en python, crea una funcion que tome una palabra y la oracion del contexto como argumento y haga word sense disambiguation, retornando la definicion, el codigo debe utilizar nltk, la libreria de la api de babelnet en modo online en lugar de wordnet, una cache persistente para api, dar la opcion de elegir el idioma, y lesk, esta es la documentacion de la libreria de la api de babelnet:

PyPi
babelnet 1.2.0
pip install babelnet
Versión más reciente
Publicación: 17 ene 2024

Python API for BabelNet

Descripción de proyecto
This package consists of a Python API to work with BabelNet, a very large multilingual semantic network. For more information, please refer to the documentation below on how to use the software, and our website (https://babelnet.org) for news, updates and papers.

Version compatibility
BabelNet Python API can be used with BabeNet 4.0 and above.

Configuration
After the installation, the first step to take when you want to use BabelNet in another project (or in the REPL) is to create a file called babelnet_conf.yml in the current working directory. Alternatively, the path of the configuration file can be specified using the BABELNET_CONF environment variable.

The content of the babelnet_conf.yml should vary according to the usage mode of choice:

Online Mode: uses the online REST service to retrieve the data. To use this mode you need an internet connection and a valid API key.

RPC Mode: reads data directly from a local copy of the BabelNet indices, making it more suitable for heavy workloads than the online mode since it is faster and doesn't have usage limits. To use this mode you need the BabelNet indices and Docker installed in your system. The RPC server controller (see below) requires additional dependencies that can be installed with the following pip command:

pip install babelnet[rpc]
Further details on how to use these modes are provided in the following sections.

Online Mode
This is the simplest version to use, since it requires only a valid API key. However, the drawback is that the iterators are unavailable, i.e. the iterator, offset_iterator, lexicon_iterator and wordnet_iterator methods.

Assuming you have received by e-mail the key 3x54mp13-8au0-o97q-9vzz-3vakcpec8w4p, add the following line to babelnet_conf.yml:

RESTFUL_KEY: '3x54mp13-8au0-o97q-9vzz-3vakcpec8w4p'
This will automatically be used to authenticate you on the official BabelNet REST service.

The supported REST endpoints are:

https://babelnet.io/v9/service for BabelNet 5.3 (default)
https://babelnet.io/v8/service for BabelNet 5.2
https://babelnet.io/v7/service for BabelNet 5.1
https://babelnet.io/v6/service for BabelNet 5.0
https://babelnet.io/v5/service for BabelNet 4.0
If you want to use a different REST endpoint, add the following line to babelnet_conf.yml:

# BabelNet 5.3 REST endpoint
RESTFUL_URL: 'https://babelnet.io/v9/service'
RPC Mode
To use the RPC mode you need a local copy of the BabelNet indices. To download them, follow the procedure on the official website. This can be considered a full mode, because it has no usage limit and faster responses.

BabelNet Python API requires PyLucene, which has a dependency on Lucene itself. The installation process of Lucene can be tricky since it has many dependencies that need compiling. Because of this, we moved this PyLucene build and install process to a simple Docker image. In the RPC mode, the Remote Procedure Call paradigm is applied in calling this Docker container as a remote service, effectively decoupling PyLucene and BabelNet.

To configure the APIs in RPC mode, you just need to add one of these lines to your babelnet_conf.yml, depending on which protocol you want to use.

The default protocol used by the RPC server is TCP. You can specify the URL where the server is listening with the following configuration line.

# TCP URL example
RPC_URL: "tcp://127.0.0.1:7790"
If the RPC server has the optional IPC protocol enabled, you can use it with the following configuration line.

# IPC URL example
RPC_URL: "ipc:///home/user/your_ipc_dir/socket"
Important: to use lambdas in RPC mode, the client code must be run using the same Python version of the server, i.e. Python 3.8, and the same (or older) version of cloudpickle, i.e. 2.1.0.

To start the server, you can either use the RPC server controller or manually start the Docker. In any case you need Docker to be installed in your system. The controller is described in the following section; for details on how to directly use the Docker image, please follow the documentation on the Docker Hub page.

Note: when you update the API to a newer version, you need to either restart the server using the controller or pull the new docker from the hub and start a new server with the updated image.

RPC server controller
To simplify the management of the RPC server, you can use the babelnet-rpc command.

The additional dependencies required by the controller can be installed with the command:

pip install babelnet[rpc]
For Windows users: if you are working in an Anaconda environment, you need to install pywin32 using anaconda with the following command:

conda install pywin32=227
Documentation
Once the server is started, the documentation of the Python API will be available at http://localhost:7780, or alternatively to the port defined by the arguments of the start command.

