This paper describes a dependency structure analysis of Japanese sentences based on the maximum entropy models . Our model is created by learning the weights of some features from a training corpus to predict the dependency between bunsetsus or phrasal units . The dependency accuracy of our system is 87.2 % using the Kyoto University corpus . We discuss the contribution of each feature set and the relationship between the number of training data and the accuracy . Dependency structure analysis is one of the basic techniques in Japanese sentence analysis . The Japanese dependency structure is usually represented by the relationship between phrasal units called ' bunsetsu . ' The analysis has two conceptual steps . In the first step , a dependency matrix is prepared . Each element of the matrix represents how likely one bunsetsu is to depend on the other . In the second step , an optimal set of dependencies for the entire sentence is found . In this paper , we will mainly discuss the first step , a model for estimating dependency likelihood . So far there have been two different approaches to estimating the dependency likelihood , One is the rule-based approach , in which the rules are created by experts and likelihoods are calculated by some means , including semiautomatic corpusbased methods but also by manual assignment of scores for rules . However , hand-crafted rules have the following problems . • They have a problem with their coverage . Because there are many features to find correct dependencies , it is difficult to find them manually . • They also have a problem with their consistency , since many of the features compete with each other and humans can not create consistent rules or assign consistent scores . • As syntactic characteristics differ across different domains , the rules have to be changed when the target domain changes . It is costly to create a new hand-made rule for each domain . At / other approach is a fully automatic corpusbased approach . This approach has the potential to overcome the problems of the rule-based approach . It automatically learns the likelihoods of dependencies from a tagged corpus and calculates the best dependencies for an input sentence . We take this approach . This approach is taken by some other systems ( Collins , 1996 ; Fujio and Matsumoto , 1998 ; Haruno et ah , 1998 ) . The parser proposed by Ratnaparkhi ( Ratnaparkhi , 1997 ) is considered to be one of the most accurate parsers in English . Its probability estimation is based on the maximum entropy models . We also use the maximum entropy model . This model learns the weights of given features from a training corpus . The weights are calculated based on the frequencies of the features in the training data . The set of features is defined by a human . In our model , we use features of bunsetsu , such as character strings , parts of speech , and inflection types of bunsetsu , as well as information between bunsetsus , such as the existence of punctuation , and the distance between bunsetsus . The probabilities of dependencies are estimated from the model by using those features in input sentences . We assume that the overall dependencies in a whole sentence can be determined as the product of the probabilities of all the dependencies in the sentence . Now , we briefly describe the algorithm of dependency analysis . It is said that Japanese dependencies have the following characteristics . ( 1 ) Dependencies are directed from left to right ( 2 ) Dependencies do not cross ( 3 ) A bunsetsu , except for the rightmost one , depends on only one bunsetsu ( 4 ) In many cases , the left context is not necessary to determine a dependency 1 The analysis method proposed in this paper is designed to utilize these features . Based on these properties , we detect the dependencies in a sentence by analyzing it backwards ( from right to left ) . In the past , such a backward algorithm has been used with rule-based parsers ( e.g. , ( Fujita , 1988 ) ) . We applied it to our statistically based approach . Because of the statistical property , we can incorporate a beam search , an effective way of limiting the search space in a backward analysis . This paper described a Japanese dependency structure analysis based on the maximum entropy model . Our model is created by learning the weights of some features from a training corpus to predict the dependency between bunsetsus or phrasal units . The probabilities of dependencies between bunsetsus are estimated by this model . The dependency accuracy of our system was 87.2 % using the Kyoto University corpus . In our experiments without the feature sets shown in Tables 1 and 2 , we found that some basic and combined features strongly contribute to improve the accuracy . Investigating the relationship between the number of training data and the accuracy , we found that good accuracy can be achieved even with a very small set of training data . We believe that the maximum entropy framework has suitable characteristics for overcoming the data sparseness problem . There are several future directions . In particular , we are interested in how to deal with coordinate structures , since that seems to be the largest problem at the moment .

Challenge: Existing models that estimate dependency likelihood for Japanese use hand-crafted rules which have few problems such as low coverage, consistency, and domain scalability.
Approach: They propose to use a maximum entropy model which leans weights from a training corpus using human-defined features of bunsetsu, Japanese phrasal units.
Outcome: The proposed system achieves 87.2% accuracy on the Kyoto University corpus, and they find that basic and combined features contribute to improvements.