Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL   This paper presents a simple unsupervised learning algorithm for recognizing
synonyms, based on statistical data acquired by querying a Web search engine.
The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and
Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR
is empirically evaluated using 80 synonym test questions from the Test of
English as a Foreign Language (TOEFL) and 50 synonym test questions from a
collection of tests for students of English as a Second Language (ESL). On both
tests, the algorithm obtains a score of 74%. PMI-IR is contrasted with Latent
Semantic Analysis (LSA), which achieves a score of 64% on the same 80 TOEFL
questions. The paper discusses potential applications of the new unsupervised
learning algorithm and some implications of the results for LSA and LSI (Latent
Semantic Indexing).

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The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph