Several options arise to thoroughly represent s. First, we find it useful to consider the semantic representation of the affirmative counterpart: AGENT(the cow, ate), THEME(grass, ate), and INSTRUMENT(With a fork, ate).

Table 2 depicts five different possible semantic representations .

It corresponds to the semantic representation of the affirmative counterpart after applying the pseudo-relation NOT over the focus of the negation.

In this paper, we present a novel way to semantically represent negation using focus detection.

The main contributions are: (l) interpretation of negation using focus detection; (2) focus of negation annotation over all PropBank negated sen-tencesl; (3) feature set to detect the focus of negation; and (4) model to semantically represent negation and reveal its underlying positive meaning.

In tackling this challenging learning problem, we introduce a new semantic representation which highlights a parallel between dependency syntax and efficient evaluation of logical forms.

Our system is based on a new semantic representation , DCS, which offers a simple and expressive alternative to lambda calculus.

A major focus of this work is on our semantic representation , DCS, which offers a new perspective on compositional semantics.

The main technical contribution of this work is a new semantic representation , dependency-based compositional semantics (DCS), which is both simple and expressive (Section 2).