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!wget --no-check-certificate https://biocreative.bioinformatics.udel.edu/media/store/files/2014/chemdner_corpus.tar.gzText mining with chemical text from chemdNER
take-home
deep-learning
spacy
nlp
This is a take home project that I did for a company as part of their interview process.
“Text mining with chemical text from chemdNER”
“This is a take home project that I did for a company as part of their interview process.”
This blog post has the code and details, of the take home project that I did for a company as part of their interview process, this primarily uses the CHEMDNER data to create a named recognition model using spaCy
The CHEMDNER corpus of chemicals and drugs and its annotation principles - Krallinger, M. et al. The CHEMDNER corpus of chemicals and drugs and its annotation principles. J Cheminform, 2014
This code should be directly runnable in colab, so just check and change the paths where I save and get data from my google drive, to run on your colab instance.
Prepare data
Get data and make create directories to organise information.
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!mkdir /content/data
!tar -xvf /content/drive/MyDrive/chemdNER\ data/data.tar.gz -C /content/dataStandard Output
chemdner_corpus/
chemdner_corpus/BioC.dtd
chemdner_corpus/cdi_ann_test_13-09-13.txt
chemdner_corpus/cem_ann_test_13-09-13.txt
chemdner_corpus/chemdner_abs_test_pmid_label.txt
chemdner_corpus/chemdner_chemical_disciplines.txt
chemdner_corpus/chemdner.key
chemdner_corpus/development.abstracts.txt
chemdner_corpus/development.annotations.txt
chemdner_corpus/development.bioc.xml
chemdner_corpus/evaluation.abstracts.txt
chemdner_corpus/evaluation.annotations.txt
chemdner_corpus/evaluation.bioc.xml
chemdner_corpus/evaluation.predictions.txt
chemdner_corpus/plain2bioc.py
chemdner_corpus/Readme.txt
chemdner_corpus/silver.abstracts.txt
chemdner_corpus/silver.predictions.txt
chemdner_corpus/training.abstracts.txt
chemdner_corpus/training.annotations.txt
chemdner_corpus/training.bioc.xml
chemdner_corpus/evaluation.predictions.bioc.xmlShow code
#hide
!tail -f "/content/data/chemdner_corpus/training.annotations.txt"Install required libraries
Look at the libraries currently installed.
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!pip list | grep spacyspacy 2.2.4 Show code
!pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.3.0/en_core_sci_lg-0.3.0.tar.gzSince we are training data with medical text a model with similar vectors will be extremely useful, using the scispacy model vectors.
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!pip list| grep en-core-sci-lgen-core-sci-lg 0.3.0 Import annotations
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import pandas as pd
from pathlib import Path
annot_path = Path('/content/data/chemdner_corpus')
export_path = Path('/content/data/spacy_annotation_data')
export_path.mkdir(exist_ok=True)Show code
# lets read the annotations
train_annot = pd.read_csv(annot_path/'training.annotations.txt', sep='\t', names=['PMID', 'Text Type', 'Start', 'End', 'Entity Text', 'Entity'])
train_annot.head()| PMID | Text Type | Start | End | Entity Text | Entity | |
|---|---|---|---|---|---|---|
| 0 | 21826085 | A | 946 | 957 | haloperidol | TRIVIAL |
| 1 | 22080034 | A | 190 | 199 | aflatoxin | FAMILY |
| 2 | 22080034 | A | 594 | 603 | aflatoxin | FAMILY |
| 3 | 22080034 | A | 718 | 727 | aflatoxin | FAMILY |
| 4 | 22080034 | A | 1072 | 1081 | aflatoxin | FAMILY |
Since the training text can be in the title or in the abstract, we should be careful on what text we are giving in an annotation.
Let’s check in the annotation for one value
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start, end = train_annot.loc[train_annot['PMID'] == 21826085, ['Start', 'End']].values.tolist()[0]Show code
start, end # start, end indices for haloperidol(946, 957)Show code
# lets read the texts from which the entities were annotated
train_text = pd.read_csv(annot_path/'training.abstracts.txt', sep='\t', names=['PMID', 'Title', 'Abstract'])
train_text.head()| PMID | Title | Abstract | |
|---|---|---|---|
| 0 | 21826085 | DPP6 as a candidate gene for neuroleptic-induc... | We implemented a two-step approach to detect p... |
| 1 | 22080034 | Nanosilver effects on growth parameters in exp... | Aflatoxicosis is a cause of economic losses in... |
| 2 | 22080035 | The influence of the intensity of smoking and ... | The aim of this study was to investigate the e... |
| 3 | 22080037 | Mercury induces the expression of cyclooxygena... | Nuclear factor-κB (NF-κB) is a transcription f... |
| 4 | 22258629 | Toxic effects of chromium on tannery workers a... | Chromium is widely used in the leather industr... |
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train_annot['Text Type'].value_counts()A 26425
T 3053
Name: Text Type, dtype: int64Show code
# text in the abstract/title for the same id
train_text.loc[train_text['PMID'] == 21826085, 'Abstract'].item()[start:end]'haloperidol'We can see that the annotation data will seperate for the Title and Abstract pieces of the text.
Make raw text files for pretraining
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import srslyShow code
# import other files for text
dev_text = pd.read_csv(annot_path/'development.abstracts.txt', sep='\t', names=['PMID', 'Title', 'Abstract'])
eval_text = pd.read_csv(annot_path/'evaluation.abstracts.txt', sep='\t', names=['PMID', 'Title', 'Abstract'])Show code
texts_path = export_path/'texts'
texts_path.mkdir(exist_ok=True)
def make_jsonl(df:pd.DataFrame):
jsonl_txts = []
for data in df.itertuples(index=False):
jsonl_txts.append({'text':data.Title+'\n'+data.Abstract})
return jsonl_txts
train_txts = make_jsonl(train_text)
dev_txts = make_jsonl(dev_text)
eval_txts = make_jsonl(eval_text)
all_txts = train_txts + eval_txts + dev_txtsShow code
# I only write 1500 texts because it does not cause memory errors when pretraining
srsly.write_jsonl(str(texts_path/'pretrain_txts.jsonl'), train_txts[:1500])Make json file for training
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# join the dataframes so that we can generate annotations from them
# set pmid as the index
train_annot.set_index('PMID', drop=True, inplace=True)
train_text.set_index('PMID', drop=True, inplace=True)Show code
train_data = train_text.join(train_annot)Show code
train_data.head()| Title | Abstract | Text Type | Start | End | Entity Text | Entity | |
|---|---|---|---|---|---|---|---|
| PMID | |||||||
| 21826085 | DPP6 as a candidate gene for neuroleptic-induc... | We implemented a two-step approach to detect p... | A | 946.0 | 957.0 | haloperidol | TRIVIAL |
| 22080034 | Nanosilver effects on growth parameters in exp... | Aflatoxicosis is a cause of economic losses in... | A | 190.0 | 199.0 | aflatoxin | FAMILY |
| 22080034 | Nanosilver effects on growth parameters in exp... | Aflatoxicosis is a cause of economic losses in... | A | 594.0 | 603.0 | aflatoxin | FAMILY |
| 22080034 | Nanosilver effects on growth parameters in exp... | Aflatoxicosis is a cause of economic losses in... | A | 718.0 | 727.0 | aflatoxin | FAMILY |
| 22080034 | Nanosilver effects on growth parameters in exp... | Aflatoxicosis is a cause of economic losses in... | A | 1072.0 | 1081.0 | aflatoxin | FAMILY |
Let’s check the number of annotations for each label.
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train_data['Entity'].value_counts().plot(kind='bar', title='Number of annotations per label')
We have eight entities and the entity named TRIVIAL has the most number of annotations
Now we can start generating the annotation from the dataframe, to accomodate the training format that we want we can split the data frame to Title and Abstract annotations, so that we can run the same logic easily, (ie) grouping the texts and getting all the annotations corresponding to that text in one shot.
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abstract_annots = train_data.loc[train_data['Text Type'] == 'A', :]
title_annots = train_data.loc[train_data['Text Type'] == 'T', :]Show code
import en_core_sci_lg
from spacy.tokens import Span
from spacy.gold import docs_to_json
import json
# load the pretrained model and other utility functions
# to generate the training data
mod = en_core_sci_lg.load()
# since we are going to fill this manually
# for annotation, disabling some pipeline
# modules may speedup the process
mod.disable_pipes(['ner'])[('ner', <spacy.pipeline.pipes.EntityRecognizer at 0x7f8f0be55408>)]Show code
def extract_spacy_docs(annot_df:pd.DataFrame, ext_from:str) -> list:
# Extracting the doc objects from a pretrained model
# so that the tokenization matches for this domain of
# text that we have
annot_docs = []
# Same texts have multiple annotations so group and take
# one by one
for _, annot_data in annot_df.groupby(ext_from):
# Text for annotation
txt = annot_data.iloc[0][ext_from]
doc = mod(txt)
ent_spans = []
text_ranges = []
for annot in annot_data.itertuples():
start = int(annot.Start)
end = int(annot.End)
# Validating overlapping entity annotations
if start not in text_ranges and end not in text_ranges:
# Make spans with our annotations
span = doc.char_span(int(annot.Start), int(annot.End), label=annot.Entity)
# It may be None for invalid annotations(wrt spaCy)
if span is not None:
ent_spans.append(span)
text_ranges.extend(list(range(start, end+1,)))
# Just for info
else:
print(txt, txt[start:end])
doc.ents = ent_spans
annot_docs.append(doc)
return annot_docsShow code
# generate docs for training
abstract_annots = extract_spacy_docs(abstract_annots, 'Abstract')
title_annots = extract_spacy_docs(title_annots, 'Title')
train_annot_docs = abstract_annots + title_annots
train_json = docs_to_json(train_annot_docs)Show code
# Persist data
spacy_annot_path = export_path/'annots'
spacy_annot_path.mkdir(exist_ok=True)
srsly.write_json(spacy_annot_path/'train_data.json', [train_json])We must do the same for the development and evaluation data
Make json file for evaluation and development
Let’s make a function out of the process we did for the training data, for the evaluation and development data.
