A spaCy wrapper of OpenTapioca for named entity linking on Wikidata

Overview

spaCyOpenTapioca

A spaCy wrapper of OpenTapioca for named entity linking on Wikidata.

Table of contents

Installation

pip install spacyopentapioca

or

git clone https://github.com/UB-Mannheim/spacyopentapioca
cd spacyopentapioca/
pip install .

How to use

After installation the OpenTapioca pipeline can be used without any other pipelines:

import spacy
nlp = spacy.blank("en")
nlp.add_pipe('opentapioca')
doc = nlp("Christian Drosten works in Germany.")
for span in doc.ents:
    print((span.text, span.kb_id_, span.label_, span._.description, span._.score))
('Christian Drosten', 'Q1079331', 'PERSON', 'German virologist and university teacher', 3.6533377082098895)
('Germany', 'Q183', 'LOC', 'sovereign state in Central Europe', 2.1099332471902863)

The types and aliases are also available:

for span in doc.ents:
    print((span._.types, span._.aliases[0:5]))
({'Q43229': False, 'Q618123': False, 'Q5': True, 'P2427': False, 'P1566': False, 'P496': True}, ['كريستيان دروستين', 'Крістіан Дростен', 'Christian Heinrich Maria Drosten', 'کریستین دروستن', '크리스티안 드로스텐'])
({'Q43229': True, 'Q618123': True, 'Q5': False, 'P2427': False, 'P1566': True, 'P496': False}, ['IJalimani', 'R. F. A.', 'Alemania', '도이칠란트', 'Germaniya'])

The Wikidata QIDs are attached to tokens:

for token in doc:
    print((token.text, token.ent_kb_id_))
('Christian', 'Q1079331')
('Drosten', 'Q1079331')
('works', '')
('in', '')
('Germany', 'Q183')
('.', '')

The raw response of the OpenTapioca API can be accessed in the doc- and span-objects:

raw_annotations1 = doc._.annotations
raw_annotations2 = [span._.annotations for span in doc.ents]

The partial metadata for the response returned by the OpenTapioca API is

doc._.metadata

All span-extensions are:

span._.annotations
span._.description
span._.aliases
span._.rank
span._.score
span._.types
span._.label
span._.extra_aliases
span._.nb_sitelinks
span._.nb_statements

Note that spaCyOpenTapioca does a tiny processing of entities appearing in doc.ents. All entities returned by OpenTapioca can be found in doc.spans['all_entities_opentapioca'].

Local OpenTapioca

If OpenTapioca is deployed locally, specify the URL of the new OpenTapioca API in the config:

import spacy
nlp = spacy.blank("en")
nlp.add_pipe('opentapioca', config={"url": OpenTapiocaAPI})
doc = nlp("Christian Drosten works in Germany.")

Vizualization

NER vizualization in spaCy via displaCy cannot show yet the links to entities. This can be added into spaCy as proposed in issue 9129.

Comments
  • AttributeError: 'NoneType' object has no attribute 'text' when using nlp.pipe()

    AttributeError: 'NoneType' object has no attribute 'text' when using nlp.pipe()

    Hi, when I process multiple text documents as a batch, I have failure with the error message: AttributeError: 'NoneType' object has no attribute 'text'. However, processing each text document by itself produces no such error. Here is a easy to reproduce example:

    docs = ["""String of 126 characters. String of 126 characters. String of 126 characters. String of 126 characters. String of 126 characte""","""Any string which is 93 characters. Any string which is 93 characters. Any string which is 93 """]
    nlp = spacy.blank("en")
    nlp.add_pipe("opentapioca")
    for doc in nlp.pipe(docs):
        print(doc)
    

    Fulll stack trace below:

    AttributeError                            Traceback (most recent call last)
    <command-370658210397732> in <module>
          4 nlp = spacy.blank("en")
          5 nlp.add_pipe("opentapioca")
    ----> 6 for doc in nlp.pipe(docs):
          7     print(doc)
    
    /databricks/python/lib/python3.8/site-packages/spacy/language.py in pipe(self, texts, as_tuples, batch_size, disable, component_cfg, n_process)
       1570         else:
       1571             # if n_process == 1, no processes are forked.
    -> 1572             docs = (self._ensure_doc(text) for text in texts)
       1573             for pipe in pipes:
       1574                 docs = pipe(docs)
    
    /databricks/python/lib/python3.8/site-packages/spacy/util.py in _pipe(docs, proc, name, default_error_handler, kwargs)
       1597     if hasattr(proc, "pipe"):
       1598         yield from proc.pipe(docs, **kwargs)
    -> 1599     else:
       1600         # We added some args for pipe that __call__ doesn't expect.
       1601         kwargs = dict(kwargs)
    
