Demos - Sabine Buchholz
Shallow parsing is a useful preprocessing step for many Natural
Language Processing applications. Sentences are then no longer just
sequences of words, but receive some structure: groups of words that
closely belong together are marked, specific relations between (groups
of) words are found. In contrast to full parsing, shallow parsing does
not attempt to find a structure comprising the whole
sentence. Therefore, it is in general much faster.
The Memory-Based Shallow Parser (MBSP) applies several modules to an
English sentence supplied by the user. It first assigns a
Part-of-Speech to each word in the sentence (see MBT). In a next step
MBSP recognises chunks (non-overlapping, non-embedded
constituents). Finally, MBSP assigns subjects and objects to the verbal
chunks in the sentence. MBSP is trained on the Wall Street Journal
(WSJ) treebank, a link to more recent WSJ material is included.