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dev_annots = pd.read_csv(annot_path/'development.annotations.txt', sep='\t',
names=['PMID', 'Text Type', 'Start', 'End', 'Entity Text', 'Entity'])
eval_annots = pd.read_csv(annot_path/'evaluation.annotations.txt', sep='\t',
names=['PMID', 'Text Type', 'Start', 'End', 'Entity Text', 'Entity'])Show code
def prepare_df_for_spacy_annots(df1: pd.DataFrame, df2: pd.DataFrame) -> 'Abstract and Title dataframes':
# Emulates the processing we did for training data
df1.set_index('PMID', drop=True, inplace=True)
df2.set_index('PMID', drop=True, inplace=True)
df = df1.join(df2)
abstract_annots = df.loc[df['Text Type'] == 'A', :]
title_annots = df.loc[df['Text Type'] == 'T', :]
return abstract_annots, title_annots
def prepare_spacy_json_from_df(df1: pd.DataFrame, df2: pd.DataFrame):
# transform the docs to spacy json
abstract_annots, title_annots = prepare_df_for_spacy_annots(df1, df2)
abstract_docs = extract_spacy_docs(abstract_annots, 'Abstract')
title_docs = extract_spacy_docs(title_annots, 'Title')
annot_docs = abstract_docs + title_docs
spacy_json = docs_to_json(train_annot_docs)
return spacy_jsonShow code
# This might take some time
dev_json = prepare_spacy_json_from_df(dev_text, dev_annots)
eval_json = prepare_spacy_json_from_df(eval_text, eval_annots)
srsly.write_json(spacy_annot_path/'eval_data.json', [eval_json])
srsly.write_json(spacy_annot_path/'dev_data.json', [dev_json])We now have all the files required to make the model, let’s start by pretraining the model with spacy cli
Pretrain spacy with texts
We have imported scispacy's model vectors(en_core_sci_lg) to give us a headstart
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!python -m spacy pretrain /content/data/spacy_annotation_data/texts/pretrain_txts.jsonl /usr/local/lib/python3.6/dist-packages/en_core_sci_lg/en_core_sci_lg-0.3.0 /content/data/pretrained_model -i 50 -se 10 -s 0 -bs 64Debug the training data for spacy NER model
Let’s see if the data is proper for training using spacy’s debug-data command
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%%bash
python -m spacy debug-data en\
/content/drive/MyDrive/chemdNER\ data/annot_data/train_data.json \
/content/drive/MyDrive/chemdNER\ data/annot_data/dev_data.json \
-p 'ner' \
-b /usr/local/lib/python3.6/dist-packages/en_core_sci_lg/en_core_sci_lg-0.3.0=========================== Data format validation =========================== ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ 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Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ⠧ Loading corpus... ⠇ Loading corpus... ⠏ Loading corpus... ⠙ Loading corpus... ⠹ Loading corpus... ⠸ Loading corpus... ⠼ Loading corpus... ⠴ Loading corpus... ⠦ Loading corpus... ✔ Corpus is loadable =============================== Training stats =============================== Training pipeline: ner Starting with base model '/usr/local/lib/python3.6/dist-packages/en_core_sci_lg/en_core_sci_lg-0.3.0' 4884 training docs 4884 evaluation docs ⚠ 4884 training examples also in evaluation data ============================== Vocab & Vectors ============================== ℹ 647737 total words in the data (47733 unique) ℹ 600000 vectors (667864 unique keys, 200 dimensions) ⚠ 21625 words in training data without vectors (0.03%) ========================== Named Entity Recognition ========================== ℹ 8 new labels, 0 existing labels 0 missing values (tokens with '-' label) ⚠ 3 entity span(s) with punctuation ⚠ Low number of examples for new label 'NO CLASS' (40) ⠙ Analyzing label distribution... ✔ Examples without occurrences available for all labels ✔ No entities consisting of or starting/ending with whitespace Entity spans consisting of or starting/ending with punctuation can not be trained with a noise level > 0. ================================== Summary ================================== ✔ 3 checks passed ⚠ 4 warnings
We have low annotations for NO CLASS but since we do not have more data, we have to work with what we’ve got.
