgements
This research was performed under a MITRE
Corporation sponsored research project.
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