jel - Japanese Entity Linker - is Bi-encoder based entity linker for japanese.

izuna385

Last update: Jan 6, 2023

Overview

jel: Japanese Entity Linker

jel - Japanese Entity Linker - is Bi-encoder based entity linker for japanese.

Usage

Currently, link and question methods are supported.

`el.link`

This returnes named entity and its candidate ones from Wikipedia titles.

from jel import EntityLinker
el = EntityLinker()

el.link('今日は東京都のマックにアップルを買いに行き、スティーブジョブスとドナルドに会い、堀田区に引っ越した。')
>> [
    {
        "text": "東京都",
        "label": "GPE",
        "span": [
            3,
            6
        ],
        "predicted_normalized_entities": [
            [
                "東京都庁",
                0.1084
            ],
            [
                "東京",
                0.0633
            ],
            [
                "国家地方警察東京都本部",
                0.0604
            ],
            [
                "東京都",
                0.0598
            ],
            ...
        ]
    },
    {
        "text": "アップル",
        "label": "ORG",
        "span": [
            11,
            15
        ],
        "predicted_normalized_entities": [
            [
                "アップル",
                0.2986
            ],
            [
                "アップル インコーポレイテッド",
                0.1792
            ],
            …
        ]
    }

`el.question`

This returnes candidate entity for any question from Wikipedia titles.

>>> linker.question('日本の総理大臣は？')
[('菅内閣', 0.05791765857101555), ('枢密院', 0.05592481946602986), ('党', 0.05430194711042564), ('総選挙', 0.052795400668513175)]

Setup

$ pip install jel
$ python -m spacy download ja_core_news_md

Run as API

$ uvicorn jel.api.server:app --reload --port 8000 --host 0.0.0.0 --log-level trace

Example

# link
$ curl localhost:8000/link -X POST -H "Content-Type: application/json" \
    -d '{"sentence": "日本の総理は菅総理だ。"}'

# question
$ curl localhost:8000/question -X POST -H "Content-Type: application/json" \
    -d '{"sentence": "日本で有名な総理は？"}

Test

$ python pytest

Notes

faiss==1.5.3 from pip causes error _swigfaiss.
To solve this, see this issue.

LICENSE

Apache 2.0 License.

CITATION

@INPROCEEDINGS{manabe2019chive,
    author    = {真鍋陽俊, 岡照晃, 海川祥毅, 髙岡一馬, 内田佳孝, 浅原正幸},
    title     = {複数粒度の分割結果に基づく日本語単語分散表現},
    booktitle = "言語処理学会第25回年次大会(NLP2019)",
    year      = "2019",
    pages     = "NLP2019-P8-5",
    publisher = "言語処理学会",
}

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Comments

ModuleNotFoundError

Traceback (most recent call last):
  File "scripts/biencoder_training_check.py", line 1, in <module>
    from jel.biencoder.train import biencoder_training
ModuleNotFoundError: No module named 'jel'

opened by izuna385 1

Separate Estimation Model and DB

Because the inference model and knowledge base are currently loaded together, it takes 30 seconds to load the model. To prevent this, we will separate the DB into a separate container.

opened by izuna385 0

Releases(v0.1.1)

v0.1.1(May 29, 2021)

First release.
Source code(tar.gz)
Source code(zip)

jel - Japanese Entity Linker - is Bi-encoder based entity linker for japanese.

Related tags

Overview

jel: Japanese Entity Linker

Usage

el.link

el.question

Setup

Run as API

Example

Test

Notes

LICENSE

CITATION

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Comments

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Owner

izuna385

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`el.link`

`el.question`