We only had 4884 pieces of text from the dataset, and it seems to be used in all the places (train, dev, eval), but the annotation in each will differ.
Show code
# linking for easy access
!python -m spacy link /usr/local/lib/python3.6/dist-packages/en_core_sci_lg/en_core_sci_lg-0.3.0 en_core_sci_lgTrain model
I tried to train with the pretrained model which we made using raw text but it was not working and it seems most of the tutorials use it only text classification problems.
But anyway I start with the vectors of scispacy's pretrained en_core_sci_lg model for a headstart.
Show code
%%bash
python -m spacy train \
en \
/content/data/NER\ model \
/content/drive/MyDrive/chemdNER\ data/annot_data/train_data.json \
/content/drive/MyDrive/chemdNER\ data/annot_data/dev_data.json \
-v /usr/local/lib/python3.6/dist-packages/en_core_sci_lg/en_core_sci_lg-0.3.0 \
-p "ner" \
-n 30 \
-g 0 \
-V 0.1 \
-cW 3 -cd 4 -cw 4 -cP 3Training pipeline: ['ner'] Using GPU: 0 Starting with blank model 'en' Loading vector from model '/usr/local/lib/python3.6/dist-packages/en_core_sci_lg/en_core_sci_lg-0.3.0' Counting training words (limit=0) Itn NER Loss NER P NER R NER F Token % CPU WPS GPU WPS --- --------- ------ ------ ------ ------- ------- ------- 1 48369.133 38.580 23.141 28.929 91.214 79243 91040 2 37874.029 41.895 28.937 34.231 91.214 79243 93572 3 35916.519 45.435 33.101 38.300 91.214 79243 88774 4 34941.559 48.925 37.233 42.286 91.214 79243 90150 5 33485.552 52.235 40.570 45.669 91.214 79243 88586 6 32591.528 56.412 44.786 49.931 91.214 79243 85016 7 31565.729 59.580 48.290 53.344 91.214 79243 87689 8 30361.623 63.674 53.106 57.912 91.214 79243 83318 9 29941.032 66.154 56.482 60.937 91.214 79243 82007 10 28910.156 68.003 58.950 63.154 91.214 79243 81201 11 28704.946 69.475 60.998 64.961 91.214 79243 84349 12 27963.309 70.596 62.859 66.503 91.214 79243 84314 13 27236.334 71.279 64.103 67.501 91.214 79243 85654 14 27401.528 72.042 65.215 68.458 91.214 79243 85735 15 26876.413 72.860 65.895 69.203 91.214 79243 86002 16 26792.615 73.405 66.611 69.843 91.214 79243 83849 17 26456.828 74.399 67.623 70.849 91.214 79243 86532 18 25825.262 74.959 68.207 71.424 91.214 79243 85455 19 25672.156 75.481 68.839 72.007 91.214 79243 84888 20 25490.234 75.762 69.159 72.310 91.214 79243 84073 21 24909.075 76.367 69.799 72.935 91.214 79243 86211 22 24919.627 76.807 70.315 73.418 91.214 79243 85168 23 24733.465 77.390 70.775 73.935 91.214 79243 87323 24 24437.002 77.744 71.139 74.295 91.214 79243 83429 25 24302.424 78.324 71.547 74.782 91.214 79243 88724 26 24329.596 78.659 71.951 75.156 91.214 79243 84788 27 24270.859 79.210 72.179 75.531 91.214 79243 86826 28 23739.026 79.464 72.519 75.833 91.214 79243 85965 29 23988.403 79.575 72.563 75.908 91.214 79243 83390 30 23883.377 79.800 72.583 76.021 91.214 79243 82936 ✔ Saved model to output directory /content/data/NER model/model-final ⠙ Creating best model... ⠹ Creating best model... ⠸ Creating best model... ⠼ Creating best model... ⠴ Creating best model... ⠦ Creating best model... ⠧ Creating best model... ⠇ Creating best model... ⠏ Creating best model... ⠙ Creating best model... ⠹ Creating best model... ⠸ Creating best model... ⠼ Creating best model... ⠴ Creating best model... ⠦ Creating best model... ⠧ Creating best model... ⠇ Creating best model... ⠏ Creating best model... ⠙ Creating best model... ⠹ Creating best model... ✔ Created best model /content/data/NER model/model-best
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I did not try to tune the hyper parameters and relied on the defaults from the lib, but it has given decent results
Evaluate model with test data
Let’s evaluate with the evaluation data that we generated earlier
Show code
%%bash
python -m spacy evaluate \
/content/data/NER\ model/model-best \
/content/drive/MyDrive/chemdNER\ data/annot_data/eval_data.json \
-g 0 -dp /content/data/eval\ results -R================================== Results ================================== Time 8.66 s Words 688898 Words/s 79533 TOK 91.21 POS 0.03 UAS 3.88 LAS 0.00 NER P 79.80 NER R 72.58 NER F 76.02 Textcat 0.00 ✔ Generated 25 parses as HTML /content/data/eval results
/usr/local/lib/python3.6/dist-packages/spacy/displacy/__init__.py:189: UserWarning: [W006] No entities to visualize found in Doc object. If this is surprising to you, make sure the Doc was processed using a model that supports named entity recognition, and check the `doc.ents` property manually if necessary.
warnings.warn(Warnings.W006)The results of some text are persisted and can be viewed in the archive.
