Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Combining Temporal Information and Topic Modeling for Cross-Document
  Event Ordering   Building unified timelines from a collection of written news articles
requires cross-document event coreference resolution and temporal relation
extraction. In this paper we present an approach event coreference resolution
according to: a) similar temporal information, and b) similar semantic
arguments. Temporal information is detected using an automatic temporal
information system (TIPSem), while semantic information is represented by means
of LDA Topic Modeling. The evaluation of our approach shows that it obtains the
highest Micro-average F-score results in the SemEval2015 Task 4: TimeLine:
Cross-Document Event Ordering (25.36\% for TrackB, 23.15\% for SubtrackB), with
an improvement of up to 6\% in comparison to the other systems. However, our
experiment also showed some draw-backs in the Topic Modeling approach that
degrades performance of the system.

24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat