Designing your own experiment with a Jupyter Notebook
A Jupyter notebook (hereafter just notebook, for short) is a type of document that contains executable code interspersed with visualizations of code execution results and narrative text.
Below we provide an example notebook which you may use as a starting point for designing your own experiments using language resources offered by PORTULAN CLARIN.
Pre-requisites
To execute this notebook, you need an access key you can obtain by clicking the button below. A key is valid for 31 days. It allows to submit a total of 500 million characters by means of requests with no more 2000 characters each. It allows to enter 100,000 requests, at a rate of no more than 200 requests per hour.
For other usage regimes, you should contact the helpdesk.
The input data sent to any PORTULAN CLARIN web service and the respective output will be automatically deleted from our computers after being processed. However, when running a notebook on an external service, such as the ones suggested below, you should take their data privacy policies into consideration.
Running the notebook
You have three options to run the notebook presented below:
- Run on Binder — The Binder Project is funded by a 501c3 non-profit
organization and is described in detail in the following paper:
Jupyter et al., "Binder 2.0 - Reproducible, Interactive, Sharable Environments for Science at Scale."
Proceedings of the 17th Python in Science Conference. 2018. doi://10.25080/Majora-4af1f417-011 - Run on Google Colab — Google Colaboratory is a free-to-use product from Google Research.
- Download the notebook from our public Github repository and run it on your computer.
This is a more advanced option, which requires you to install Python 3 and Jupyter on your computer. For anyone without prior experience setting up a Python development environment, we strongly recommend one of the two options above.
Using LX-DepParser to parse sentences and displaying dependency tree graphs¶
This is an example notebook that illustrates how you can use the LX-DepParser web service to parse sentences and how to visualize dependency tree graphs in a notebook.
Before you run this example, replace access_key_goes_here by your webservice access key, below:
LXDEPPARSER_WS_API_KEY = 'access_key_goes_here'
LXDEPPARSER_WS_API_URL = 'https://portulanclarin.net/workbench/lx-depparser/api/'
Importing required Python modules¶
The next cell will take care of installing the requests and matplotlib packages,
if not already installed, and make them available to use in this notebook.
try:
import requests
except:
!pip3 install requests
import requests
try:
import pydependencygrapher
except:
# see https://github.com/pygobject/pycairo/issues/39#issuecomment-391830334
!apt-get install libcairo2-dev libjpeg-dev libgif-dev
!pip3 install pydependencygrapher
import pydependencygrapher
try:
import matplotlib.pyplot as plt
except:
!pip3 install matplotlib
import matplotlib.pyplot as plt
import base64
import collections
import IPython
Wrapping the complexities of the JSON-RPC API in a simple, easy to use function¶
The WSException class defined below, will be used later to identify errors
from the webservice.
class WSException(Exception):
'Webservice Exception'
def __init__(self, errordata):
"errordata is a dict returned by the webservice with details about the error"
super().__init__(self)
assert isinstance(errordata, dict)
self.message = errordata["message"]
# see https://json-rpc.readthedocs.io/en/latest/exceptions.html for more info
# about JSON-RPC error codes
if -32099 <= errordata["code"] <= -32000: # Server Error
if errordata["data"]["type"] == "WebServiceException":
self.message += f": {errordata['data']['message']}"
else:
self.message += f": {errordata['data']!r}"
def __str__(self):
return self.message
The next function invoques the LX-Suite webservice through it's public JSON-RPC API.
def parse(text, tagset, format):
'''
Arguments
text: a string with a maximum of 2000 characters, Portuguese text, with
the input to be processed
tagset: either 'CINTIL' or 'UD' (universal dependencies)
format: either 'CONLL' or 'JSON'
Returns a string with the output according to specification in
https://portulanclarin.net/workbench/lx-suite/
Raises a WSException if an error occurs.
'''
request_data = {
'method': 'parse',
'jsonrpc': '2.0',
'id': 0,
'params': {
'text': text,
'tagset': tagset,
'format': format,
'key': LXDEPPARSER_WS_API_KEY,
},
}
request = requests.post(LXDEPPARSER_WS_API_URL, json=request_data)
response_data = request.json()
if "error" in response_data:
raise WSException(response_data["error"])
else:
return response_data["result"]
Let us test the function we just defined:
text = '''Esta frase serve para testar o funcionamento do parser de dependências. Esta outra
frase faz o mesmo.'''