Misc
Here I tried to write code for simple annotation format, mostly for archival, can be skipped, if need be
Show code
def extract_annot_data(annot_df: pd.DataFrame, ext_from: str) -> list:
# groups text and generates the list of entities and
# their spans in the text
train_data = []
for _, annot_data in annot_df.groupby(ext_from):
ttext = annot_data.iloc[0][ext_from]
entity_list = []
# print(ttext)
text_ranges = []
for annot in annot_data.itertuples():
start = int(annot.Start)
end = int(annot.End)
if start not in text_ranges and end not in text_ranges:
text_ranges.extend(list(range(start, end+1,)))
entity_list.append((annot.Start, annot.End, annot.Entity))
train_data.append((ttext, {'entities':entity_list}))
return train_dataShow code
abst_annots = extract_annot_data(abstract_annots, 'Abstract')
title_annots = extract_annot_data(title_annots, 'Title')Show code
abst_annots[0]('(-)-Carvone is a monoterpene ketone found in spearmint (Mentha spicata var. crispa) essential oil that is widely used as an odor and flavor additive. An intestinal antispasmodic effect was recently reported for (-)-carvone, and it has been shown to be more potent than its (+)-antipode. The mechanism of (-)-carvone action in the intestines has not been investigated. To gain a better understanding of the (-)-carvone antispasmodic effect, we investigated its pharmacological effects in the guinea pig ileum. Terminal portions of the ileum were mounted for isotonic contraction recordings. The effect of (-)-carvone was compared with that of the classical calcium channel blocker (CCB) verapamil. In isolated ileal smooth muscle, (-)-carvone did not produce direct contractile or relaxation responses and did not modify electrically elicited contractions or low K(+)-evoked contractions. The submaximal contractions induced by histamine (p<0.001), BaCl2 (p<0.05), and carbachol (p<0.01) were significantly reduced by (-)-carvone. The contractile response elicited by high concentrations of carbachol was reduced but not abolished by (-)-carvone. No additive action was detected with co-incubation of (-)-carvone and verapamil on carbachol-induced contraction. (-)-Carvone reduced the contraction induced by high K(+) and was almost 100 times more potent than verapamil. Thus, (-)-carvone showed a typical and potent CCB-like action. Many effects described for both (-)-carvone and spearmint oil can be explained as a CCB-like mode of action.',
{'entities': [(1090.0, 1099.0, 'TRIVIAL'),
(1133.0, 1144.0, 'TRIVIAL'),
(1200.0, 1211.0, 'TRIVIAL'),
(1216.0, 1225.0, 'TRIVIAL'),
(1229.0, 1238.0, 'TRIVIAL'),
(1359.0, 1368.0, 'TRIVIAL'),
(1312.0, 1316.0, 'FORMULA'),
(968.0, 977.0, 'TRIVIAL'),
(1376.0, 1387.0, 'TRIVIAL'),
(1465.0, 1476.0, 'TRIVIAL'),
(1260.0, 1271.0, 'TRIVIAL'),
(948.0, 953.0, 'FORMULA'),
(1017.0, 1028.0, 'TRIVIAL'),
(862.0, 866.0, 'FORMULA'),
(927.0, 936.0, 'TRIVIAL'),
(0.0, 11.0, 'TRIVIAL'),
(17.0, 35.0, 'FAMILY'),
(211.0, 222.0, 'TRIVIAL'),
(304.0, 315.0, 'TRIVIAL'),
(604.0, 615.0, 'TRIVIAL'),
(656.0, 663.0, 'SYSTEMATIC'),
(686.0, 695.0, 'TRIVIAL'),
(730.0, 741.0, 'TRIVIAL'),
(406.0, 417.0, 'TRIVIAL')]})A sample text from the corpus
Show code
