Machine learning based named entity recognizer.
NamedEntityContextTagger is used by @see StochasticTermSegmenter to help in segmenting sentences in which no term separators such as spaces are used.
train(mixed $text_files, string $term_tag_separator = "/", float $learning_rate = 0.1, integer $num_epoch = 1200, \seekquarry\yioop\library\function $term_callback = null, \seekquarry\yioop\library\function $tag_callback = null)
Uses text files containing sentences to create a matrix so that from a two chars before a term, two chars after a char context, together with a two tags before a term context and a term, the odds that a named entity as been found can be calculated
| mixed | $text_files | with training data. These can be a file or an array of file names. |
| string | $term_tag_separator | separator used to separate term and tag for terms in input sentence |
| float | $learning_rate | learning rate when cycling over data trying to minimize the cross-entropy loss in the prediction of the tag of the middle term. |
| integer | $num_epoch | number of times to cycle through the complete data set. Default value of 1200 seems to avoid overfitting |
| \seekquarry\yioop\library\function | $term_callback | callback function applied to a term before adding term to sentence term array as part of processing and training with a sentence. |
| \seekquarry\yioop\library\function | $tag_callback | callback function applied to a part of speech tag before adding tag to sentence tag array as part of processing and training with a sentence. |
predict(mixed $sentence) : array
Predicts named entities that exists in a sentence.
| mixed | $sentence | is an array of segmented words/terms or a string that will be split on white space |
all predicted named entities together with a tag indicating kind of named entity ex. [["郑振铎","nr"],["国民党","nt"]]