# the CONLL annotation format is a popular format for annotating part of speech
# and dependency tree graphs
result = parse(text, tagset="CINTIL", format="CONLL")
print(result)
Displaying dependency tree graphs from parsed text in CONLL format¶
To view dependency tree graphs for the parsed sentences, first we will split the CONLL output on empty lines to get one set of lines per sentence (each line carrying information pertaining to each token).
def group_sentence_conll_lines(conll_lines):
"""Groups CONLL-encoded lines (one line encodes one token), according to sentences.
This generator function takes as argument a sequence of CONLL lines, and generates
a sequence of lists, each one containing the CONLL lines of a sentence
"""
parsed_sentences = []
current_sentence = []
for line in conll_lines:
# lines starting with # are comments; ignore
if line.startswith("#"):
continue
# one or more consecutive empty lines mark the end of a sentence
if not line:
if current_sentence:
parsed_sentences.append(current_sentence)
current_sentence = []
else:
current_sentence.append(line)
if current_sentence:
parsed_sentences.append(current_sentence)
return parsed_sentences
Let us define a function render_tree that displays a sentence dependency graph, making use of the pydependencygrapher package for rendering the graph into an image and the IPython package for displaying the resulting image.
We also define a function render_tree_from_conll that will take a CONLL sentence (a list of CONLL-formatted lines, one for each token) and create one pydependencygrapher.Token object for each token, before calling render_tree to display the dependency graph.
def render_tree(sentence):
graph = pydependencygrapher.DependencyGraph(sentence)
graph.draw()
b64png = graph.save_buffer()
IPython.display.display(IPython.display.Image(data=base64.b64decode(b64png)))
def render_tree_from_conll(conll_sentence):
sentence = [pydependencygrapher.Token(*conll_token.split("\t")) for conll_token in conll_sentence]
return render_tree(sentence)
conll_lines = result.splitlines(keepends=False)
for conll_sentence in group_sentence_conll_lines(conll_lines):
data = render_tree_from_conll(conll_sentence)
The JSON output format¶
The JSON format (which we obtain by passing format="JSON" into the parse function) is more
convenient when we need to further process the annotations, because each abstraction is mapped
directly into a Python native object (lists, dicts, strings, etc) as follows:
- The returned object is a
list, where each element corresponds to a paragraph of the given text; - In turn, each paragraph is a
listwhere each element represents a sentence; - Each sentence is a
listwhere each element represents a token; - Each token is a
dictwhere each key-value pair is an attribute of the token.
parsed_text = parse(text, tagset="CINTIL", format="JSON")
for pnum, paragraph in enumerate(parsed_text, start=1): # enumerate paragraphs in text, starting at 1
print(f"paragraph {pnum}:")
for snum, sentence in enumerate(paragraph, start=1): # enumerate sentences in paragraph, starting at 1
print(f" sentence {snum}:")
for tnum, token in enumerate(sentence, start=1): # enumerate tokens in sentence, starting at 1
print(f" token {tnum}: {token!r}") # print a token representation
Displaying dependency graphs from parsed text in JSON format¶
Let us define a function, similar to render_tree_from_conll to display dependency graphs for JSON-encoded sentences.
def render_tree_from_json(json_sentence):
token_attributes = ["form", "lemma", "pos", "pos", "infl", "parent", "deprel", "parent", "deprel"]
sentence = []
for num, token in enumerate(json_sentence, start=1):
sentence.append(
pydependencygrapher.Token(
num,
*[token.get(attribute, "_") for attribute in token_attributes]
)
)
return render_tree(sentence)
Let us test the function we just defined
for paragraph in parsed_text:
for sentence in paragraph:
render_tree_from_json(sentence)
Getting the status of a webservice access key¶
def get_key_status():
'''Returns a string with the detailed status of the webservice access key'''
request_data = {
'method': 'key_status',
'jsonrpc': '2.0',
'id': 0,
'params': {
'key': LXDEPPARSER_WS_API_KEY,
},
}
request = requests.post(LXDEPPARSER_WS_API_URL, json=request_data)
response_data = request.json()
if "error" in response_data:
raise WSException(response_data["error"])
else:
return response_data["result"]
get_key_status()