ttext'(-)-Carvone is a monoterpene ketone found in spearmint (Mentha spicata var. crispa) essential oil that is widely used as an odor and flavor additive. An intestinal antispasmodic effect was recently reported for (-)-carvone, and it has been shown to be more potent than its (+)-antipode. The mechanism of (-)-carvone action in the intestines has not been investigated. To gain a better understanding of the (-)-carvone antispasmodic effect, we investigated its pharmacological effects in the guinea pig ileum. Terminal portions of the ileum were mounted for isotonic contraction recordings. The effect of (-)-carvone was compared with that of the classical calcium channel blocker (CCB) verapamil. In isolated ileal smooth muscle, (-)-carvone did not produce direct contractile or relaxation responses and did not modify electrically elicited contractions or low K(+)-evoked contractions. The submaximal contractions induced by histamine (p<0.001), BaCl2 (p<0.05), and carbachol (p<0.01) were significantly reduced by (-)-carvone. The contractile response elicited by high concentrations of carbachol was reduced but not abolished by (-)-carvone. No additive action was detected with co-incubation of (-)-carvone and verapamil on carbachol-induced contraction. (-)-Carvone reduced the contraction induced by high K(+) and was almost 100 times more potent than verapamil. Thus, (-)-carvone showed a typical and potent CCB-like action. Many effects described for both (-)-carvone and spearmint oil can be explained as a CCB-like mode of action.'Show code
from spacy.gold import docs_to_json, GoldParseA sample of the spacy json format for training models
Show code
docs_to_json([mod(ttext)])Display Output
{'id': 0,
'paragraphs': [{'cats': [],
'raw': '(-)-Carvone is a monoterpene ketone found in spearmint (Mentha spicata var. crispa) essential oil that is widely used as an odor and flavor additive. An intestinal antispasmodic effect was recently reported for (-)-carvone, and it has been shown to be more potent than its (+)-antipode. The mechanism of (-)-carvone action in the intestines has not been investigated. To gain a better understanding of the (-)-carvone antispasmodic effect, we investigated its pharmacological effects in the guinea pig ileum. Terminal portions of the ileum were mounted for isotonic contraction recordings. The effect of (-)-carvone was compared with that of the classical calcium channel blocker (CCB) verapamil. In isolated ileal smooth muscle, (-)-carvone did not produce direct contractile or relaxation responses and did not modify electrically elicited contractions or low K(+)-evoked contractions. The submaximal contractions induced by histamine (p<0.001), BaCl2 (p<0.05), and carbachol (p<0.01) were significantly reduced by (-)-carvone. The contractile response elicited by high concentrations of carbachol was reduced but not abolished by (-)-carvone. No additive action was detected with co-incubation of (-)-carvone and verapamil on carbachol-induced contraction. (-)-Carvone reduced the contraction induced by high K(+) and was almost 100 times more potent than verapamil. Thus, (-)-carvone showed a typical and potent CCB-like action. Many effects described for both (-)-carvone and spearmint oil can be explained as a CCB-like mode of action.',
'sentences': [{'brackets': [],
'tokens': [{'dep': 'nsubj',
'head': 4,
'id': 0,
'ner': 'O',
'orth': '(-)-Carvone',
'tag': 'NN'},
{'dep': 'cop',
'head': 3,
'id': 1,
'ner': 'O',
'orth': 'is',
'tag': 'VBZ'},
{'dep': 'det', 'head': 2, 'id': 2, 'ner': 'O', 'orth': 'a', 'tag': 'DT'},
{'dep': 'compound',
'head': 1,
'id': 3,
'ner': 'O',
'orth': 'monoterpene',
'tag': 'NN'},