    /databricks/python/lib/python3.8/site-packages/spacyopentapioca/entity_linker.py in pipe(self, stream, batch_size)
        117                     self.make_request, doc): doc for doc in docs}
        118                 for doc, future in zip(docs, concurrent.futures.as_completed(future_to_url)):
    --> 119                     yield self.process_single_doc_after_call(doc, future.result())
    
    /databricks/python/lib/python3.8/site-packages/spacyopentapioca/entity_linker.py in process_single_doc_after_call(self, doc, r)
         66                                      alignment_mode='expand')
         67                 log.warning('The OpenTapioca-entity "%s" %s does not fit the span "%s" %s in spaCy. EXPANDED!',
    ---> 68                             ent['tags'][0]['label'][0], (start, end), span.text, (span.start_char, span.end_char))
         69             span._.annotations = ent
         70             span._.description = ent['tags'][0]['desc']
    
    AttributeError: 'NoneType' object has no attribute 'text'
    

    I don't know what about the lengths of the strings causes an issue, but they do seem to matter in some way. Adding or removing a couple characters from either string can resolve the issue.

    opened by coltonpeltier-db 6
  • Add methods to highlights

    Add methods to highlights

    In the same way by clicking a NER highlighting leads to a web side it would perhaps be possible to extend this functionality and pass a method to be run when clicking the highlighted NER.

    opened by joseberlines 4
  • Add CodeQL workflow for GitHub code scanning

    Add CodeQL workflow for GitHub code scanning

    Hi UB-Mannheim/spacyopentapioca!

    This is a one-off automatically generated pull request from LGTM.com :robot:. You might have heard that we’ve integrated LGTM’s underlying CodeQL analysis engine natively into GitHub. The result is GitHub code scanning!

    With LGTM fully integrated into code scanning, we are focused on improving CodeQL within the native GitHub code scanning experience. In order to take advantage of current and future improvements to our analysis capabilities, we suggest you enable code scanning on your repository. Please take a look at our blog post for more information.

    This pull request enables code scanning by adding an auto-generated codeql.yml workflow file for GitHub Actions to your repository — take a look! We tested it before opening this pull request, so all should be working :heavy_check_mark:. In fact, you might already have seen some alerts appear on this pull request!

    Where needed and if possible, we’ve adjusted the configuration to the needs of your particular repository. But of course, you should feel free to tweak it further! Check this page for detailed documentation.

    Questions? Check out the FAQ below!

    FAQ

    Click here to expand the FAQ section

    How often will the code scanning analysis run?

    By default, code scanning will trigger a scan with the CodeQL engine on the following events:

    • On every pull request — to flag up potential security problems for you to investigate before merging a PR.
    • On every push to your default branch and other protected branches — this keeps the analysis results on your repository’s Security tab up to date.
    • Once a week at a fixed time — to make sure you benefit from the latest updated security analysis even when no code was committed or PRs were opened.

    What will this cost?

    Nothing! The CodeQL engine will run inside GitHub Actions, making use of your unlimited free compute minutes for public repositories.

    What types of problems does CodeQL find?

    The CodeQL engine that powers GitHub code scanning is the exact same engine that powers LGTM.com. The exact set of rules has been tweaked slightly, but you should see almost exactly the same types of alerts as you were used to on LGTM.com: we’ve enabled the security-and-quality query suite for you.

    How do I upgrade my CodeQL engine?

    No need! New versions of the CodeQL analysis are constantly deployed on GitHub.com; your repository will automatically benefit from the most recently released version.

    The analysis doesn’t seem to be working

    If you get an error in GitHub Actions that indicates that CodeQL wasn’t able to analyze your code, please follow the instructions here to debug the analysis.

    How do I disable LGTM.com?

    If you have LGTM’s automatic pull request analysis enabled, then you can follow these steps to disable the LGTM pull request analysis. You don’t actually need to remove your repository from LGTM.com; it will automatically be removed in the next few months as part of the deprecation of LGTM.com (more info here).

    Which source code hosting platforms does code scanning support?

    GitHub code scanning is deeply integrated within GitHub itself. If you’d like to scan source code that is hosted elsewhere, we suggest that you create a mirror of that code on GitHub.

    How do I know this PR is legitimate?

    This PR is filed by the official LGTM.com GitHub App, in line with the deprecation timeline that was announced on the official GitHub Blog. The proposed GitHub Action workflow uses the official open source GitHub CodeQL Action. If you have any other questions or concerns, please join the discussion here in the official GitHub community!

    I have another question / how do I get in touch?

    Please join the discussion here to ask further questions and send us suggestions!

    opened by lgtm-com[bot] 1
  • 'ent_kb_id' referenced before assignment

    'ent_kb_id' referenced before assignment

    Hello, while trying this example : nlp("M. Knajdek"), An error occurs in the entity_linker.py file UnboundLocalError: local variable 'ent_kb_id' referenced before assignment on line 67 in the file. This is due to the . separator.

    opened by TheNizzo 1
  • Added logging & Fixed Reference Error

    Added logging & Fixed Reference Error

    Added logger to allow user to suppress logs coming from spacyopentapioca.

