Question: Which approaches have been applied to solve word segmentation in Vietnamese?

Evidence:
raw_evidence: Maximum Entropy theory is applied to solve Vietnamese word segmentation BIBREF15 , BIBREF22 , BIBREF23 . Some researchers do not want the limit in Markov chain model. So, they use the context around of the word needed to be segmented. Let h is a context, w is a list of words and t is a list of taggers, Le BIBREF15 , BIBREF22 used DISPLAYFORM0
There are several studies about the Vietnamese word segmentation task over the last decade. Dinh et al. started this task with Weighted Finite State Transducer (WFST) approach and Neural Network approach BIBREF9 . In addition, machine learning approaches are studied and widely applied to natural language processing and word segmentation as well. In fact, several studies used support vector machines (SVM) and conditional random fields (CRF) for the word segmentation task BIBREF7 , BIBREF8 . Based on annotated corpora and token-based features, studies used machine learning approaches to build word segmentation systems with accuracy about 94%-97%.
raw_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.
In Markov chain model is represented as a chain of tokens which are observations, and word taggers are represented as predicted labels. Many researchers applied Hidden Markov model to solve Vietnamese word segmentation such as in BIBREF8 , BIBREF20 and so on.
Maximum Entropy theory is applied to solve Vietnamese word segmentation BIBREF15 , BIBREF22 , BIBREF23 . Some researchers do not want the limit in Markov chain model. So, they use the context around of the word needed to be segmented. Let h is a context, w is a list of words and t is a list of taggers, Le BIBREF15 , BIBREF22 used DISPLAYFORM0
To tokenize a Vietnamese word, in HMM or ME, authors only rely on features around a word segment position. Some other features are also affected by adding more special attributes, such as, in case ’?’ question mark at end of sentence, Part of Speech (POS), and so on. Conditional Random Fields is one of methods that uses additional features to improve the selection strategy BIBREF7 .
Support Vector Machines (SVM) is a supervised machine learning method which considers dataset as a set of vectors and tries to classify them into specific classes. Basically, SVM is a binary classifier. however, most classification tasks are multi-class classifiers. When applying SVMs, the method has been extended to classify three or more classes. Particular NLP tasks, like word segmentation and Part-of-speech task, each token/word in documents will be used as a feature vector. For the word segmentation task, each token and its features are considered as a vector for the whole document, and the SVM model will classify this vector into one of the three tags (B-IO).

Answers:
Maximum Entropy, Weighted Finite State Transducer (WFST),  support vector machines (SVM), conditional random fields (CRF)
Maximum matching, Hidden Markov model , Maximum Entropy, Conditional Random Fields , Support Vector Machines