Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sentence Embeddings and High-speed Similarity Search for Fast Computer
  Assisted Annotation of Legal Documents   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.

0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex