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Why is German dependency parsing more reliable than constituent parsing?
In recent years, research in parsing has extended in several new directions. One of these directions is concerned with parsing languages other than English. Treebanks have become available for many European languages, but also for Arabic, Chinese, or Japanese. However, it was shown that parsing results on these treebanks depend on the types of treebank annotations used. Another direction in parsing research is the development of dependency parsers. Dependency parsing profits from the non-hierarchical nature of dependency relations, thus lexical information can be included in the parsing process in a much more natural way. Especially machine learning based approaches are very successful (cf. e.g.). The results achieved by these dependency parsers are very competitive although comparisons are difficult because of the differences in annotation. For English, the Penn Treebank has been converted to dependencies. For this version, Nivre et al. report an accuracy rate of 86.3%, as compared to an F-score of 92.1 for Charniaks parser. The Penn Chinese Treebank is also available in a constituent and a dependency representations. The best results reported for parsing experiments with this treebank give an F-score of 81.8 for the constituent version and 79.8% accuracy for the dependency version. The general trend in comparisons between constituent and dependency parsers is that the dependency parser performs slightly worse than the constituent parser. The only exception occurs for German, where F-scores for constituent plus grammatical function parses range between 51.4 and 75.3, depending on the treebank, NEGRA or TüBa-D/Z. The dependency parser based on a converted version of Tüba-D/Z, in contrast, reached an accuracy of 83.4%, i.e. 12 percent points better than the best constituent analysis including grammatical functions
ANNOTATED DISJUNCT FOR MACHINE TRANSLATION
Most information found in the Internet is available in English version. However,
most people in the world are non-English speaker. Hence, it will be of great advantage
to have reliable Machine Translation tool for those people. There are many
approaches for developing Machine Translation (MT) systems, some of them are
direct, rule-based/transfer, interlingua, and statistical approaches. This thesis focuses
on developing an MT for less resourced languages i.e. languages that do not have
available grammar formalism, parser, and corpus, such as some languages in South
East Asia. The nonexistence of bilingual corpora motivates us to use direct or transfer
approaches. Moreover, the unavailability of grammar formalism and parser in the
target languages motivates us to develop a hybrid between direct and transfer
approaches. This hybrid approach is referred as a hybrid transfer approach. This
approach uses the Annotated Disjunct (ADJ) method. This method, based on Link
Grammar (LG) formalism, can theoretically handle one-to-one, many-to-one, and
many-to-many word(s) translations. This method consists of transfer rules module
which maps source words in a source sentence (SS) into target words in correct
position in a target sentence (TS). The developed transfer rules are demonstrated on
English → Indonesian translation tasks. An experimental evaluation is conducted to
measure the performance of the developed system over available English-Indonesian
MT systems. The developed ADJ-based MT system translated simple, compound, and
complex English sentences in present, present continuous, present perfect, past, past
perfect, and future tenses with better precision than other systems, with the accuracy
of 71.17% in Subjective Sentence Error Rate metric
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