Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lexical Normalisation of Twitter Data   Twitter with over 500 million users globally, generates over 100,000 tweets
per minute . The 140 character limit per tweet, perhaps unintentionally,
encourages users to use shorthand notations and to strip spellings to their
bare minimum "syllables" or elisions e.g. "srsly". The analysis of twitter
messages which typically contain misspellings, elisions, and grammatical
errors, poses a challenge to established Natural Language Processing (NLP)
tools which are generally designed with the assumption that the data conforms
to the basic grammatical structure commonly used in English language. In order
to make sense of Twitter messages it is necessary to first transform them into
a canonical form, consistent with the dictionary or grammar. This process,
performed at the level of individual tokens ("words"), is called lexical
normalisation. This paper investigates various techniques for lexical
normalisation of Twitter data and presents the findings as the techniques are
applied to process raw data from Twitter.

9
The categories of this paper are: nlin
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys