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Parent(s):
98ac1da
upload-nltk-data
Browse files- app.py +12 -11
- nltk_data/punkt/PY3/README +98 -0
- nltk_data/punkt/PY3/english.pickle +0 -0
- nltk_data/punkt/README +98 -0
- nltk_data/punkt/english.pickle +0 -0
- nltk_data/stopwords/README +32 -0
- nltk_data/stopwords/english +179 -0
app.py
CHANGED
@@ -1,6 +1,7 @@
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import nltk
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nltk.
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nltk.download('
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import pandas as pd
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import classify_abs
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import extract_abs
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@@ -20,7 +21,7 @@ st.markdown('''<img src="https://huggingface.co/spaces/ncats/EpiPipeline4GARD/re
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#### TITLE ####
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st.title("Epidemiology Information Extraction Pipeline for Rare Diseases")
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#st.subheader("National Center for Advancing Translational Sciences (NIH/NCATS)")
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#### CHANGE SIDEBAR WIDTH ###
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st.markdown(
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@@ -58,21 +59,21 @@ def load_models_experimental():
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NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
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GARD_dict, max_length = extract_abs.load_GARD_diseases()
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return classify_model_vars, NER_pipeline, entity_classes, GARD_dict, max_length
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-
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#### DOWNLOAD FUNCTION ####
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@st.cache
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def convert_df(df):
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# IMPORTANT: Cache the conversion to prevent computation on every rerun
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return df.to_csv().encode('utf-8')
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-
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#### SANKEY FUNCTION ####
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#@st.cache(allow_output_mutation=True)
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@st.experimental_singleton()
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def epi_sankey(sankey_data, disease_or_gard_id):
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gathered, relevant, epidemiologic = sankey_data
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-
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fig = go.Figure(data=[go.Sankey(
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node = dict(
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pad = 15,
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@@ -92,7 +93,7 @@ def epi_sankey(sankey_data, disease_or_gard_id):
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title="Search for the Epidemiology of "+disease_or_gard_id,
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font=dict(size = 10, color = 'black'),
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)
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-
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return fig
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#### BEGIN APP ####
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if disease_or_gard_id:
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df, sankey_data = extract_abs.streamlit_extraction(disease_or_gard_id, max_results, filtering,
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NER_pipeline, entity_classes,
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extract_diseases,GARD_dict, max_length,
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classify_model_vars)
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st.dataframe(df, height=100)
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csv = convert_df(df)
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)
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#st.dataframe(data=None, width=None, height=None)
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fig = epi_sankey(sankey_data,disease_or_gard_id)
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#if st.button('Display Sankey Diagram of Automated Search'):
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st.plotly_chart(fig, use_container_width=True)
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# st.code(body, language="python")
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import nltk
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nltk.data.path.append("./nltk_data/")
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#nltk.download('stopwords')
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#nltk.download('punkt')
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import pandas as pd
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import classify_abs
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import extract_abs
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#### TITLE ####
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st.title("Epidemiology Information Extraction Pipeline for Rare Diseases")
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#st.subheader("National Center for Advancing Translational Sciences (NIH/NCATS)")
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#### CHANGE SIDEBAR WIDTH ###
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st.markdown(
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NER_pipeline, entity_classes = extract_abs.init_NER_pipeline()
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GARD_dict, max_length = extract_abs.load_GARD_diseases()
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return classify_model_vars, NER_pipeline, entity_classes, GARD_dict, max_length
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+
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#### DOWNLOAD FUNCTION ####
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@st.cache
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def convert_df(df):
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# IMPORTANT: Cache the conversion to prevent computation on every rerun
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return df.to_csv().encode('utf-8')
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+
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#### SANKEY FUNCTION ####
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#@st.cache(allow_output_mutation=True)
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@st.experimental_singleton()
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def epi_sankey(sankey_data, disease_or_gard_id):
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gathered, relevant, epidemiologic = sankey_data
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fig = go.Figure(data=[go.Sankey(
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node = dict(
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pad = 15,
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title="Search for the Epidemiology of "+disease_or_gard_id,
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font=dict(size = 10, color = 'black'),
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)
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return fig
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#### BEGIN APP ####
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if disease_or_gard_id:
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df, sankey_data = extract_abs.streamlit_extraction(disease_or_gard_id, max_results, filtering,
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NER_pipeline, entity_classes,
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extract_diseases,GARD_dict, max_length,
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classify_model_vars)
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st.dataframe(df, height=100)
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csv = convert_df(df)
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)
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#st.dataframe(data=None, width=None, height=None)
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fig = epi_sankey(sankey_data,disease_or_gard_id)
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#if st.button('Display Sankey Diagram of Automated Search'):
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st.plotly_chart(fig, use_container_width=True)
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# st.code(body, language="python")
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nltk_data/punkt/PY3/README
ADDED
@@ -0,0 +1,98 @@
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Pretrained Punkt Models -- Jan Strunk (New version trained after issues 313 and 514 had been corrected)
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Most models were prepared using the test corpora from Kiss and Strunk (2006). Additional models have
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been contributed by various people using NLTK for sentence boundary detection.
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For information about how to use these models, please confer the tokenization HOWTO:
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http://nltk.googlecode.com/svn/trunk/doc/howto/tokenize.html
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and chapter 3.8 of the NLTK book:
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http://nltk.googlecode.com/svn/trunk/doc/book/ch03.html#sec-segmentation
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There are pretrained tokenizers for the following languages:
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File Language Source Contents Size of training corpus(in tokens) Model contributed by
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=======================================================================================================================================================================
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czech.pickle Czech Multilingual Corpus 1 (ECI) Lidove Noviny ~345,000 Jan Strunk / Tibor Kiss
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Literarni Noviny
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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danish.pickle Danish Avisdata CD-Rom Ver. 1.1. 1995 Berlingske Tidende ~550,000 Jan Strunk / Tibor Kiss
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(Berlingske Avisdata, Copenhagen) Weekend Avisen
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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dutch.pickle Dutch Multilingual Corpus 1 (ECI) De Limburger ~340,000 Jan Strunk / Tibor Kiss
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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english.pickle English Penn Treebank (LDC) Wall Street Journal ~469,000 Jan Strunk / Tibor Kiss
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(American)
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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estonian.pickle Estonian University of Tartu, Estonia Eesti Ekspress ~359,000 Jan Strunk / Tibor Kiss
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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finnish.pickle Finnish Finnish Parole Corpus, Finnish Books and major national ~364,000 Jan Strunk / Tibor Kiss
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Text Bank (Suomen Kielen newspapers
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Tekstipankki)
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Finnish Center for IT Science
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(CSC)
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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french.pickle French Multilingual Corpus 1 (ECI) Le Monde ~370,000 Jan Strunk / Tibor Kiss
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(European)
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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german.pickle German Neue Zürcher Zeitung AG Neue Zürcher Zeitung ~847,000 Jan Strunk / Tibor Kiss
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(Switzerland) CD-ROM
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(Uses "ss"
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instead of "ß")
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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greek.pickle Greek Efstathios Stamatatos To Vima (TO BHMA) ~227,000 Jan Strunk / Tibor Kiss
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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italian.pickle Italian Multilingual Corpus 1 (ECI) La Stampa, Il Mattino ~312,000 Jan Strunk / Tibor Kiss
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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norwegian.pickle Norwegian Centre for Humanities Bergens Tidende ~479,000 Jan Strunk / Tibor Kiss
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(Bokmål and Information Technologies,
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Nynorsk) Bergen
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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polish.pickle Polish Polish National Corpus Literature, newspapers, etc. ~1,000,000 Krzysztof Langner
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(http://www.nkjp.pl/)
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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portuguese.pickle Portuguese CETENFolha Corpus Folha de São Paulo ~321,000 Jan Strunk / Tibor Kiss
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(Brazilian) (Linguateca)
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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slovene.pickle Slovene TRACTOR Delo ~354,000 Jan Strunk / Tibor Kiss
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Slovene Academy for Arts
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and Sciences
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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spanish.pickle Spanish Multilingual Corpus 1 (ECI) Sur ~353,000 Jan Strunk / Tibor Kiss
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+
(European)
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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swedish.pickle Swedish Multilingual Corpus 1 (ECI) Dagens Nyheter ~339,000 Jan Strunk / Tibor Kiss
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(and some other texts)
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+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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+
turkish.pickle Turkish METU Turkish Corpus Milliyet ~333,000 Jan Strunk / Tibor Kiss
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(Türkçe Derlem Projesi)
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University of Ankara
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-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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+
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The corpora contained about 400,000 tokens on average and mostly consisted of newspaper text converted to
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+
Unicode using the codecs module.
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73 |
+
|
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+
Kiss, Tibor and Strunk, Jan (2006): Unsupervised Multilingual Sentence Boundary Detection.
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Computational Linguistics 32: 485-525.
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---- Training Code ----
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# import punkt
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import nltk.tokenize.punkt
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# Make a new Tokenizer
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tokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()
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# Read in training corpus (one example: Slovene)
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import codecs
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text = codecs.open("slovene.plain","Ur","iso-8859-2").read()
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# Train tokenizer
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tokenizer.train(text)
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# Dump pickled tokenizer
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import pickle
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out = open("slovene.pickle","wb")
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pickle.dump(tokenizer, out)
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out.close()
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---------
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nltk_data/punkt/PY3/english.pickle
ADDED
Binary file (407 kB). View file
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nltk_data/punkt/README
ADDED
@@ -0,0 +1,98 @@
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1 |
+
Pretrained Punkt Models -- Jan Strunk (New version trained after issues 313 and 514 had been corrected)
|
2 |
+
|
3 |
+
Most models were prepared using the test corpora from Kiss and Strunk (2006). Additional models have
|
4 |
+
been contributed by various people using NLTK for sentence boundary detection.
|
5 |
+
|
6 |
+
For information about how to use these models, please confer the tokenization HOWTO:
|
7 |
+
http://nltk.googlecode.com/svn/trunk/doc/howto/tokenize.html
|
8 |
+
and chapter 3.8 of the NLTK book:
|
9 |
+
http://nltk.googlecode.com/svn/trunk/doc/book/ch03.html#sec-segmentation
|
10 |
+
|
11 |
+
There are pretrained tokenizers for the following languages:
|
12 |
+
|
13 |
+
File Language Source Contents Size of training corpus(in tokens) Model contributed by
|
14 |
+
=======================================================================================================================================================================
|
15 |
+
czech.pickle Czech Multilingual Corpus 1 (ECI) Lidove Noviny ~345,000 Jan Strunk / Tibor Kiss
|
16 |
+
Literarni Noviny
|
17 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
18 |
+
danish.pickle Danish Avisdata CD-Rom Ver. 1.1. 1995 Berlingske Tidende ~550,000 Jan Strunk / Tibor Kiss
|
19 |
+
(Berlingske Avisdata, Copenhagen) Weekend Avisen
|
20 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
21 |
+
dutch.pickle Dutch Multilingual Corpus 1 (ECI) De Limburger ~340,000 Jan Strunk / Tibor Kiss
|
22 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
23 |
+
english.pickle English Penn Treebank (LDC) Wall Street Journal ~469,000 Jan Strunk / Tibor Kiss
|
24 |
+
(American)
|
25 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
26 |
+
estonian.pickle Estonian University of Tartu, Estonia Eesti Ekspress ~359,000 Jan Strunk / Tibor Kiss
|
27 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
28 |
+
finnish.pickle Finnish Finnish Parole Corpus, Finnish Books and major national ~364,000 Jan Strunk / Tibor Kiss
|
29 |
+
Text Bank (Suomen Kielen newspapers
|
30 |
+
Tekstipankki)
|
31 |
+
Finnish Center for IT Science
|
32 |
+
(CSC)
|
33 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
34 |
+
french.pickle French Multilingual Corpus 1 (ECI) Le Monde ~370,000 Jan Strunk / Tibor Kiss
|
35 |
+
(European)
|
36 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
37 |
+
german.pickle German Neue Zürcher Zeitung AG Neue Zürcher Zeitung ~847,000 Jan Strunk / Tibor Kiss
|
38 |
+
(Switzerland) CD-ROM
|
39 |
+
(Uses "ss"
|
40 |
+
instead of "ß")
|
41 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
42 |
+
greek.pickle Greek Efstathios Stamatatos To Vima (TO BHMA) ~227,000 Jan Strunk / Tibor Kiss
|
43 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
44 |
+
italian.pickle Italian Multilingual Corpus 1 (ECI) La Stampa, Il Mattino ~312,000 Jan Strunk / Tibor Kiss
|
45 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
46 |
+
norwegian.pickle Norwegian Centre for Humanities Bergens Tidende ~479,000 Jan Strunk / Tibor Kiss
|
47 |
+
(Bokmål and Information Technologies,
|
48 |
+
Nynorsk) Bergen
|
49 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
50 |
+
polish.pickle Polish Polish National Corpus Literature, newspapers, etc. ~1,000,000 Krzysztof Langner
|
51 |
+
(http://www.nkjp.pl/)
|
52 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
53 |
+
portuguese.pickle Portuguese CETENFolha Corpus Folha de São Paulo ~321,000 Jan Strunk / Tibor Kiss
|
54 |
+
(Brazilian) (Linguateca)
|
55 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
56 |
+
slovene.pickle Slovene TRACTOR Delo ~354,000 Jan Strunk / Tibor Kiss
|
57 |
+
Slovene Academy for Arts
|
58 |
+
and Sciences
|
59 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
60 |
+
spanish.pickle Spanish Multilingual Corpus 1 (ECI) Sur ~353,000 Jan Strunk / Tibor Kiss
|
61 |
+
(European)
|
62 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
63 |
+
swedish.pickle Swedish Multilingual Corpus 1 (ECI) Dagens Nyheter ~339,000 Jan Strunk / Tibor Kiss
|
64 |
+
(and some other texts)
|
65 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
66 |
+
turkish.pickle Turkish METU Turkish Corpus Milliyet ~333,000 Jan Strunk / Tibor Kiss
|
67 |
+
(Türkçe Derlem Projesi)
|
68 |
+
University of Ankara
|
69 |
+
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
70 |
+
|
71 |
+
The corpora contained about 400,000 tokens on average and mostly consisted of newspaper text converted to
|
72 |
+
Unicode using the codecs module.
