Comments (1)
bachan
commented on July 26, 2024
2
I'm not really working in the company for quite a while anymore, but I'll reply here.
I guess, the goal when building this tokenizer was efficiency over precision. So that the memory footprint is relatively low and the throughput is high. We had to process billions of pages with that thing daily.
It is important to note that we tried to keep cases where it's wrong to be reasonable from text matching perspective. Which means that if coccoc-tokenizer is incorrect from the linguistics standpoint, it still doesn't affect search ranking metrics.
Hope that helps. :)
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Related Issues (20)
- How to config options when using Python binding HOT 3
- V/v giữ nguyên hoa/thường cho bản Java wrapper HOT 2
- use lib for python HOT 1
- Missed tokenizing entity's name HOT 4
- Build trên hệ điều hành windows HOT 3
- Khi build trên Windows với tham số -DBUILD_PYTHON=1 bị lỗi HOT 1
- lỗi khi chạy bằng c++ trên window HOT 1
- java.lang.RuntimeException: Cannot initialize Tokenizer HOT 2
- Lỗi khi build project trên Ubuntu HOT 1
- Lỗi khi chạy câu lệnh make install HOT 4
- ERROR ON ELASTIC SEARCH 7.13.1 HOT 1
- Hướng dẫn đầy đủ cài đặt C++ Tokenizer & ES 7.12.1 Analysis Vietnam plugin HOT 6
- Can the tokenizer produce tokens as original case? HOT 2
- Need help when install libary for python in conda environment HOT 4
- What option available for `tokenize_option` in Python binding ? HOT 1
- how to install coccoc-tokenizer with pip HOT 1
- Thêm từ ghép tiếng Việt mới
- Error in coccoc-tokenizer/dicts/tokenizer/Freq2NontoneUniFile when installing on CentOS 7 HOT 2
- Python with coccoc_tokenizer HOT 3
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