Start the server
To start the server, you can use the command babelnet-rpc start. If no arguments are provided, it will start in interactive mode, in which you will be prompted to provide the required values.

$ babelnet-rpc start

BabelNet indices path: /home/user/BabelNet-5.3
Port for documentation ([7780], -1 to ignore): 8080
RPC mode ([tcp]/ipc/all): all
Port for TCP mode ([7790]): 
IPC directory: your_ipc_dir
Starting server...
Server started

BabelNet Python API documentation is available at http://localhost:8080

To use BabelNet in RPC mode, add one of these lines in your babelnet_conf.yml file
RPC_URL: "tcp://127.0.0.1:7790"
RPC_URL: "ipc:///home/user/your_ipc_dir/socket"
Alternatively, the values can be passed as arguments. The available arguments are:

--bn <path> required, the BabelNet indices path
--doc <port> port for the BabelNet API documentation (default 7780)
--no-doc disable the documentation port
-m, --mode the RPC mode enabled on the server (tcp, ipc or all, default tcp). On Windows the only available mode is tcp.
--tcp <port> the port for TPC mode (default 7790)
--ipc <path> the IPC directory (required with mode ipc or all)
--print print the command instead of executing it
Examples of usage
Basic usage
$ babelnet-rpc start --bn /home/user/BabelNet-5.3 

Starting server...
Server started

BabelNet Python API documentation will be available at http://localhost:7790

To use BabelNet in RPC mode, add this line in your babelnet_conf.yml file
RPC_URL: "tcp://127.0.0.1:7790"
IPC mode without documentation
$ babelnet-rpc start --bn /home/user/BabelNet-5.3 --no-doc -m ipc --ipc your_ipc_dir

Starting server...
Server started

To use BabelNet in RPC mode, add this line in your babelnet_conf.yml file
RPC_URL: "ipc:///home/user/your_ipc_dir/socket"
Custom TCP port, print docker command
$ babelnet-rpc start --bn /home/user/BabelNet-5.3 --print --tcp 1234

To start the RPC server, run the following command:
docker run -d --name babelnet-rpc -p 7780:8000 -p 1234:1234 -v "/home/user/BabelNet-5.3:/root/babelnet" babelscape/babelnet-rpc:latest

BabelNet Python API documentation will be available at http://localhost:7780

To use BabelNet in RPC mode, add this line in your babelnet_conf.yml file
RPC_URL: "tcp://127.0.0.1:1234"
Stop the server
To stop a running RPC server, run the command:

babelnet-rpc stop
Code
Assuming the installation and configuration phases have been completed, you can start working with BabelNet.

The entry point in the library is the babelnet package. It contains a set of functions that query the available content. You can import the package by calling:

import babelnet as bn
The two main classes of BabelNet are:

BabelSynset (a concept or named entity identified by a set of multilingual lexicalizations, each being a BabelSense)
BabelSense (a lexicalization of a given concept, i.e. a BabelSynset)
For more details, see the API documentation at https://babelnet.org/pydoc/1.2/.

BabelSynset
A BabelSynset is a set of multilingual lexicalizations that are synonyms expressing a given concept or named entity. For instance, the synset for car in the motorcar sense looks like this. After importing babelnet as bn we can use its functions to retrieve one or many BabelSynset objects. For instance, to retrieve all the synsets containing car we can call get_synsets:

from babelnet.language import Language

# Given a word in a certain language,
# returns the concepts (BabelSynsets) denoted by the word.
byl = bn.get_synsets('car', from_langs=[Language.EN])
We can also specify which of the parts of speech we are interested in and obtain only synsets for the specified part of speech. In the following example, we retrieve all the verbal synsets containing the English lexicalization run :

from babelnet.language import Language
from babelnet.pos import POS

# Given a word in a certain language and pos (part of speech),
# returns the concepts denoted by the word.
byl = bn.get_synsets('run', from_langs=[Language.EN], poses=[POS.VERB])
Due to the nature of BabelNet, a BabelSynset may contain lexicalizations from different sources. You can restrict your search only to your sources of interest. For instance:

from babelnet.language import Language
from babelnet.pos import POS
from babelnet.data.source import BabelSenseSource

# Given a word in a certain language, returns the concepts
# for the word available in the given sense sources.
byl = bn.get_synsets('run', from_langs=[Language.EN], poses=[POS.NOUN],
                     sources=[BabelSenseSource.WIKI, BabelSenseSource.OMWIKI])
Each BabelSynset has an ID that univocally identifies the synset, and that can be obtained via the id attribute of BabelSynset instances. If we have an ID and want to retrieve the corresponding synset, we can use get_synset. For instance:

from babelnet.resources import BabelSynsetID

# Gets a BabelSynset from a concept identifier (Babel synset ID).
by = bn.get_synset(BabelSynsetID('bn:03083790n'))
returns the BabelSynset corresponding to ID bn:03083790n, that is, the synset about BabelNet.