{'dep': 'ROOT',
'head': 0,
'id': 4,
'ner': 'O',
'orth': 'ketone',
'tag': 'NN'},
{'dep': 'acl',
'head': -1,
'id': 5,
'ner': 'O',
'orth': 'found',
'tag': 'VBN'},
{'dep': 'case',
'head': 1,
'id': 6,
'ner': 'O',
'orth': 'in',
'tag': 'IN'},
{'dep': 'nmod',
'head': -2,
'id': 7,
'ner': 'O',
'orth': 'spearmint',
'tag': 'NN'},
{'dep': 'punct',
'head': 3,
'id': 8,
'ner': 'O',
'orth': '(',
'tag': '-LRB-'},
{'dep': 'compound',
'head': 2,
'id': 9,
'ner': 'O',
'orth': 'Mentha',
'tag': 'FW'},
{'dep': 'compound',
'head': 1,
'id': 10,
'ner': 'O',
'orth': 'spicata',
'tag': 'FW'},
{'dep': 'dep',
'head': -4,
'id': 11,
'ner': 'O',
'orth': 'var',
'tag': 'FW'},
{'dep': 'punct',
'head': -8,
'id': 12,
'ner': 'O',
'orth': '.',
'tag': '.'}]},
{'brackets': [],
'tokens': [{'dep': 'ROOT',
'head': 0,
'id': 13,
'ner': 'O',
'orth': 'crispa',
'tag': 'NN'},
{'dep': 'punct',
'head': -1,
'id': 14,
'ner': 'O',
'orth': ')',
'tag': '-RRB-'},
{'dep': 'amod',
'head': 1,
'id': 15,
'ner': 'O',
'orth': 'essential',
'tag': 'JJ'},
{'dep': 'appos',
'head': -3,
'id': 16,
'ner': 'O',
'orth': 'oil',
'tag': 'NN'},
{'dep': 'nsubjpass',
'head': 3,
'id': 17,
'ner': 'O',
'orth': 'that',
'tag': 'WDT'},
{'dep': 'auxpass',
'head': 2,
'id': 18,
'ner': 'O',
'orth': 'is',
'tag': 'VBZ'},
{'dep': 'advmod',
'head': 1,
'id': 19,
'ner': 'O',
'orth': 'widely',
'tag': 'RB'},
{'dep': 'acl:relcl',
'head': -4,
'id': 20,
'ner': 'O',
'orth': 'used',
'tag': 'VBN'},
{'dep': 'case',
'head': 5,
'id': 21,
'ner': 'O',
'orth': 'as',
'tag': 'IN'},
{'dep': 'det',
'head': 4,
'id': 22,
'ner': 'O',
'orth': 'an',
'tag': 'DT'},
{'dep': 'compound',
'head': 3,
'id': 23,
'ner': 'O',
'orth': 'odor',
'tag': 'NN'},
{'dep': 'cc',
'head': -1,
'id': 24,
'ner': 'O',
'orth': 'and',
'tag': 'CC'},
{'dep': 'conj',
'head': -2,
'id': 25,
'ner': 'O',
'orth': 'flavor',
'tag': 'NN'},
{'dep': 'nmod',
'head': -6,
'id': 26,
'ner': 'O',
'orth': 'additive',
'tag': 'JJ'},
{'dep': 'punct',
'head': -14,
'id': 27,
'ner': 'O',
'orth': '.',
'tag': '.'}]},
{'brackets': [],
'tokens': [{'dep': 'det',
'head': 3,
'id': 28,
'ner': 'O',
'orth': 'An',
'tag': 'DT'},
{'dep': 'amod',
'head': 2,
'id': 29,
'ner': 'O',
'orth': 'intestinal',
'tag': 'JJ'},
{'dep': 'amod',
'head': 1,
'id': 30,
'ner': 'O',
'orth': 'antispasmodic',
'tag': 'JJ'},
{'dep': 'nsubjpass',
'head': 3,
'id': 31,
'ner': 'O',
'orth': 'effect',
'tag': 'NN'},
{'dep': 'auxpass',
'head': 2,
'id': 32,
'ner': 'O',
'orth': 'was',
'tag': 'VBD'},
{'dep': 'advmod',
'head': 1,
'id': 33,
'ner': 'O',
'orth': 'recently',
'tag': 'RB'},
{'dep': 'ROOT',
'head': 0,
'id': 34,
'ner': 'O',
'orth': 'reported',
'tag': 'VBN'},
{'dep': 'case',
'head': 1,
'id': 35,
'ner': 'O',
'orth': 'for',
'tag': 'IN'},
{'dep': 'nmod',
'head': -2,
'id': 36,
'ner': 'O',
'orth': '(-)-carvone',
'tag': 'NN'},
{'dep': 'punct',
'head': -3,
'id': 37,
'ner': 'O',
'orth': ',',
'tag': ','},
{'dep': 'cc',
'head': -4,
'id': 38,
'ner': 'O',
'orth': 'and',
'tag': 'CC'},
{'dep': 'nsubjpass',
'head': 3,
'id': 39,
'ner': 'O',
'orth': 'it',
'tag': 'PRP'},
{'dep': 'aux',
'head': 2,
'id': 40,
'ner': 'O',
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'ner': 'O',
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'ner': 'O',
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'tag': 'IN'},
{'dep': 'amod',
'head': 1,
'id': 208,
'ner': 'O',
'orth': 'high',
'tag': 'JJ'},
{'dep': 'nmod',
'head': -3,
'id': 209,
'ner': 'O',
'orth': 'K(+',
'tag': 'NN'},
{'dep': 'punct',