    Fixed thelocal variable 'etype' referenced before assignment error at line 65.

    opened by jordanparker6 1
Releases(v.0.1.6)
Owner
Universitätsbibliothek Mannheim
Mannheim University Library
Universitätsbibliothek Mannheim
Exploration of BERT-based models on twitter sentiment classifications

twitter-sentiment-analysis Explore the relationship between twitter sentiment of Tesla and its stock price/return. Explore the effect of different BER

Sammy Cui 2 Oct 02, 2022
Python functions for summarizing and improving voice dictation input.

Helpmespeak Help me speak uses Python functions for summarizing and improving voice dictation input. Get started with OpenAI gpt-3 OpenAI is a amazing

Margarita Humanitarian Foundation 6 Dec 17, 2022
Text Classification Using LSTM

Text classification is the task of assigning a set of predefined categories to free text. Text classifiers can be used to organize, structure, and categorize pretty much anything. For example, new ar

KrishArul26 3 Jan 03, 2023
KR-FinBert And KR-FinBert-SC

KR-FinBert & KR-FinBert-SC Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adapt

5 Jul 29, 2022
Utilize Korean BERT model in sentence-transformers library

ko-sentence-transformers 이 프로젝트는 KoBERT 모델을 sentence-transformers 에서 보다 쉽게 사용하기 위해 만들어졌습니다. Ko-Sentence-BERT-SKTBERT 프로젝트에서는 KoBERT 모델을 sentence-trans

Junghyun 40 Dec 20, 2022
text to speech toolkit. 好用的中文语音合成工具箱,包含语音编码器、语音合成器、声码器和可视化模块。

ttskit Text To Speech Toolkit: 语音合成工具箱。 安装 pip install -U ttskit 注意 可能需另外安装的依赖包:torch,版本要求torch=1.6.0,=1.7.1,根据自己的实际环境安装合适cuda或cpu版本的torch。 ttskit的

KDD 483 Jan 04, 2023
Code for Findings at EMNLP 2021 paper: "Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning"

Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot Learning This repo is for Findings at EMNLP 2021 paper: Learn Cont

INK Lab @ USC 6 Sep 02, 2022
Rank-One Model Editing for Locating and Editing Factual Knowledge in GPT

Rank-One Model Editing (ROME) This repository provides an implementation of Rank-One Model Editing (ROME) on auto-regressive transformers (GPU-only).

Kevin Meng 130 Dec 21, 2022
MASS: Masked Sequence to Sequence Pre-training for Language Generation

MASS: Masked Sequence to Sequence Pre-training for Language Generation

Microsoft 1.1k Dec 17, 2022
MicBot - MicBot uses Google Translate to speak everyone's chat messages

MicBot MicBot uses Google Translate to speak everyone's chat messages. It can al

2 Mar 09, 2022
leaking paid token generator that was a shit lmao for 100$ haha

Discord-Token-Generator-Leaked leaking paid token generator that was a shit lmao for 100$ he selling it for 100$ wth here the code enjoy don't forget

Keevo 5 Apr 15, 2022
Use Google's BERT for named entity recognition (CoNLL-2003 as the dataset).

For better performance, you can try NLPGNN, see NLPGNN for more details. BERT-NER Version 2 Use Google's BERT for named entity recognition (CoNLL-2003

Kaiyinzhou 1.2k Dec 26, 2022
Unsupervised Language Model Pre-training for French

FlauBERT and FLUE FlauBERT is a French BERT trained on a very large and heterogeneous French corpus. Models of different sizes are trained using the n

GETALP 212 Dec 10, 2022
GCRC: A Gaokao Chinese Reading Comprehension dataset for interpretable Evaluation

GCRC GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Eva

Yunxiao Zhao 5 Nov 04, 2022
Finetune gpt-2 in google colab

gpt-2-colab finetune gpt-2 in google colab sample result (117M) from retraining on A Tale of Two Cities by Charles Di

212 Jan 02, 2023
xFormers is a modular and field agnostic library to flexibly generate transformer architectures by interoperable and optimized building blocks.

Description xFormers is a modular and field agnostic library to flexibly generate transformer architectures by interoperable and optimized building bl

Facebook Research 2.3k Jan 08, 2023
My Implementation for the paper EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks using Tensorflow

Easy Data Augmentation Implementation This repository contains my Implementation for the paper EDA: Easy Data Augmentation Techniques for Boosting Per

Aflah 9 Oct 31, 2022
MiCECo - Misskey Custom Emoji Counter

MiCECo Misskey Custom Emoji Counter Introduction This little script counts custo

7 Dec 25, 2022
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

303 Dec 17, 2022