-
```
Building a wrapper for the MATE pipeline for processing several documents and
without loading each model for every document, one needs to call the out()
methods of the respective tools (after in…
-
```
Building a wrapper for the MATE pipeline for processing several documents and
without loading each model for every document, one needs to call the out()
methods of the respective tools (after in…
-
```
Building a wrapper for the MATE pipeline for processing several documents and
without loading each model for every document, one needs to call the out()
methods of the respective tools (after in…
-
Extract keywords from either text or hopefully HTML to be able to search in them. It doesn't have to be anything super fancy, looking at the most frequent words is okay.
Instructions from the super…
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I’m starting to lemmatize, and came across this issue: the lemmatizer doesn’t seem to deal with ligatures properly, e.g. {d}+EN and {d}+AG, which are standard in Neo-Babylonian. When I have names writ…
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I want to add my own sense2vec to my own spacy model, as you wrote in documentation,
I add that to my current pipeline
```
[initialize.components]
[initialize.components.sense2vec]
data_…
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```
Building a wrapper for the MATE pipeline for processing several documents and
without loading each model for every document, one needs to call the out()
methods of the respective tools (after in…
-
```
Building a wrapper for the MATE pipeline for processing several documents and
without loading each model for every document, one needs to call the out()
methods of the respective tools (after in…
-
# 英语单词的词形还原（Lemmatization）
## 定义
把单词的各种时态人称单复数的变形转为原形，比如：
- 动词原型 `calls`, `called`, `calling` -> `call`
- 名词单数 `cats` -> cat
- 比较级和最高级 `hardest` -> `hard`
## 可用的库：
pyhton:
- 自然语言工具…
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```
Building a wrapper for the MATE pipeline for processing several documents and
without loading each model for every document, one needs to call the out()
methods of the respective tools (after in…