rhoknp: Yet another Python binding for Juman++/KNP/KWJA

Documentation: https://rhoknp.readthedocs.io/en/latest/

Source Code: https://github.com/ku-nlp/rhoknp

rhoknp is a Python binding for Juman++, KNP, and KWJA.[^1]

[^1]: The logo was generated by OpenAI DALL·E 2.

import rhoknp

# Perform morphological analysis by Juman++
jumanpp = rhoknp.Jumanpp()
sentence = jumanpp.apply_to_sentence(
    "電気抵抗率は電気の通しにくさを表す物性値である。"
)

# Access to the result
for morpheme in sentence.morphemes:  # a.k.a. keitai-so
    ...

# Save the result
with open("result.jumanpp", "wt") as f:
    f.write(sentence.to_jumanpp())

# Load the result
with open("result.jumanpp", "rt") as f:
    sentence = rhoknp.Sentence.from_jumanpp(f.read())

Requirements

Python 3.9+
(Optional) Juman++ v2.0.0-rc3+
(Optional) KNP 5.0+
(Optional) KWJA 1.0.0+

Installation

pip install rhoknp

Quick tour

Let's begin by using Juman++ with rhoknp. Here, we present a simple example demonstrating how Juman++ can be used to analyze a sentence.

# Perform morphological analysis by Juman++
jumanpp = rhoknp.Jumanpp()
sentence = jumanpp.apply_to_sentence("電気抵抗率は電気の通しにくさを表す物性値である。")

You can easily access the individual morphemes that make up the sentence.

for morpheme in sentence.morphemes:  # a.k.a. keitai-so
    ...

Sentence objects can be saved in the JUMAN format.

# Save the sentence in the JUMAN format
with open("sentence.jumanpp", "wt") as f:
    f.write(sentence.to_jumanpp())

# Load the sentence
with open("sentence.jumanpp", "rt") as f:
    sentence = rhoknp.Sentence.from_jumanpp(f.read())

Almost the same APIs are available for KNP.

# Perform language analysis by KNP
knp = rhoknp.KNP()
sentence = knp.apply_to_sentence("電気抵抗率は電気の通しにくさを表す物性値である。")

KNP performs language analysis at multiple levels.

for clause in sentence.clauses:  # a.k.a., setsu
    ...
for phrase in sentence.phrases:  # a.k.a. bunsetsu
    ...
for base_phrase in sentence.base_phrases:  # a.k.a. kihon-ku
    ...
for morpheme in sentence.morphemes:  # a.k.a. keitai-so
    ...

Sentence objects can be saved in the KNP format.

# Save the sentence in the KNP format
with open("sentence.knp", "wt") as f:
    f.write(sentence.to_knp())

# Load the sentence
with open("sentence.knp", "rt") as f:
    sentence = rhoknp.Sentence.from_knp(f.read())

Furthermore, rhoknp provides convenient APIs for document-level language analysis.

document = rhoknp.Document.from_raw_text(
    "電気抵抗率は電気の通しにくさを表す物性値である。単に抵抗率とも呼ばれる。"
)
# If you know sentence boundaries, you can use `Document.from_sentences` instead.
document = rhoknp.Document.from_sentences(
    [
        "電気抵抗率は電気の通しにくさを表す物性値である。",
        "単に抵抗率とも呼ばれる。",
    ]
)

Document objects can be handled in a similar manner as Sentence objects.

# Perform morphological analysis by Juman++
document = jumanpp.apply_to_document(document)

# Access language units in the document
for sentence in document.sentences:
    ...
for morpheme in document.morphemes:
    ...

# Save language analysis by Juman++
with open("document.jumanpp", "wt") as f:
    f.write(document.to_jumanpp())

# Load language analysis by Juman++
with open("document.jumanpp", "rt") as f:
    document = rhoknp.Document.from_jumanpp(f.read())

For more information, please refer to the examples and documentation.

Main differences from pyknp

pyknp serves as the official Python binding for Juman++ and KNP. In the development of rhoknp, we redesigned the API, considering the current use cases of pyknp. The key differences between the two are as follows:

Support for document-level language analysis: rhoknp allows you to load and instantiate the results of document-level language analysis, including cohesion analysis and discourse relation analysis.
Strict type-awareness: rhoknp has been thoroughly annotated with type annotations, ensuring strict type checking and improved code clarity.
Comprehensive test suite: rhoknp is extensively tested with a comprehensive test suite. You can view the code coverage report on Codecov.