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performance per entity type
This commit is contained in:
svlandeg
parent
b312f2d0e7
commit
81731907ba
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@ -15,10 +15,10 @@ INPUT_DIM = 300 # dimension of pre-trained vectors
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DESC_WIDTH = 64
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def create_kb(nlp, max_entities_per_alias, min_occ,
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def create_kb(nlp, max_entities_per_alias, min_entity_freq, min_occ,
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entity_def_output, entity_descr_output,
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count_input, prior_prob_input, to_print=False):
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""" Create the knowledge base from Wikidata entries """
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# Create the knowledge base from Wikidata entries
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kb = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=DESC_WIDTH)
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# disable this part of the pipeline when rerunning the KB generation from preprocessed files
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@ -37,21 +37,26 @@ def create_kb(nlp, max_entities_per_alias, min_occ,
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title_to_id = _get_entity_to_id(entity_def_output)
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id_to_descr = _get_id_to_description(entity_descr_output)
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title_list = list(title_to_id.keys())
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# TODO: remove this filter (just for quicker testing of code)
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# title_list = title_list[0:342]
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# title_to_id = {t: title_to_id[t] for t in title_list}
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entity_list = [title_to_id[x] for x in title_list]
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# Currently keeping entities from the KB where there is no description - putting a default void description
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description_list = [id_to_descr.get(x, "No description defined") for x in entity_list]
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print()
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print(" * _get_entity_frequencies", datetime.datetime.now())
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print()
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entity_frequencies = wp.get_entity_frequencies(count_input=count_input, entities=title_list)
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entity_frequencies = wp.get_all_frequencies(count_input=count_input)
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# filter the entities for in the KB by frequency, because there's just too much data otherwise
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filtered_title_to_id = dict()
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entity_list = list()
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description_list = list()
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frequency_list = list()
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for title, entity in title_to_id.items():
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freq = entity_frequencies.get(title, 0)
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desc = id_to_descr.get(entity, None)
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if desc and freq > min_entity_freq:
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entity_list.append(entity)
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description_list.append(desc)
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frequency_list.append(freq)
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filtered_title_to_id[title] = entity
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print("Kept", len(filtered_title_to_id.keys()), "out of", len(title_to_id.keys()), "titles")
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print()
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print(" * train entity encoder", datetime.datetime.now())
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@ -67,12 +72,12 @@ def create_kb(nlp, max_entities_per_alias, min_occ,
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print()
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print(" * adding", len(entity_list), "entities", datetime.datetime.now())
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kb.set_entities(entity_list=entity_list, prob_list=entity_frequencies, vector_list=embeddings)
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kb.set_entities(entity_list=entity_list, prob_list=frequency_list, vector_list=embeddings)
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print()
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print(" * adding aliases", datetime.datetime.now())
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print()
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_add_aliases(kb, title_to_id=title_to_id,
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_add_aliases(kb, title_to_id=filtered_title_to_id,
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max_entities_per_alias=max_entities_per_alias, min_occ=min_occ,
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prior_prob_input=prior_prob_input)
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@ -21,7 +21,7 @@ ENTITY_FILE = "gold_entities.csv"
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def create_training(entity_def_input, training_output):
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wp_to_id = kb_creator._get_entity_to_id(entity_def_input)
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_process_wikipedia_texts(wp_to_id, training_output, limit=100000000) # TODO: full dataset 100000000
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_process_wikipedia_texts(wp_to_id, training_output, limit=100000000)
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def _process_wikipedia_texts(wp_to_id, training_output, limit=None):
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@ -29,6 +29,7 @@ NLP_2_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/nlp_2'
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TRAINING_DIR = 'C:/Users/Sofie/Documents/data/wikipedia/training_data_nel/'
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MAX_CANDIDATES = 10
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MIN_ENTITY_FREQ = 200
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MIN_PAIR_OCC = 5
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DOC_SENT_CUTOFF = 2
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EPOCHS = 10
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@ -46,14 +47,14 @@ def run_pipeline():
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# one-time methods to create KB and write to file
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to_create_prior_probs = False
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to_create_entity_counts = False
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to_create_kb = False # TODO: entity_defs should also contain entities not in the KB
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to_create_kb = True
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# read KB back in from file
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to_read_kb = False
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to_test_kb = False
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# create training dataset
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create_wp_training = True
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create_wp_training = False
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# train the EL pipe
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train_pipe = False
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@ -84,13 +85,14 @@ def run_pipeline():
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if to_create_kb:
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print("STEP 3a: to_create_kb", datetime.datetime.now())
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kb_1 = kb_creator.create_kb(nlp_1,
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max_entities_per_alias=MAX_CANDIDATES,
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min_occ=MIN_PAIR_OCC,
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entity_def_output=ENTITY_DEFS,
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entity_descr_output=ENTITY_DESCR,
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count_input=ENTITY_COUNTS,
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prior_prob_input=PRIOR_PROB,
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to_print=False)
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max_entities_per_alias=MAX_CANDIDATES,
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min_entity_freq=MIN_ENTITY_FREQ,
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min_occ=MIN_PAIR_OCC,
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entity_def_output=ENTITY_DEFS,
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entity_descr_output=ENTITY_DESCR,
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count_input=ENTITY_COUNTS,
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prior_prob_input=PRIOR_PROB,
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to_print=False)
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print("kb entities:", kb_1.get_size_entities())
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print("kb aliases:", kb_1.get_size_aliases())
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print()
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@ -112,7 +114,7 @@ def run_pipeline():
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# test KB
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if to_test_kb:
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run_el.run_kb_toy_example(kb=kb_2)
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test_kb(kb_2)
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print()
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# STEP 5: create a training dataset from WP
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@ -121,10 +123,18 @@ def run_pipeline():
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training_set_creator.create_training(entity_def_input=ENTITY_DEFS, training_output=TRAINING_DIR)
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# STEP 6: create the entity linking pipe
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el_pipe = nlp_2.create_pipe(name='entity_linker', config={"doc_cutoff": DOC_SENT_CUTOFF})
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el_pipe.set_kb(kb_2)
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nlp_2.add_pipe(el_pipe, last=True)
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other_pipes = [pipe for pipe in nlp_2.pipe_names if pipe != "entity_linker"]
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with nlp_2.disable_pipes(*other_pipes): # only train Entity Linking
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nlp_2.begin_training()
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if train_pipe:
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print("STEP 6: training Entity Linking pipe", datetime.datetime.now())
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train_limit = 50
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dev_limit = 10
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train_limit = 10
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dev_limit = 2
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print("Training on", train_limit, "articles")
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print("Dev testing on", dev_limit, "articles")
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print()
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@ -141,14 +151,6 @@ def run_pipeline():
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limit=dev_limit,
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to_print=False)
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el_pipe = nlp_2.create_pipe(name='entity_linker', config={"doc_cutoff": DOC_SENT_CUTOFF})
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el_pipe.set_kb(kb_2)
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nlp_2.add_pipe(el_pipe, last=True)
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other_pipes = [pipe for pipe in nlp_2.pipe_names if pipe != "entity_linker"]
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with nlp_2.disable_pipes(*other_pipes): # only train Entity Linking
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nlp_2.begin_training()
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for itn in range(EPOCHS):
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random.shuffle(train_data)
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losses = {}
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@ -180,30 +182,32 @@ def run_pipeline():
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# print(" measuring accuracy 1-1")
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 1
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dev_acc_1_1 = _measure_accuracy(dev_data, el_pipe)
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train_acc_1_1 = _measure_accuracy(train_data, el_pipe)
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print("train/dev acc combo:", round(train_acc_1_1, 2), round(dev_acc_1_1, 2))
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dev_acc_1_1, dev_acc_1_1_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc combo:", round(dev_acc_1_1, 3), [(x, round(y, 3)) for x, y in dev_acc_1_1_dict.items()])
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train_acc_1_1, train_acc_1_1_dict = _measure_accuracy(train_data, el_pipe)
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print("train acc combo:", round(train_acc_1_1, 3), [(x, round(y, 3)) for x, y in train_acc_1_1_dict.items()])
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# baseline using only prior probabilities
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el_pipe.context_weight = 0
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el_pipe.prior_weight = 1
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dev_acc_0_1 = _measure_accuracy(dev_data, el_pipe)
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train_acc_0_1 = _measure_accuracy(train_data, el_pipe)
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print("train/dev acc prior:", round(train_acc_0_1, 2), round(dev_acc_0_1, 2))
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dev_acc_0_1, dev_acc_0_1_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc prior:", round(dev_acc_0_1, 3), [(x, round(y, 3)) for x, y in dev_acc_0_1_dict.items()])
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train_acc_0_1, train_acc_0_1_dict = _measure_accuracy(train_data, el_pipe)
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print("train acc prior:", round(train_acc_0_1, 3), [(x, round(y, 3)) for x, y in train_acc_0_1_dict.items()])
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# using only context
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 0
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dev_acc_1_0 = _measure_accuracy(dev_data, el_pipe)
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train_acc_1_0 = _measure_accuracy(train_data, el_pipe)
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print("train/dev acc context:", round(train_acc_1_0, 2), round(dev_acc_1_0, 2))
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dev_acc_1_0, dev_acc_1_0_dict = _measure_accuracy(dev_data, el_pipe)
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print("dev acc context:", round(dev_acc_1_0, 3), [(x, round(y, 3)) for x, y in dev_acc_1_0_dict.items()])
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train_acc_1_0, train_acc_1_0_dict = _measure_accuracy(train_data, el_pipe)
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print("train acc context:", round(train_acc_1_0, 3), [(x, round(y, 3)) for x, y in train_acc_1_0_dict.items()])
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print()
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# reset for follow-up tests
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el_pipe.context_weight = 1
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el_pipe.prior_weight = 1
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if to_test_pipeline:
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print()
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print("STEP 8: applying Entity Linking to toy example", datetime.datetime.now())
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@ -230,8 +234,8 @@ def run_pipeline():
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def _measure_accuracy(data, el_pipe):
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correct = 0
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incorrect = 0
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correct_by_label = dict()
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incorrect_by_label = dict()
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docs = [d for d, g in data if len(d) > 0]
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docs = el_pipe.pipe(docs)
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@ -245,31 +249,53 @@ def _measure_accuracy(data, el_pipe):
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correct_entries_per_article[str(start) + "-" + str(end)] = gold_kb
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for ent in doc.ents:
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if ent.label_ == "PERSON": # TODO: expand to other types
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pred_entity = ent.kb_id_
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start = ent.start_char
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end = ent.end_char
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gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
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# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
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if gold_entity is not None:
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if gold_entity == pred_entity:
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correct += 1
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else:
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incorrect += 1
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ent_label = ent.label_
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pred_entity = ent.kb_id_
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start = ent.start_char
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end = ent.end_char
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gold_entity = correct_entries_per_article.get(str(start) + "-" + str(end), None)
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# the gold annotations are not complete so we can't evaluate missing annotations as 'wrong'
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if gold_entity is not None:
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if gold_entity == pred_entity:
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correct = correct_by_label.get(ent_label, 0)
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correct_by_label[ent_label] = correct + 1
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else:
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incorrect = incorrect_by_label.get(ent_label, 0)
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incorrect_by_label[ent_label] = incorrect + 1
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except Exception as e:
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print("Error assessing accuracy", e)
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if correct == incorrect == 0:
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return 0
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acc_by_label = dict()
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total_correct = 0
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total_incorrect = 0
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for label, correct in correct_by_label.items():
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incorrect = incorrect_by_label.get(label, 0)
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total_correct += correct
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total_incorrect += incorrect
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if correct == incorrect == 0:
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acc_by_label[label] = 0
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else:
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acc_by_label[label] = correct / (correct + incorrect)
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acc = 0
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if not (total_correct == total_incorrect == 0):
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acc = total_correct / (total_correct + total_incorrect)
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return acc, acc_by_label
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acc = correct / (correct + incorrect)
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return acc
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def test_kb(kb):
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for mention in ("Bush", "Douglas Adams", "Homer", "Brazil", "China"):
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candidates = kb.get_candidates(mention)
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print("generating candidates for " + mention + " :")
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for c in candidates:
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print(" ", c.prior_prob, c.alias_, "-->", c.entity_ + " (freq=" + str(c.entity_freq) + ")")
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print()
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def run_el_toy_example(nlp):
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text = "In The Hitchhiker's Guide to the Galaxy, written by Douglas Adams, " \
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"Douglas reminds us to always bring our towel. " \
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"Douglas reminds us to always bring our towel, even in China or Brazil. " \
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"The main character in Doug's novel is the man Arthur Dent, " \
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"but Douglas doesn't write about George Washington or Homer Simpson."
