Backend: SVC

The SVC that implements Linear Support Vector Classification. It is based on LinearSVC in scikit-learn, which in turn is based on liblinear. This kind of algorithm is well suited for multiclass (but not multilabel) classification, for example classifying documents with the Dewey Decimal Classification or the 20 Newsgroups classification, which is used for the examples below. It requires relatively little training data. It is suitable for classifications of up to around 10,000 classes.

Note that SVC cannot handle more than one class/subject per document during training. If a document in the training corpus has more than one subject, a random one of the subjects is picked and a warning is shown.

See also the Annif-tutorial exercise about classification using SVC.

Example configuration

[svc-en]
name=SVC English
language=en
backend=svc
analyzer=snowball(english)
limit=100
vocab=20news

Backend-specific parameters

The parameters are:

Parameter	Description
limit	Maximum number of results to return
min_df	How many documents a word must appear in to be considered. Default: 1
ngram	maximum length of word n-grams (default: 1)

The min_df parameter controls the features (words/tokens) used to build the model. With the default setting of 1, all the words in the training set will be used, even ones that appear in only one training document. With a higher value such as 5, only those that appear in at least that many documents are included. Increasing the min_df value will decrease the size and training time of the model.

The ngram parameter controls the use of word n-grams. With the default setting (1), only single words are considered. Setting this to 2 will use word bigrams, which may increase accuracy, but the model will be much larger.

Usage

Load a vocabulary:

annif load-vocab 20news --language en /path/to/Annif-corpora/fulltext/20news/20news-vocab.tsv

Train the model:

annif train svc-en /path/to/Annif-corpora/fulltext/20news/20news-train.tsv

Test the model with a single document:

cat document.txt | annif suggest svc-en

Evaluate a files in short text document corpus format:

annif eval svc-en /path/to/Annif-corpora/fulltext/20news/20news-test.tsv