'head': -7,
'id': 210,
'ner': 'O',
'orth': ')',
'tag': '-RRB-'},
{'dep': 'cc',
'head': -8,
'id': 211,
'ner': 'O',
'orth': 'and',
'tag': 'CC'},
{'dep': 'cop',
'head': 5,
'id': 212,
'ner': 'O',
'orth': 'was',
'tag': 'VBD'},
{'dep': 'advmod',
'head': 1,
'id': 213,
'ner': 'O',
'orth': 'almost',
'tag': 'RB'},
{'dep': 'nummod',
'head': 1,
'id': 214,
'ner': 'O',
'orth': '100',
'tag': 'CD'},
{'dep': 'nmod:npmod',
'head': 1,
'id': 215,
'ner': 'O',
'orth': 'times',
'tag': 'NNS'},
{'dep': 'advmod',
'head': 1,
'id': 216,
'ner': 'O',
'orth': 'more',
'tag': 'RBR'},
{'dep': 'conj',
'head': -14,
'id': 217,
'ner': 'O',
'orth': 'potent',
'tag': 'JJ'},
{'dep': 'case',
'head': 1,
'id': 218,
'ner': 'O',
'orth': 'than',
'tag': 'IN'},
{'dep': 'nmod',
'head': -2,
'id': 219,
'ner': 'O',
'orth': 'verapamil',
'tag': 'NN'},
{'dep': 'punct',
'head': -17,
'id': 220,
'ner': 'O',
'orth': '.',
'tag': '.'}]},
{'brackets': [],
'tokens': [{'dep': 'advmod',
'head': 3,
'id': 221,
'ner': 'O',
'orth': 'Thus',
'tag': 'RB'},
{'dep': 'punct',
'head': 2,
'id': 222,
'ner': 'O',
'orth': ',',
'tag': ','},
{'dep': 'nsubj',
'head': 1,
'id': 223,
'ner': 'O',
'orth': '(-)-carvone',
'tag': 'NN'},
{'dep': 'ROOT',
'head': 0,
'id': 224,
'ner': 'O',
'orth': 'showed',
'tag': 'VBD'},
{'dep': 'det',
'head': 5,
'id': 225,
'ner': 'O',
'orth': 'a',
'tag': 'DT'},
{'dep': 'amod',
'head': 4,
'id': 226,
'ner': 'O',
'orth': 'typical',
'tag': 'JJ'},
{'dep': 'cc',
'head': -1,
'id': 227,
'ner': 'O',
'orth': 'and',
'tag': 'CC'},
{'dep': 'conj',
'head': -2,
'id': 228,
'ner': 'O',
'orth': 'potent',
'tag': 'JJ'},
{'dep': 'amod',
'head': 1,
'id': 229,
'ner': 'O',
'orth': 'CCB-like',
'tag': 'JJ'},
{'dep': 'dobj',
'head': -6,
'id': 230,
'ner': 'O',
'orth': 'action',
'tag': 'NN'},
{'dep': 'punct',
'head': -7,
'id': 231,
'ner': 'O',
'orth': '.',
'tag': '.'}]},
{'brackets': [],
'tokens': [{'dep': 'amod',
'head': 1,
'id': 232,
'ner': 'O',
'orth': 'Many',
'tag': 'JJ'},
{'dep': 'nsubjpass',
'head': 10,
'id': 233,
'ner': 'O',
'orth': 'effects',
'tag': 'NNS'},
{'dep': 'acl',
'head': -1,
'id': 234,
'ner': 'O',
'orth': 'described',
'tag': 'VBN'},
{'dep': 'case',
'head': 5,
'id': 235,
'ner': 'O',
'orth': 'for',
'tag': 'IN'},
{'dep': 'cc:preconj',
'head': 1,
'id': 236,
'ner': 'O',
'orth': 'both',
'tag': 'CC'},
{'dep': 'compound',
'head': 3,
'id': 237,
'ner': 'O',
'orth': '(-)-carvone',
'tag': 'NN'},
{'dep': 'cc',
'head': -1,
'id': 238,
'ner': 'O',
'orth': 'and',
'tag': 'CC'},
{'dep': 'conj',
'head': -2,
'id': 239,
'ner': 'O',
'orth': 'spearmint',
'tag': 'NN'},
{'dep': 'nmod',
'head': -6,
'id': 240,
'ner': 'O',
'orth': 'oil',
'tag': 'NN'},
{'dep': 'aux',
'head': 2,
'id': 241,
'ner': 'O',
'orth': 'can',
'tag': 'MD'},
{'dep': 'auxpass',
'head': 1,
'id': 242,
'ner': 'O',
'orth': 'be',
'tag': 'VB'},
{'dep': 'ROOT',
'head': 0,
'id': 243,
'ner': 'O',
'orth': 'explained',
'tag': 'VBN'},
{'dep': 'case',
'head': 3,
'id': 244,
'ner': 'O',
'orth': 'as',
'tag': 'IN'},
{'dep': 'det',
'head': 2,
'id': 245,
'ner': 'O',
'orth': 'a',
'tag': 'DT'},
{'dep': 'amod',
'head': 1,
'id': 246,
'ner': 'O',
'orth': 'CCB-like',
'tag': 'JJ'},
{'dep': 'nmod',
'head': -4,
'id': 247,
'ner': 'O',
'orth': 'mode',
'tag': 'NN'},
{'dep': 'case',
'head': 1,
'id': 248,
'ner': 'O',
'orth': 'of',
'tag': 'IN'},
{'dep': 'nmod',
'head': -2,
'id': 249,
'ner': 'O',
'orth': 'action',
'tag': 'NN'},
{'dep': 'punct',
'head': -7,
'id': 250,
'ner': 'O',
'orth': '.',
'tag': '.'}]}]}]}