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concepcy-0.1.0
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💫 SpaCy wrapper for ConceptNet 💫
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | concepcy-0.1.0 |
| نام | concepcy |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | JulesBelveze |
| ایمیل نویسنده | jules.belveze@hotmail.fr |
| آدرس صفحه اصلی | https://github.com/JulesBelveze/concepcy |
| آدرس اینترنتی | https://pypi.org/project/concepcy/ |
| مجوز | - |
<img src="figures/concepcy.png" width="40%" align="right"/>
# concepCy
[](https://pypi.org/project/concepCy/)
[](https://julesbelveze.github.io/concepcy/)
`concepCy` is a spaCy wrapper for [ConceptNet](https://conceptnet.io/), a freely-available semantic network designed to
help computers understand the meaning of words.
`concepCy` allows you to query [ConceptNet.io](https://conceptnet.io/) to extract word meanings directly from the
resource itself.
# Install
You can install `concepCy` via pip:
```
pip install concepcy
```
Alternatively you can directly clone the repository and install it using [poetry](https://python-poetry.org/docs/) by
running the following:
```
git clone https://github.com/JulesBelveze/concepcy.git
cd concepcy
poetry install
```
## Getting Started
To get started you need to install of one the pre-trained spaCy model available [here](https://spacy.io/models).
In `ConceptNet` words are represented as `Node` and relations between words as `Edge`. \
The `Node` object contains the following attributes:
* `id`: where you can look up all the information about that word
* `label`: which may be a more complete phrase such as "an example" instead of just the word "example" that appears in
the URI.
* `language`: code for what language the `label` is in
* `term`: a link to the most general version of this term. In many cases this is just the same URI.
The `Edge` object features the following attributes:
* `start`: starting `Node`
* `end`: ending `Node`
* `relation`: name of the relation for those two nodes
* `text`: some of ConceptNet's data is extracted from text, `text` shows you what this text was
* `weight`: how believable the information is
### Simple start
In this case we will simply be interested in the *RelatedTo* relations between words.
```python
import spacy
import concepcy
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("concepcy")
doc = nlp("WHO is a lovely company")
# Access all the "RelatedTo" relations from the Doc
print("--- All the 'RelatedTo' relations from the Doc ---")
for word, relations in doc._.relatedto.items():
print(f"Word: '{word}'\n{relations}")
# Access the "RelatedTo" relations word by word
print("--- The 'RelatedTo' relations word by word ---")
for token in doc:
print(f"Word: '{token}'\n{token._.relatedto}\n")
```
```bash
--- All the 'RelatedTo' relations from the Doc ---
Word: 'company'
[{'start': {'id': '/c/en/company', 'type': 'Node', 'label': 'company', 'language': 'en', 'term': '/c/en/company'}, 'end': {'id': '/c/en/business', 'type': 'Node', 'label': 'business', 'language': 'en', 'term': '/c/en/business'}, 'relation': 'RelatedTo', 'text': '[[company]] is related to [[business]]', 'weight': 6.424017434596516}, {'start': {'id': '/c/en/company', 'type': 'Node', 'label': 'company', 'language': 'en', 'term': '/c/en/company'}, 'end': {'id': '/c/en/corporation', 'type': 'Node', 'label': 'corporation', 'language': 'en', 'term': '/c/en/corporation'}, 'relation': 'RelatedTo', 'text': '[[company]] is related to [[corporation]]', 'weight': 4.432155231938521}, {'start': {'id': '/c/en/company', 'type': 'Node', 'label': 'company', 'language': 'en', 'term': '/c/en/company'}, 'end': {'id': '/c/en/organization', 'type': 'Node', 'label': 'organization', 'language': 'en', 'term': '/c/en/organization'}, 'relation': 'RelatedTo', 'text': '[[company]] is related to [[organization]]', 'weight': 4.259107887809371}]
--- The 'RelatedTo' relations word by word ---
Word: 'WHO'
[]
Word: 'is'
[]
Word: 'a'
[]
Word: 'lovely'
[]
Word: 'company'
[{'start': {'id': '/c/en/company', 'type': 'Node', 'label': 'company', 'language': 'en', 'term': '/c/en/company'}, 'end': {'id': '/c/en/business', 'type': 'Node', 'label': 'business', 'language': 'en', 'term': '/c/en/business'}, 'relation': 'RelatedTo', 'text': '[[company]] is related to [[business]]', 'weight': 6.424017434596516}, {'start': {'id': '/c/en/company', 'type': 'Node', 'label': 'company', 'language': 'en', 'term': '/c/en/company'}, 'end': {'id': '/c/en/corporation', 'type': 'Node', 'label': 'corporation', 'language': 'en', 'term': '/c/en/corporation'}, 'relation': 'RelatedTo', 'text': '[[company]] is related to [[corporation]]', 'weight': 4.432155231938521}, {'start': {'id': '/c/en/company', 'type': 'Node', 'label': 'company', 'language': 'en', 'term': '/c/en/company'}, 'end': {'id': '/c/en/organization', 'type': 'Node', 'label': 'organization', 'language': 'en', 'term': '/c/en/organization'}, 'relation': 'RelatedTo', 'text': '[[company]] is related to [[organization]]', 'weight': 4.259107887809371}]
```
### Custom configuration
One can customize the `concepcy` wrapper by changing the default value of the config. The two parameters of interest
are:
* `relations_of_interest: List[str]`: ConceptNet currently support 34 word-relations. Some of them might not be needed
for your use case. To only keep the ones needed pass a list of all the relations you want to keep (see all relations
available [here](https://github.com/commonsense/conceptnet5/wiki/Relations)). Each relation then becomes an extension.
* `filter_edge_fct: Callable[Edge]`: Conceptnet is a crowd-sourced resource, meaning that some information might be more
relevant than others. To only keep reliable relations you can pass a function that will take an `Edge` as input and
will return a boolean indicating whether to filter that edge or not.
```python
import spacy
import concepcy
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe(
"concepcy",
config={
"relations_of_interest": ["MotivatedByGoal", "CapableOf"],
"filter_edge_weight": 3.0,
"filter_missing_text": True,
"as_dict": False
}
)
```
# Documentation 📚
The whole documentation along with design decisions and examples can be
found [here](https://julesbelveze.github.io/concepcy/).
# References
* [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://arxiv.org/abs/1612.03975)
نیازمندی
| مقدار | نام |
|---|---|
| >=3.3.0,<4.0.0 | spacy |
| >=1.9.1,<2.0.0 | pydantic |
| >=0.6,<0.7 | request-boost |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<4.0 | Python |
نحوه نصب
نصب پکیج whl concepcy-0.1.0:
pip install concepcy-0.1.0.whl
نصب پکیج tar.gz concepcy-0.1.0:
pip install concepcy-0.1.0.tar.gz