If we want to retrieve the BabelSynset corresponding to a given WordNet 3.0 ID, we can do the following:

from babelnet.resources import WordNetSynsetID

# Gets the BabelSynsets corresponding to an input WordNet offset.
by = bn.get_synset(WordNetSynsetID('wn:06879521n'))
If we want to retrieve the BabelSynset corresponding to a given Wikidata page ID, we can do the following:

from babelnet.resources import WikidataID

# Gets the BabelSynsets corresponding to an input Wikidata page ID.
by = bn.get_synset(WikidataID('Q4837690'))
If we want to retrieve the BabelSynsets containing a given Wikipedia page title, we can use the function get_synsets:

from babelnet.language import Language
from babelnet.pos import POS
from babelnet.resources import WikipediaID

# Given a Wikipedia title, returns the BabelSynsets which contain it.
byl = bn.get_synsets(WikipediaID('Men in Black (film 1997)', Language.IT, POS.NOUN))
BabelSense
A BabelSense is a term (either word or multi-word expression) in a given language occurring in a certain BabelSynset . Each occurrence of the same term (e.g., car) in different synsets is, therefore, a different BabelSense of that term.

Now let's look at the functions to retrieve a BabelSense using the bn module we have imported earlier:

from babelnet.language import Language
from babelnet.pos import POS
from babelnet.data.source import BabelSenseSource

#  Returns the senses for the word in a certain language.
senses1 = bn.get_senses('run', from_langs=[Language.EN])

# Returns the senses for the word in a certain language and Part-Of-Speech.
senses2 = bn.get_senses('run', from_langs=[Language.EN], poses=[POS.VERB])

# Returns the senses for the word with the given constraints.
senses3 = bn.get_senses('run', from_langs=[Language.EN], poses=[POS.VERB],
                        sources=[BabelSenseSource.WIKI, BabelSenseSource.OMWIKI])
Once we have a BabelSense, we can go back to the synset it belongs with the synset property:

by = sense.synset
We can view the BabelSynset as a container of BabelSense s, i.e., the lexicalizations in the various languages contained in the synset that express its concept or named entity.

Some attributes of BabelSynset and BabelSense
We are now going into details about important attributes (methods, properties) of the BabelSynset and BabelSense classes.

BabelSynset
BabelSynset is composed of various elements, which we describe below. Furthermore, a BabelSynset is connected to other BabelSynset objects. The main components of a BabelSynset are objects of the following types:

BabelSense (a lexicalization of the concept, see above)
POS (the synset's part of speech)
BabelGloss (a definition of the concept in a given language)
BabelExample (an example sentence of the meaning expressed by the synset)
BabelImage (an image depicting the concept)
BabelSynsetRelation (an edge semantically connecting the synset to another synset)
Let's take a look at the main methods and properties of a BabelSynset object which we call by. Note: to obtain BabelSynset objects we can also use the above examples.

# Get a BabelSynset from a concept identifier (Babel synset ID).
by = bn.get_synset(BabelSynsetID('bn:03083790n'))

# Most relevant BabelSense to this BabelSynset for a given language.
bs = by.main_sense(Language.EN)

# The part of speech of this BabelSynset.
pos = by.pos

# True if the BabelSynset is a key concept
is_key_concept = by.is_key_concept

# Gets the senses contained in this BabelSynset.
senses = by.senses()

# Collects all BabelGlosses in the given source for this BabelSynset.
glosses = by.glosses()

# Collects all BabelExamples for this BabelSynset.
examples = by.examples()

# The images (BabelImages) of this BabelSynset.
images = by.images

# Collects all the edges incident on this BabelSynset.
edges = by.outgoing_edges()

# Gets the BabelCategory objects of this BabelSynset.
cats = by.categories()
BabelSense
We now have a look at the BabelSense attributes. The main components of a BabelSense are:

BabelSynset (the synset the sense belongs to)
POS (its part-of-speech tag)
the lemma string (the lexicalization of the sense)
BabelSensePhonetics (the written and audio pronunciations of this sense)
BabelSenseSource (the source of the sense, e.g.: Wikipedia, WordNet, etc.)
Some code retrieving the above information follows:

bs = by.main_sense(Language.EN)

# The language of this BabelSense
lang = bs.language

# The part-of-speech tag of this BabelSense
pos = bs.pos

# True if the BabelSense is a key concept
is_key_concept = bs.is_key_sense

# The lemma of this BabelSense
lemma = bs.full_lemma

# The normalized lemma of this sense (i.e., lowercase, without parentheses, etc.)
normalized_lemma = bs.normalized_lemma

# The pronunciations of this sense
pronunciations = bs.pronunciations

# The source of the sense; ex: Wikipedia, WordNet, etc.
source = bs.source
Usage examples
Here we show full examples that show how you can use the BabelNet API to accomplish several tasks.

