sequor 0.3.0
AUTHOR: Grzegorz Chrupała <gchrupala@lsv.uni-saarland.de>
Sequor is a sequence labeler based on Collins's sequence
perceptron. Sequor has a flexible feature template language and is
meant mainly for NLP applications such as Part of Speech tagging,
syntactic chunking or Named Entity labeling.
This version of Sequor includes SemiNER, a named-entity labeler, with
pre-trained models for German. For details see ./lib/seminer/README
INSTALLATION
See installation instructions: http://code.google.com/p/sequor/wiki/INSTALL
USAGE
With Sequor you can learn a model from sequences manually annotated
with labels, and then apply this model to new data in order to add
labels. Sequor is meant to be used mainly with linguistic data, for
example to learn Part of Speech tagging, syntactic chunking or Named
Entity labeling.
Usage: sequor command [OPTION...] [ARG...]
train: train model
train [OPTION...] TEMPLATE-FILE TRAIN-FILE MODEL-FILE
--rate=NUM learning rate
--beam=INT beam size
--iter=INT number of iterations
--min-count=INT minimum feature frequency for label dictionary
--heldout=FILE path to heldout data
--hash use hashing instead of feature dictionary
--hash-sample=INT sample size to estimate number of features when hashing
--hash-max-size=INT maximum size of parameter vector when hashing
predict: predict using model
predict MODEL-FILE
version: print version
version
help: print usage information
help
Data files should be in the UTF-8 encoding.
As an example we can use data annotated with syntactic chunk labels in
the data directory. For example:
./bin/sequor train data/all.features data/train.conll model\
--rate 0.1 --beam 10 --iter 5 --min-count 50 --hash\
--heldout data/devel.conll
./bin/sequor predict model < data/test.conll > data/test.labels
FEATURE TEMPLATE SYNTAX
Sequor uses a small language for specifying feature templates to use
when learing. This section gives an informal overview of this
language. Sequor uses the simple CoNLL format for the input files. In
this format sentences are separated by blank lines. Each line
represents a single token (word). Each token should have the same
fixed number of space-separated fields, where the last field is the
label, e.g.
der d ART I-NC O
Europäischen europäisch ADJA I-NC ORG
Union Union NN I-NC ORG
The template language treats the input sentence as a matrix of
features (i.e. field values) and allows you to select and apply some
transformations to those features.
The language consists of a number predefined functions. By calling the
functions with certain argument you can specify the feature set to
use. As an example consider the following template:
Cat [ Cell 0 0, Suffix 2 (Cell 0 0), Row -1, Row 1 ].
It specifies the following features: the first field in the current
token, the two-character suffix of the first field of the current
token, all the fields of the previous token and all the fields of the
following token.
Functions:
Cell r c Selects field in row r and column c.
Rect r c r' c Selects all features in the rectangle
whose upper-left corner is in row r column c
and lower-right corner is in row r' column c'.
Row r Selects all features in row r.
MarkNull f If feature does not exist, replace it with a NULL mark
Typically used when absence of feature is significant,
e.g. to mark the beginning of the sentence.
Index f Marks the feature f to use in indexing for label
dictionary.
Cat [f1,f2,...,fn] Selects features in the list.
Cart f f' Creates Cartesian product of feature sets f and f'.
If f and f' are singletons, simply conjoins the
two features.
Lower f Maps f to lower case characters.
Suffix i f Takes suffix of i character length of feature f.
Prefix i f Takes prefix of i character langth of feature f.
WordShape f Creates a specification of which charater classes such
as lower case and upper case letters, digits or
punctuation occur in feature f.
Remarks: Rows are indexed relative to the current token (0). Columns
are indexed starting with 0. Functions which take features as
arguments can be passed either singleton features or sequences of
features. If passed a sequence they are applied to each of its
elements. For example (Suffix 3 (Row 0)) will return the sequence of
features formed by taking the suffix of length 3 of each field of row
0.
For more examples see files all.features and example.features in the
directory data.