|
73 |
+
|
74 |
+
Kiss, Tibor and Strunk, Jan (2006): Unsupervised Multilingual Sentence Boundary Detection.
|
75 |
+
Computational Linguistics 32: 485-525.
|
76 |
+
|
77 |
+
---- Training Code ----
|
78 |
+
|
79 |
+
# import punkt
|
80 |
+
import nltk.tokenize.punkt
|
81 |
+
|
82 |
+
# Make a new Tokenizer
|
83 |
+
tokenizer = nltk.tokenize.punkt.PunktSentenceTokenizer()
|
84 |
+
|
85 |
+
# Read in training corpus (one example: Slovene)
|
86 |
+
import codecs
|
87 |
+
text = codecs.open("slovene.plain","Ur","iso-8859-2").read()
|
88 |
+
|
89 |
+
# Train tokenizer
|
90 |
+
tokenizer.train(text)
|
91 |
+
|
92 |
+
# Dump pickled tokenizer
|
93 |
+
import pickle
|
94 |
+
out = open("slovene.pickle","wb")
|
95 |
+
pickle.dump(tokenizer, out)
|
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+
out.close()
|
97 |
+
|
98 |
+
---------
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nltk_data/punkt/english.pickle
ADDED
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nltk_data/stopwords/README
ADDED
@@ -0,0 +1,32 @@
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1 |
+
Stopwords Corpus
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2 |
+
|
3 |
+
This corpus contains lists of stop words for several languages. These
|
4 |
+
are high-frequency grammatical words which are usually ignored in text
|
5 |
+
retrieval applications.
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6 |
+
|
7 |
+
They were obtained from:
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+
http://anoncvs.postgresql.org/cvsweb.cgi/pgsql/src/backend/snowball/stopwords/
|
9 |
+
|
10 |
+
The stop words for the Romanian language were obtained from:
|
11 |
+
http://arlc.ro/resources/
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+
|
13 |
+
The English list has been augmented
|
14 |
+
https://github.com/nltk/nltk_data/issues/22
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+
|
16 |
+
The German list has been corrected
|
17 |
+
https://github.com/nltk/nltk_data/pull/49
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+
|
19 |
+
A Kazakh list has been added
|
20 |
+
https://github.com/nltk/nltk_data/pull/52
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+
|
22 |
+
A Nepali list has been added
|
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+
https://github.com/nltk/nltk_data/pull/83
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+
|
25 |
+
An Azerbaijani list has been added
|
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+
https://github.com/nltk/nltk_data/pull/100
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27 |
+
|
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+
A Greek list has been added
|
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+
https://github.com/nltk/nltk_data/pull/103
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+
|
31 |
+