Retrieve all BabelSynset objects for a specific word
import babelnet as bn
from babelnet import Language

for synset in bn.get_synsets('home', from_langs=[Language.EN]):
    print('Synset ID:', synset.id)
For a specific word retrieves all BabelSynset objects in English, Italian and French
import babelnet as bn
from babelnet import Language

synsets = bn.get_synsets('home', from_langs=[Language.EN],
                         to_langs=[Language.IT, Language.FR])
for synset in synsets:
    print('Synset ID:', synset.id)
Retrieve all BabelSense objects for a specific BabelSynset object
import babelnet as bn
from babelnet import BabelSynsetID

synset = bn.get_synset(BabelSynsetID('bn:00000356n'))
# a synset is an iterator over its senses
for sense in synset:
    print('Sense: ' + sense.full_lemma,
          'Language: ' + str(sense.language),
          'Source: ' + str(sense.source), sep='\t')
    phonetic = sense.pronunciations
    for audio in phonetic.audios:
        print('Audio URL', audio.validated_url)
Retrieve all BabelSense objects for a specific Wikidata page id
import babelnet as bn
from babelnet.resources import WikidataID

synset = bn.get_synset(WikidataID('Q4837690'))
# a synset is an iterator over its senses
for sense in synset:
    print('Sense: ' + sense.full_lemma,
          'Language: ' + str(sense.language),
          'Source: ' + str(sense.source), sep='\t')
    phonetic = sense.pronunciations
    for audio in phonetic.audios:
        print('Audio URL', audio.validated_url)
Retrieve Wikidata id for each BabelSense in a BabelSynset
import babelnet as bn
from babelnet import BabelSynsetID, BabelSenseSource

by = bn.get_synset(BabelSynsetID('bn:00000356n'))
for sense in by.senses(source=BabelSenseSource.WIKIDATA):
    sensekey = sense.sensekey
    print(sense.full_lemma, sense.language, sensekey, sep='\t')
Retrieve neighbors of a BabelSynset object
import babelnet as bn
from babelnet import BabelSynsetID, Language
from babelnet.data.relation import BabelPointer

by = bn.get_synset(BabelSynsetID('bn:00015556n'))
for edge in by.outgoing_edges(BabelPointer.ANY_HYPERNYM):
    print(str(by.id) + '\t' + by.main_sense(Language.EN).full_lemma,
          edge.pointer, edge.id_target, sep=' - ')
Retrieve the distribution of relationships (frequency of each BabelPointer type) for a specific word
from itertools import groupby

import babelnet as bn
from babelnet import Language

synsets = bn.get_synsets('car', from_langs=[Language.EN])
li = [edge.pointer.symbol for synset in synsets for edge
      in synset.outgoing_edges()]
for p, l in groupby(sorted(li)):
    print(p, len(list(l)), sep='\t')
Multithreading
In online mode requests can come from different threads or processes and are elaborated concurrently.

In RPC mode, using the API simultaneously from multiple threads is discouraged due to Python's threading management and the limitations of the RPC library. Since sending concurrent requests to the server can lead to long response times, to avoid timeouts it is recommended to use a limited pool like in the following example.

import concurrent.futures
from datetime import datetime
from sys import stdout

import babelnet as bn
from babelnet import Language


# function called from the threads
def func(name: str, word: str):
    stdout.write(datetime.now().strftime("%H:%M:%S.%f") + " - Start - " + name + "\n")
    synsets = bn.get_synsets(word, from_langs=[Language.EN])
    glosses = []
    for synset in synsets:
        gloss = synset.main_gloss(Language.EN)
        if gloss:
            glosses.append(gloss.gloss)
    stdout.write(datetime.now().strftime("%H:%M:%S.%f") + " - End   - " + name + "\n")
    return {word: glosses}


word_list = ["vocabulary", "article", "time", "bakery", "phoenix", "stunning", "judge", "clause", "anaconda",
             "patience", "risk", "scribble", "writing", "zebra", "trade"]

with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
    future = []
    for i, w in enumerate(word_list):
        future.append(executor.submit(func, f'Thread {i} "{w}"', w))
    results = {}
    for f in future:
        results.update(f.result())

for w, gs in results.items():
    for g in gs:
        print(w, g, sep='\t')