Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Building a Syllable Database to Solve the Problem of Khmer Word Segmentation (1703.02166v1)

Published 7 Mar 2017 in cs.CL

Abstract: Word segmentation is a basic problem in natural language processing. With the languages having the complex writing system like the Khmer language in Southern of Vietnam, this problem really very intractable, posing the significant challenges. Although there are some experts in Vietnam as well as international having deeply researched this problem, there are still no reasonable results meeting the demand, in particular, no treated thoroughly the ambiguous phenomenon, in the process of Khmer language processing so far. This paper present a solution based on the syllable division into component clusters using two syllable models proposed, thereby building a Khmer syllable database, is still not actually available. This method using a lexical database updated from the online Khmer dictionaries and some supported dictionaries serving role of training data and complementary linguistic characteristics. Each component cluster is labelled and located by the first and last letter to identify entirety a syllable. This approach is workable and the test results achieve high accuracy, eliminate the ambiguity, contribute to solving the problem of word segmentation and applying efficiency in Khmer language processing.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (1)
  1. Nam Tran Van (1 paper)
Citations (2)

Summary

We haven't generated a summary for this paper yet.