Source code for gatenlp.stanfordnlp

"""
Support for using stanfordnlp: convert from stanfordnlp output to gatenlp documents and annotations.
"""
from gatenlp import Document
from gatenlp import utils


[docs]def apply_stanfordnlp(nlp, gatenlpdoc, setname=""): """ Run the stanford nlp pipeline on the gatenlp document and transfer the annotations. This modifies the gatenlp document in place. :param nlp: StanfordNLP pipeline :param gatenlpdoc: gatenlp document :param setname: set to use :return: """ spacydoc = nlp(gatenlpdoc.text) return stanfordnlp2gatenlp(spacydoc, gatenlpdoc=gatenlpdoc, setname=setname)
[docs]def stanfordnlp2gatenlp(stanfordnlpdoc, gatenlpdoc=None, setname="", word_type="Word", sentence_type="Sentence"): """ Convert a StanfordNLP document to a gatenlp document. If a gatenlp document is already provided, add the annotations from the spacy document to it. In this case the original gatenlpdoc is used and gets modified. :param stanfordnlpdoc: a spacy document :param gatenlpdoc: if None, a new gatenlp document is created otherwise this document is added to. :param setname: the annotation set name to which the annotations get added, empty string for the default annotation set. :param token_type: the annotation type to use for tokens :param sentence_type: the annotation type to use for sentence anntoations :return: the new or modified """ if gatenlpdoc is None: retdoc = Document(stanfordnlpdoc.text) else: retdoc = gatenlpdoc toki2annid = {} annset = retdoc.get_annotations(setname) # stanford nlp processes text in sentence chunks, so we do everything per sentence # NOTE: the stanford elements do not contain any text offsets, so we have to match and find # them ourselves. for this we keep an index to first character in the text which has not # been matched yet notmatchedidx = 0 for sent in stanfordnlpdoc.sentences: # a sentence is a list of tokens and a list of words. Some tokens consist of several words. # dependency parsers are over words, so we create Word and Token annotations, but we only # set the features per Word annotation for now. offsetinfos = utils.match_substrings(stanfordnlpdoc.text[notmatchedidx:], sent.words, getstr=lambda x: x.text) idx2annid = {} for oinfo in offsetinfos: word = oinfo[2] fm = { "string": word.text, "lemma": word.lemma, "upos": word.upos, "xpos": word.xpos, "dependency_relation": word.dependency_relation, "governor": int(word.governor) } for feat in word.feats.split("|"): if feat and feat != "_": k, v = feat.split("=") # TODO: maybe try to detect and convert bool/int values fm["feat_"+k] = v snlp_idx = int(word.index) annid = annset.add(oinfo[0]+notmatchedidx, oinfo[1]+notmatchedidx, word_type, fm) idx2annid[snlp_idx] = annid # create a sentence annotation from beginning of first word to end of last sentid = annset.add(offsetinfos[0][0]+notmatchedidx, offsetinfos[-1][1]+notmatchedidx, sentence_type) # now replace the governor index with the corresponding annid, the governor index is # mapped to the sentence annotation idx2annid[0] = sentid for annid in list(idx2annid.values()): ann = annset.get(annid) gov = ann.get_feature("governor") if gov is not None: ann.set_feature("governor", idx2annid[gov]) notmatchedidx = offsetinfos[-1][1]+notmatchedidx + 1 return retdoc