This post has been more than 2 years since it was last updated.
In my previous article, I talk about "Text Tokenization" on English language where we can use existed library to do the job with ease. Unlike other languages, Khmer Word Segmentation is way more complex. Because the Khmer language does not have any standard rule on how we are using space to separate between each word(space are used for easier reading). Moreover, Khmer word can have different meaning with the order of words when it will form. Khmer word could also be a join of two or more Khmer words together. Because of uncertain rule of spacing and the complicated structure above, which it is hard to segment Khmer Word.
In this article, I am going to a simple yet powerful data struture
trie to apply on Khmer word. As we all know, we need to fit our
trie data structure with the array of words. Then we can use the model to find/split words from given sentences which we can apply "Text Tokenization" on. So we need to find a list of Khmer words. Luckily, I found a repo which contain list of Khmer words. However, we need to write few line of code to prepare those list for our
Let's start coding .
First we convert
tsv format to
txt format where later we can use it to fit our
trie model. As you are notice, we need to inform and encoding our Khmer string to
#parse_tsv_word.py #!/usr/bin/python # -*- coding: utf-8 -*- # This file use to get Khmer word from tsv and save in other file import pandas as pd import numpy as np from codecs import open from argparse import ArgumentParser parser = ArgumentParser() parser.add_argument("-f", "--file", type=str, default="", help="File input name", required=True) parser.add_argument("-o", "--out", type=str, default="", help="File output name", required=True) args = vars(parser.parse_args()) file_path = args["file"] output_file_path = args["out"] words_list = pd.read_csv(file_path, delimiter="\t", encoding="utf-8", header=None) with open(output_file_path, "w", encoding="utf-8") as f: for word in words_list[words_list.columns]: f.write(word + "\n")
Let's run the command to read
tsv file and save it as
python parse_tsv_word.py -f data/villages.tsv -o data/villages.txt
Here we read the
txt file where we prepared above to train our
trie model(Let's assume you already have already built
#train_model.py #!/usr/bin/python # -*- coding: utf-8 -*- import trie from codecs import open, decode model = trie.Trie() # model.load_from_pickle("data/trained") input_file_path = "data/sea.txt" with open(input_file_path, "r") as f: words = f.read().split("\n") print("Training start") for word in words: if not bool(word.strip()): continue print(word) model.insertWord(word) model.save_to_pickle("train_data") print("Training completed")
Then run the code to train and store in
pkl file for later use.
Next, we test our trained model and some of
#test_model.py #!/usr/bin/python # -*- coding: utf-8 -*- import trie from codecs import open, decode model = trie.Trie() model.load_from_pickle("train_data") print(model.searchWord('')) # should False print(model.searchWord('គ្រុយ')) # should be True print(model.searchWord('គ្រុ')) # should be False print(model.searchWordPrefix('គ្រុ')) # should be True print(model.searchWordPrefix('គ្រុយ')) # should be False
Let's run the code:
Finally, it's time to use our model to break our very first and simple sentences:
#word_segmentation.py #!/usr/bin/python # -*- coding: utf-8 -*- import trie from codecs import open, decode model = trie.Trie() model.load_from_pickle("train_data") kh_str = "អ្នកចេះនិយាយភាសាខ្មែរទេ?" words =  word = '' for ch in kh_str: word += ch if model.searchWord(word.strip()): words.append(word) print(word.strip()) word = ''
Then run it
It works Awesome.
- Source code: https://github.com/RathanakSreang/KhmerWordSegmentation
We have collected Khmer words from many public data sources. We then prepare those words and train them in our
Trie model. However, the text data from those public data do not container all Khmer word. So next step, we are going to fit code with the given sentences of known and unknown words where we can store those unknow words and use them to improve our model.
All Rights Reserved