values ()) for item in mapping : tokenized_canonical = tuple ( normalizer ( item ). pop_dict ) > 0 : min_popularity = min ( self. info ( "Loading synonyms from entity mapping" ) line_count = 0 synonyms_added = 0 missing_canonicals = 0 min_popularity = 0 if len ( self. def update_with_entity_map ( self, mapping, normalizer, update_if_missing_canonical = True ): """Update gazetteer with a list of normalized key,value pairs from the input mapping list Args: mapping (list): A list of dicts containing canonnical names and whitelists of a particular entity normalizer (func): A QueryFactory normalization function that is used to normalize the input mapping data before they are added to the gazetteer. info ( " %d / %d entities in entity data file exceeded popularity " "cutoff and were added to the gazetteer", entities_added, line_count, ) _update_entity ( entity, float ( pop )) entities_added = 1 logger. format ( i 1, filename, num_cols )) if num_cols = 2 : pop, entity = split_row else : pop = 1.0 entity = split_row pop = 0 if pop = "null" else float ( pop ) line_count = 1 entity = normalizer ( entity ) if pop > popularity_cutoff : self. split ( " \t " ) if num_cols is None : num_cols = len ( split_row ) if len ( split_row ) != num_cols : msg = "Row columns" raise ValueError ( msg. readlines ()): if not row : continue split_row = row. open ( filename, encoding = "utf8" ) as data_file : for i, row in enumerate ( data_file. warning ( "Entity data file was not found at %s ", filename ) else : with codecs. info ( "Loading entity data from ' %s '", filename ) line_count = 0 entities_added = 0 num_cols = None if not os. normalizer (function): A function that normalizes text. popularity_cutoff (float): A threshold at which entities with popularity below this value are ignored. Args: filename (str): The filename of the entity data file. def update_with_entity_data_file ( self, filename, popularity_cutoff, normalizer ): """ Updates this gazetteer with data from an entity data file. debug ( "Updating gazetteer value of entity %s from %s to %s ", entity, old_value, self. entity_count = 1 if keep_max : old_value = self. exclude_ngrams : for ngram in iterate_ngrams ( entity. tokenize_and_normalize ( entity ) ) if self. tokenized_gaz_entry = tuple ( token for token in self. """ # Only update the relevant data structures when the entity isn't # already in the gazetteer. keep_max (bool): If True, if the entity is already in the pop_dict, then set the popularity to the max of popularity and the value in the pop_dict. popularity (float): The entity's popularity value. Args: entity (str): A normalized entity name. sys_types = gaz_data def _update_entity ( self, entity, popularity, keep_max = True ): """ Updates all gazetteer data with an entity and its popularity. def load ( self, gaz_path ): """Loads the gazetteer from disk Args: gaz_path (str): The location on disk where the gazetteer is stored """ gaz_data = joblib. text_preparation_pipeline = text_preparation_pipeline exclude_ngrams (bool): The boolean flat whether to exclude ngrams """ self. index (dict): A dictionary containing the inverted index, which maps terms and n-grams to the set of documents which contain them entities (list): A list of all entities sys_types (set): The set of nested numeric types for this entity """ def _init_ ( self, name, text_preparation_pipeline, exclude_ngrams = False ): """ Args: domain (str): The domain that this gazetteer is used text_preparation_pipeline (TextPreparationPipeline): Pipeline for tokenization and normalization of text. If there are more than one entity with the same name, the popularity is the maximum value across all duplicate entities. Attributes: entity_count (int): Total entities in the file pop_dict (dict): A dictionary containing the entity name as a key and the popularity score as the value. class Gazetteer : """ This class holds the following fields, which are extracted and exported to file. Step-by-Step Guide to Active Learning with Log Data in MindMeld. Working with the Text Preparation Pipeline.Working with the Knowledge Base and Question Answerer.Working with the Natural Language Processor.Step 9: Optimize Question Answering Performance.Step 7: Train the Natural Language Processing Classifiers.Step 6: Generate Representative Training Data.Step 4: Define the Dialogue State Handlers.Step 3: Define the Domain, Intent, Entity, and Role Hierarchy.Step 2: Script Your Ideal Dialogue Interactions.Building a Conversational Interface in 10 Steps.Anatomy of a Conversational AI Interaction.Different Approaches for Building Conversational Applications.Introduction to Conversational Applications.
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