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doc = nlp(text)
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@ -1,7 +1,6 @@
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# coding: utf-8
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from __future__ import unicode_literals
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import re
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import bz2
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import json
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import datetime
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@ -14,7 +13,7 @@ def read_wikidata_entities_json(limit=None, to_print=False):
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""" Read the JSON wiki data and parse out the entities. Takes about 7u30 to parse 55M lines. """
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lang = 'en'
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prop_filter = {'P31': {'Q5', 'Q15632617'}} # currently defined as OR: one property suffices to be selected
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# prop_filter = {'P31': {'Q5', 'Q15632617'}} # currently defined as OR: one property suffices to be selected
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site_filter = 'enwiki'
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title_to_id = dict()
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@ -41,18 +40,19 @@ def read_wikidata_entities_json(limit=None, to_print=False):
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entry_type = obj["type"]
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if entry_type == "item":
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# filtering records on their properties
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keep = False
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# filtering records on their properties (currently disabled to get ALL data)
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# keep = False
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keep = True
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claims = obj["claims"]
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for prop, value_set in prop_filter.items():
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claim_property = claims.get(prop, None)
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if claim_property:
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for cp in claim_property:
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cp_id = cp['mainsnak'].get('datavalue', {}).get('value', {}).get('id')
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cp_rank = cp['rank']
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if cp_rank != "deprecated" and cp_id in value_set:
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keep = True
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# for prop, value_set in prop_filter.items():
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# claim_property = claims.get(prop, None)
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# if claim_property:
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# for cp in claim_property:
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# cp_id = cp['mainsnak'].get('datavalue', {}).get('value', {}).get('id')
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# cp_rank = cp['rank']
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# if cp_rank != "deprecated" and cp_id in value_set:
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# keep = True
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if keep:
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unique_id = obj["id"]
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@ -70,6 +70,7 @@ def read_wikidata_entities_json(limit=None, to_print=False):
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if to_print:
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print("prop:", prop, cp_values)
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found_link = False
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if parse_sitelinks:
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site_value = obj["sitelinks"].get(site_filter, None)
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if site_value:
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@ -77,6 +78,7 @@ def read_wikidata_entities_json(limit=None, to_print=False):
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if to_print:
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print(site_filter, ":", site)
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title_to_id[site] = unique_id
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found_link = True
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if parse_labels:
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labels = obj["labels"]
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@ -86,7 +88,7 @@ def read_wikidata_entities_json(limit=None, to_print=False):
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if to_print:
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print("label (" + lang + "):", lang_label["value"])
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if parse_descriptions:
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if found_link and parse_descriptions:
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descriptions = obj["descriptions"]
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if descriptions:
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lang_descr = descriptions.get(lang, None)
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@ -175,7 +175,7 @@ def write_entity_counts(prior_prob_input, count_output, to_print=False):
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print("Total count:", total_count)
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def get_entity_frequencies(count_input, entities):
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def get_all_frequencies(count_input):
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entity_to_count = dict()
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with open(count_input, 'r', encoding='utf8') as csvfile:
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csvreader = csv.reader(csvfile, delimiter='|')
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@ -184,4 +184,5 @@ def get_entity_frequencies(count_input, entities):
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for row in csvreader:
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entity_to_count[row[0]] = int(row[1])
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return [entity_to_count.get(e, 0) for e in entities]
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return entity_to_count
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