An Indonesian list has been added
|
32 |
+
https://github.com/nltk/nltk_data/pull/112
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nltk_data/stopwords/english
ADDED
@@ -0,0 +1,179 @@
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1 |
+
i
|
2 |
+
me
|
3 |
+
my
|
4 |
+
myself
|
5 |
+
we
|
6 |
+
our
|
7 |
+
ours
|
8 |
+
ourselves
|
9 |
+
you
|
10 |
+
you're
|
11 |
+
you've
|
12 |
+
you'll
|
13 |
+
you'd
|
14 |
+
your
|
15 |
+
yours
|
16 |
+
yourself
|
17 |
+
yourselves
|
18 |
+
he
|
19 |
+
him
|
20 |
+
his
|
21 |
+
himself
|
22 |
+
she
|
23 |
+
she's
|
24 |
+
her
|
25 |
+
hers
|
26 |
+
herself
|
27 |
+
it
|
28 |
+
it's
|
29 |
+
its
|
30 |
+
itself
|
31 |
+
they
|
32 |
+
them
|
33 |
+
their
|
34 |
+
theirs
|
35 |
+
themselves
|
36 |
+
what
|
37 |
+
which
|
38 |
+
who
|
39 |
+
whom
|
40 |
+
this
|
41 |
+
that
|
42 |
+
that'll
|
43 |
+
these
|
44 |
+
those
|
45 |
+
am
|
46 |
+
is
|
47 |
+
are
|
48 |
+
was
|
49 |
+
were
|
50 |
+
be
|
51 |
+
been
|
52 |
+
being
|
53 |
+
have
|
54 |
+
has
|
55 |
+
had
|
56 |
+
having
|
57 |
+
do
|
58 |
+
does
|
59 |
+
did
|
60 |
+
doing
|
61 |
+
a
|
62 |
+
an
|
63 |
+
the
|
64 |
+
and
|
65 |
+
but
|
66 |
+
if
|
67 |
+
or
|
68 |
+
because
|
69 |
+
as
|
70 |
+
until
|
71 |
+
while
|
72 |
+
of
|
73 |
+
at
|
74 |
+
by
|
75 |
+
for
|
76 |
+
with
|
77 |
+
about
|
78 |
+
against
|
79 |
+
between
|
80 |
+
into
|
81 |
+
through
|
82 |
+
during
|
83 |
+
before
|
84 |
+
after
|
85 |
+
above
|
86 |
+
below
|
87 |
+
to
|
88 |
+
from
|
89 |
+
up
|
90 |
+
down
|
91 |
+
in
|
92 |
+
out
|
93 |
+
on
|
94 |
+
off
|
95 |
+
over
|
96 |
+
under
|
97 |
+
again
|
98 |
+
further
|
99 |
+
then
|
100 |
+
once
|
101 |
+
here
|
102 |
+
there
|
103 |
+
when
|
104 |
+
where
|
105 |
+
why
|
106 |
+
how
|
107 |
+
all
|
108 |
+
any
|
109 |
+
both
|
110 |
+
each
|
111 |
+
few
|
112 |
+
more
|
113 |
+
most
|
114 |
+
other
|
115 |
+
some
|
116 |
+
such
|
117 |
+
no
|
118 |
+
nor
|
119 |
+
not
|
120 |
+
only
|
121 |
+
own
|
122 |
+
same
|
123 |
+
so
|
124 |
+
than
|
125 |
+
too
|
126 |
+
very
|
127 |
+
s
|
128 |
+
t
|
129 |
+
can
|
130 |
+
will
|
131 |
+
just
|
132 |
+
don
|
133 |
+
don't
|
134 |
+
should
|
135 |
+
should've
|
136 |
+
now
|
137 |
+
d
|
138 |
+
ll
|
139 |
+
m
|
140 |
+
o
|
141 |
+
re
|
142 |
+
ve
|
143 |
+
y
|
144 |
+
ain
|
145 |
+
aren
|
146 |
+
aren't
|
147 |
+
couldn
|
148 |
+
couldn't
|
149 |
+
didn
|
150 |
+
didn't
|
151 |
+
doesn
|
152 |
+
doesn't
|
153 |
+
hadn
|
154 |
+
hadn't
|
155 |
+
hasn
|
156 |
+
hasn't
|
157 |
+
haven
|
158 |
+
haven't
|
159 |
+
isn
|
160 |
+
isn't
|
161 |
+
ma
|
162 |
+
mightn
|
163 |
+
mightn't
|
164 |
+
mustn
|
165 |
+
mustn't
|
166 |
+
needn
|
167 |
+
needn't
|
168 |
+
shan
|
169 |
+
shan't
|
170 |
+
shouldn
|
171 |
+
shouldn't
|
172 |
+
wasn
|
173 |
+
wasn't
|
174 |
+
weren
|
175 |
+
weren't
|
176 |
+
won
|
177 |
+
won't
|
178 |
+
wouldn
|
179 |
+
wouldn't
|