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SNoW is a learning architecture that is tailored for learning in the presence
of a very large number of information sources (features). SNoW learns a
network of linear functions.
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Learning Based Java (LBJ) is a modeling language that expedites the
development of systems with one or more learning components. In an LBJ model,
simple learned components are modeled conditionally, and their initial
predictions are then combined via constrained optimization, yielding an
expressive, globally coherent set of final predictions.
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FEX is a feature extraction package used to provide input to machine learning
algorithms. FEX can be used to generate features from structured text or other
relational data.
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The Semantic Role Labeler identifies the verb-argument structure in a
sentence. Specifically, it labels the sentence with Propbank-style labels.
This tool is a machine-learning based system that uses SNoW and FEX for local
classification decisions, and Integer Linear Programming to make global
inferences about sets of these local decisions.
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This is an implementation of our SNoW-based POS tagger for use with LBJ.
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A classifier that partitions plain text into sequences of semantically related
words, indicating a shallow (i.e., non-hierarchical) phrase structure.
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This is a state of the art NE tagger that tags plain text with named
entitites (people / organizations / locations / miscellaneous). It uses
gazetteers extracted from Wikipedia, word class model derived from unlabeled
text and expressive non-local features. The best performance is 90.8
F1 on the CoNLL03 shared task data.
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An implementation of an unsupervised learning algorithm for rank aggregation with distance-based models.
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This package implements a Lifted First-Order Probabilistic Inference
algorithm.
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SNOW-based Named Entity Tagger. Has been replaced by the LBJ NER tagger
(also available on the download page). The tagger reads plain text and annotates entities with labels
Person, Location, Organization or Misc. Optionally, it can also give more
specific labels using a comprehensive set of lists.
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Identifies the phrase structure in a sentence after being trained on labeled
data.
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The POS tagger makes use of the Sequential Model. This is a model that
facilitates the learning and evaluation of the learned function in cases where
the number of potential targets for each decision is large (in this case,
there are about 50 different Part Of Speech tags).
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An implementation of CoRanker, an algorithm for Named Entity discovery from
multilingual comparable corpora.
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A c++ library of string similarity functions.
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A library of c++ functions that allow you to interact with WordNet.
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A collection of useful general-purpose c++ functions.
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pySNoW is a minimal python interface to the SNoW - Sparse Network of Winnows
learning architecture. It is meant to be faithful to the original command line
interface and provides access to the train, test, evaluate, interactive and
server modes directly from python. pySNoW requires SNoW version 3.2.0.
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A Coreference Resolver, based on LBJ, trained on the ACE corpus.
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This software was used in the research described in the paper,
"Learning to Detect Objects in Images via a Sparse, Part-Based
Representation". It is provided 'as-is', and includes a
README file to orient users. If you use this code or the data
it was designed for, please cite the above work.
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