Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Minimal Span-Based Neural Constituency Parser   In this work, we present a minimal neural model for constituency parsing
based on independent scoring of labels and spans. We show that this model is
not only compatible with classical dynamic programming techniques, but also
admits a novel greedy top-down inference algorithm based on recursive
partitioning of the input. We demonstrate empirically that both prediction
schemes are competitive with recent work, and when combined with basic
extensions to the scoring model are capable of achieving state-of-the-art
single-model performance on the Penn Treebank (91.79 F1) and strong performance
on the French Treebank (82.23 F1).

23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio