Question: What is the limitation of Maximum Matching?
Evidence: Maximum matching (MM) is one of the most popular fundamental and structural segmentation algorithms for word segmentation BIBREF19 . This method is also considered as the Longest Matching (LM) in several research BIBREF9 , BIBREF3 . It is used for identifying word boundary in languages like Chinese, Vietnamese and Thai. This method is a greedy algorithm, which simply chooses longest words based on the dictionary. Segmentation may start from either end of the line without any difference in segmentation results. If the dictionary is sufficient BIBREF19 , the expected segmentation accuracy is over 90%, so it is a major advantage of maximum matching . However, it does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary.
There are two types of the maximum matching approach: forward MM (FMM) and backward MM (BMM). FMM starts from the beginning token of the sentence while BMM starts from the end. If the sentence has word boundary ambiguities, the output of FMM and BMM will be different. When applying FMM and BMM, there are two types of common errors due to two ambiguities: overlapping ambiguities and combination ambiguity. Overlapping ambiguities occur when the text AB has both word A, B and AB, which are in the dictionary while the text ABC has word AB and BC, which are in the dictionary. For example, "cụ già đi nhanh quá" (there two meanings: ”the old man goes very fast” or ”the old man died suddenly”) is a case of the overlapping ambiguity while "tốc độ truyền thông tin" is a case of the combination ambiguity.
As shown in Figure 2, the method simplification ambiguities, maximum matching is the first step to get features for the modelling stage in machine learning systems, like Conditional Random Fields or Support Vector Machines.
Maximum matching (MM) is one of the most popular fundamental and structural segmentation algorithms for word segmentation BIBREF19 . This method is also considered as the Longest Matching (LM) in several research BIBREF9 , BIBREF3 . It is used for identifying word boundary in languages like Chinese, Vietnamese and Thai. This method is a greedy algorithm, which simply chooses longest words based on the dictionary. Segmentation may start from either end of the line without any difference in segmentation results. If the dictionary is sufficient BIBREF19 , the expected segmentation accuracy is over 90%, so it is a major advantage of maximum matching . However, it does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary.
There are two types of the maximum matching approach: forward MM (FMM) and backward MM (BMM). FMM starts from the beginning token of the sentence while BMM starts from the end. If the sentence has word boundary ambiguities, the output of FMM and BMM will be different. When applying FMM and BMM, there are two types of common errors due to two ambiguities: overlapping ambiguities and combination ambiguity. Overlapping ambiguities occur when the text AB has both word A, B and AB, which are in the dictionary while the text ABC has word AB and BC, which are in the dictionary. For example, "cụ già đi nhanh quá" (there two meanings: ”the old man goes very fast” or ”the old man died suddenly”) is a case of the overlapping ambiguity while "tốc độ truyền thông tin" is a case of the combination ambiguity.
As shown in Figure 2, the method simplification ambiguities, maximum matching is the first step to get features for the modelling stage in machine learning systems, like Conditional Random Fields or Support Vector Machines.
Maximum matching (MM) is one of the most popular fundamental and structural segmentation algorithms for word segmentation [20]. ****However, it does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary.
Answer: It does not solve the problem of ambiguous words and unknown words that do not exist in the dictionary.