class Annotator():
__job_position_tag = "EMP-POS"
__job_company_tag = "EMP-COMP"
__education_course_tag = "EDU-MAJOR"
__education_institution_tag = "EDU-INST"
def __init__(self):
self.__extractor = Extractor()
self.__tokeniser = Tokeniser()
self.__tagger = Tagger()
self.__dataset = Dataset()
self.__logger = Logger()
def prepare_dataset(self, nr_of_docs=-1):
resumes, labels = self.__extractor.read_raw_files(nr_of_docs)
resumes = self.__tokeniser.tokenise_docs_to_lines(resumes)
resumes = self.__tokeniser.tokenise_doclines_to_words(resumes)
self.__dataset.resume_content = self.annotate_docs(resumes, labels)
self.__dataset.save()
# resumes: list of tokenised (by line and word) résumé docs
# labels: xml structure storing labels for several resumes
def annotate_docs(self, resumes, labels):
self.__logger.println("annotating resumes")
annotated_resumes = []
for idx, resume in enumerate(resumes):
annotated_resumes.append(self.annotate_doc(resume, labels[idx]))
self.__logger.println(
"annotating resume %s/%s with true labels and pos tags" %
(idx + 1, len(resumes)))
# non local ner tag entire dataset at a time for speed
annotated_resumes = self.__tagger.nonlocal_ner_tag(annotated_resumes)
self.__logger.println("completed annotating resumes")
return annotated_resumes
# doc: a single résumé document with token strings in each slot of list
# labels: xml structure storing pre-extracted information
def annotate_doc(self, doc, labels):
job_title_list = self.__extractor.get_job_titles(labels)
job_company_list = self.__extractor.get_company_names(labels)
edu_major_list = self.__extractor.get_edu_majors(labels)
edu_inst_list = self.__extractor.get_edu_institutions(labels)
# can extract more labels here
prepared_doc = self.__tagger.prepare_doc(doc)
prepared_doc = self.__match_entity(prepared_doc, job_title_list,
self.__job_position_tag)
prepared_doc = self.__match_entity(prepared_doc, job_company_list,
self.__job_company_tag)
prepared_doc = self.__match_entity(prepared_doc, edu_major_list,
self.__education_course_tag)
prepared_doc = self.__match_entity(prepared_doc, edu_inst_list,
self.__education_institution_tag)
prepared_doc = self.__tagger.add_default_entity_tags(prepared_doc)
prepared_doc = self.__tagger.pos_tag(prepared_doc)
return prepared_doc
# doc: résumé doc to be annotated
# entity_list: list of labels to matched in doc
# tag: tag to be assigned if match found
def __match_entity(self, doc, entity_list, tag):
for entity in entity_list:
doc = self.__tagger.match_label(doc, entity, tag)
return doc
# function takes in a path to file and annotates it for tagging
# to be ideally used to tag as a one off for testing
# filepath: path to résumé
def annotate_using_trained_model(self, filepath):
resume_content = self.__extractor.read_resume_content(filepath)
resume_content = self.__tokeniser.tokenise_docs_to_lines(
resume_content)
resume_content = self.__tokeniser.tokenise_doclines_to_words(
resume_content)
prepared_doc = self.__tagger.prepare_doc(resume_content[0])
prepared_doc = self.__tagger.pos_tag(prepared_doc)
prepared_doc = self.__tagger.nonlocal_ner_tag([prepared_doc])
return prepared_doc[0]
