![](bg2.png)
ney demonstrated that their NATLOG system, an en-
tailment verifier based on NLog, makes surprisingly
accurate judgments on FraCaS test instances,
2
but it
can only verify the given entailment; one has to spec-
ify both the premise and the conclusion (MacCartney
and Manning, 2008). Moreover,inferences are limited
to single premise sentences and have to result from
“aligning” the premise with the hypothesis and then
judging whether a sequence of “edits” (substitutions,
insertions, deletions) leading from the premise to the
hypothesis makes it likely that the premise entails the
hypothesis.Hence NATLOG can verify the correctness
of the entailment
Jimmy Dean refused to move without his jeans
James Dean didn’t dance without pants
,
but it would not be able, for example, to use a second
premise, ‘Jimmy Dean could not find his jeans’ to con-
clude that ‘Jimmy Dean did not dance’. (Assume that
not being able to do something entails not doing it,
and not finding something entails not having it.)
We show that Episodic Logic (EL), a very nat-
ural representation of human language, has the po-
tential to overcome the inherent shallowness of the
NLog scheme. To demonstrate this potential, we sup-
ply EL axioms, meta-axioms, and inference rules to
EPILOG, a general EL reasoner that has been shown to
hold its own in scalable first-order reasoning against
the best current FOL theorem provers, even though
its natural language–like expressive devices go well
beyond FOL. It has been used to solve problems
in self-aware and commonsense reasoning and some
challenge problems in theorem proving (Morbini and
Schubert, 2007; Morbini and Schubert, 2008; Schu-
bert and Hwang, 2000). Once a sentence is in EL
form, we only need a KB that contains axioms and
inference rules specifying what conclusions can be
drawn from predicates with particular signatures. The
result is a reasoning system that can not only handle
the dual-premise example above but can also perform
general logical reasoning not directly related to nat-
ural language. We point out the benefits of our ap-
proach over ones based only on NLog or FOL—and
also provide an evaluation on 108 sentences randomly
sampled from the Brown corpus—in Section 4.
2 PREVIOUS WORK
In the linguistics community,a tremendous amount of
effort has been invested in the study of presupposition,
implicativity, and polarity. We do not intend to cover
all the subtleties involved in this field of study, but we
2
See MacCartney’s site http://www-nlp.stanford.edu/
∼wcmac/downloads/
Table 1: The typical behavior of E, P, and I.
E P I
Project from embeddings no yes no
Cancelable when embedded – yes –
Cancelable when unembedded no no yes
give a brief discussion of the aspects directly relevant
to our work.
The Strawsonian definition of presupposition (rel-
evant to factives and antifactives) is
One sentence presupposes another iff when-
ever the first is true or false, the second is true.
This provides a nice logical characterization that cov-
ers the case of lexically “triggered” presuppositions—
in particular, the polarity-independentexistence of the
presupposed content (Strawson, 1952). As we will see
in Section 3, this rules out an axiomatic approach to
presupposition inference.
Other important aspects of implicativity and pre-
supposition are cancelability and projection. The im-
plications of an implicative such as ‘refuse’ can be
canceled in a negative context (‘John didn’t refuse to
fight, but simply had no occasion to fight’), and do
not survive an embedding (‘John probably refused to
fight’). In contrast, a presupposition typically cannot
be canceled (#‘John doesn’t know that he snores, and
in fact he doesn’t’), and typically projects when em-
bedded (‘John probably knows that he snores’), but
not in all cases (‘I said to Mary that John knows that
he snores’). The typical behavior of entailments (E),
presuppositions (P), and implicatures (I) are summa-
rized in Table 1 (Beaver and Geurts, 2011). A notable
attempt to regulate presupposition projection is the
classification of embedding constructions into plugs,
filters, and holes (Karttunen, 1973). Plugs (e.g., ‘say’
above) block all projections, filters (e.g., ‘if–then’)
allow only certain ones, and holes (e.g., ‘probably’
above) allow all.
There have also been many efforts to computation-
ally process these linguistic phenomena. They tend to
focus on handling monotonicity properties of quanti-
fiers and other argument-taking lexical items, which
ultimately determine the polarity of arbitrarily embed-
ded constituents. For instance, (Nairn et al., 2006) pro-
posed a polarity propagationalgorithm that accommo-
dates entailment and contradiction in linguistically-
based representations. MacCartney and Manning’s
NATLOG and its success on FraCaS examples showed
the potential effectiveness of a NLog-based system
that leverages these linguistic properties (MacCart-
ney and Manning, 2008). (Clausen and Manning,
2009) further showed how to project presupposi-
tions in NLog in accord with the plug–hole–filter
EPISODIC LOGIC: NATURAL LOGIC + REASONING
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