  Human-performed annotation of sentences in legal documents is an important
prerequisite to many machine learning based systems supporting legal tasks.
Typically, the annotation is done sequentially, sentence by sentence, which is
often time consuming and, hence, expensive. In this paper, we introduce a
proof-of-concept system for annotating sentences "laterally." The approach is
based on the observation that sentences that are similar in meaning often have
the same label in terms of a particular type system. We use this observation in
allowing annotators to quickly view and annotate sentences that are
semantically similar to a given sentence, across an entire corpus of documents.
Here, we present the interface of the system and empirically evaluate the
approach. The experiments show that lateral annotation has the potential to
make the annotation process quicker and more consistent.

Computation and Language;Artificial Intelligence;Machine Learning