https://github.com/izuna385/jel

Japanese Entity Linker.
https://github.com/izuna385/jel

allennlp entity-linking jel natural-language-processing python pytorch question-answering transformers

Last synced: 1 day ago
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Japanese Entity Linker.

• Host: GitHub
• URL: https://github.com/izuna385/jel
• Owner: izuna385
• License: apache-2.0
• Created: 2021-03-08T16:14:17.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-07-25T04:01:46.000Z (about 3 years ago)
• Last Synced: 2024-09-08T13:54:23.412Z (25 days ago)
• Topics: allennlp, entity-linking, jel, natural-language-processing, python, pytorch, question-answering, transformers
• Language: Python
• Homepage: https://pypi.org/project/jel/
• Size: 46.9 MB
• Stars: 11
• Watchers: 1
• Forks: 1
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        


# 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.

```python

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.

```python

>>> 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](https://github.com/facebookresearch/faiss/issues/821#issuecomment-573531694).

## 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",

}

```