The morphosyntactic disambiguation of verbs is a crucial pre-processing step for the syntactic analysis of morphologically rich languages like German and domains with complex clause structures like law texts. This paper explores how much linguistically motivated rules can contribute to the task. It introduces an incremental system of verbal morphosyntactic disambiguation that exploits the concept of topological fields. The system presented is capable of reducing the rate of POS-tagging mistakes from 10.2% to 1.6%. The evaluation shows that this reduction is mostly gained through checking the compatibility of morphosyntactic features within the long-distance syntactic relationships of discontinuous verbal elements. Furthermore, the present study shows that in law texts, the average distance between the left and right bracket of clauses is relatively large (9.5 tokens), and that in this domain, a wide context window is therefore necessary for the morphosyntactic disambiguation of verbs. Abstract. The morphosyntactic disambiguation of verbs is a crucial pre-processing step for the syntactic analysis of morphologically rich languages like German and domains with complex clause structures like law texts. This paper explores how much linguistically motivated rules can contribute to the task. It introduces an incremental system of verbal morphosyntactic disambiguation that exploits the concept of topological fields. The system presented is capable of reducing the rate of POS-tagging mistakes from 10.2% to 1.6%. The evaluation shows that this reduction is mostly gained through checking the compatibility of morphosyntactic features within the long-distance syntactic relationships of discontinuous verbal elements. Furthermore, the present study shows that in law texts, the average distance between the left and right bracket of clauses is relatively large (9.5 tokens), and that in this domain, a wide context window is therefore necessary for the morphosyntactic disambiguation of verbs.
Abstract. In this paper, we present a segmentation system for German texts. We apply conditional random fields (CRF), a statistical sequential model, to a type of text used in private communication. We show that by segmenting individual punctuation, and by taking into account freestanding lines and that using unsupervised word representation (i. e., Brown clustering, Word2Vec and Fasttext) achieved a label accuracy of 96% in a corpus of postcards used in private communication.
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