Github user helenahm commented on the issue:
https://github.com/apache/incubator-hivemall/pull/93
In Hivemall-126:
Max Entropy Classifier (a.k.a. Multi-nominal/Multiclass Logistic
Regression) [1,2] is useful for Text classification.
Max Entropy Classifier is more often used for Part-of-Speech Tagging and
Named Entity Recognition, and some other tasks where context is used as
features. Those are also fundamental tasks of NLP. Even though Text
Classification is a candidate too.
Mohri as his colleagues also put POS task first. As Mohri writes in the
article that is a basis for the implementation I have chosen:
Our first set of experiments were carried out with âmediumâ scale data
sets containing 1M-300Minstances.
These included: English part-of-speech tagging, generated from the Penn
Treebank
[16] using the first character of each part-of-speech tag as output,
sections 2-21 for training, section
23 for testing and a feature representation based on the identity, affixes,
and orthography of the input
word and the words in a window of size two; Sentiment analysis, generated
from a set of
online product, service, and merchant reviews with a three-label output
(positive, negative, neutral),
with a bag of words feature representation; RCV1-v2 as described by [14],
where documents having
multiple labels were included multiple times, once for each label; Acoustic
Speech Data, a 39-
dimensional input consisting of 13 PLP coefficients, plus their first and
second derivatives, and 129
outputs (43 phones à 3 acoustic states); and the Deja News Archive, a text
topic classification
problem generated from a collection of Usenet discussion forums from the
years 1995-2000. For all
text experiments, we used random feature mixing [9, 20] to control the size
of the feature space.
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