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**1 - 2**of**2**### Parsing German Topological Fields with Probabilistic Context-Free Grammars

, 2009

"... Syntactic analysis is useful for many natural language processing applications requiring further semantic analysis. Recent research in statistical parsing has produced a number of highperformance parsers using probabilistic context-free (PCFG) models to parse English text, such as (Collins, 2003; Ch ..."

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Syntactic analysis is useful for many natural language processing applications requiring further semantic analysis. Recent research in statistical parsing has produced a number of highperformance parsers using probabilistic context-free (PCFG) models to parse English text, such as (Collins, 2003; Charniak and Johnson, 2005). Problems arise, however, when applying these methods to parse sentences in freer-word-order languages. Such languages as Russian, Warlpiri, and German feature syntactic constructions that produce discontinuous constituents, directly violating one of the crucial assumptions of context-free models of syntax. While PCFG technologies may thus be inadequate for full syntactic analysis of all phrasal structure in these languages, clausal structure can still be fruitfully parsed with these methods. In particular, we examine applying PCFG parsing to parse the topological field structure of German. These topological fields provide a high-level description of the major sections of a clause in relation to the clausal main verb and the subordinating heads and appear in strict linear sequences amenable to PCFG parsing. They are useful for tasks such as deep syntactic analysis, part-of-speech tagging and coreference resolution. In this work, we apply an unlexicalized, latent variable-based parser (Petrov et al., 2006) to

### Two Criteria for Model Selection in Multiclass Support Vector Machines

, 2008

"... Practical applications call for efficient model selection criteria for multiclass support vector machine (SVM) classification. To solve this problem, this paper develops two model selection criteria by combining or redefining the radius–margin bound used in binary SVMs. The combination is justified ..."

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Practical applications call for efficient model selection criteria for multiclass support vector machine (SVM) classification. To solve this problem, this paper develops two model selection criteria by combining or redefining the radius–margin bound used in binary SVMs. The combination is justified by linking the test error rate of a multiclass SVM with that of a set of binary SVMs. The redefinition, which is relatively heuristic, is inspired by the conceptual relationship between the radius–margin bound and the class separability measure. Hence, the two criteria are developed from the perspective of model selection rather than a generalization of the radius–margin bound for multiclass SVMs. As demonstrated by extensive experimental study, the minimization of these two criteria achieves good model selection on most data sets. Compared with the k-fold cross validation which is often regarded as a benchmark, these two criteria give rise to comparable performance with much less computational overhead, particularly when a large number of model parameters are to be optimized.