  This paper proposes a corpus-based language model for topic identification.
We analyze the association of noun-noun and noun-verb pairs in LOB Corpus. The
word association norms are based on three factors: 1) word importance, 2) pair
co-occurrence, and 3) distance. They are trained on the paragraph and sentence
levels for noun-noun and noun-verb pairs, respectively. Under the topic
coherence postulation, the nouns that have the strongest connectivities with
the other nouns and verbs in the discourse form the preferred topic set. The
collocational semantics then is used to identify the topics from paragraphs and
to discuss the topic shift phenomenon among paragraphs.
