We propose a generic method to perform lexical disambiguation in lexicalized grammatical formalisms. It relies on dependency constraints between words. The soundness of the method is due to invariant properties of the parsing in a given grammar that can be computed statically from the grammar.

Language proficiency tests are used to evaluate and compare the progress of language learners. We present an approach for automatic difficulty prediction of C-tests that performs on par with human experts. On the basis of detailed analysis of newly collected data, we develop a model for C-test difficulty introducing four dimensions: solution difficulty, candidate ambiguity, inter-gap dependency...

A variety of statistical methods for noun compound analysis are implemented and compared. The results support two main conclusions. First, the use of conceptual association not only enables a broad coverage, but also improves the accuracy. Second, an analysis model based on dependency grammar is substantially more accurate than one based on deepest constituents, even though the latter is more p...

We show that categories induced by unsupervised word clustering can surpass the performance of gold part-of-speech tags in dependency grammar induction. Unlike classic clustering algorithms, our method allows a word to have different tags in different contexts. In an ablative analysis, we first demonstrate that this context-dependence is crucial to the superior performance of gold tags — requir...

We link generative dependency grammars meeting natural modularity requirements with underspecified semantics of Discourse Plans intended to account for exactly those meaning components that grammars of languages mark for. We complete this link with a natural compilation of the modular dependency grammars into strongly equivalent efficiently analysed categorial dependency grammars.

We improve parse error detection, weighting dependency information on the basis of simulated parses. Such simulations extend the training grammar, and, although the simulations are not wholly correct or incorrect—as observed from the results with different weightings for small treebanks—they help to determine whether a new parse fits the training grammar.

We show how to turn a large-scale syntactic dictionary into a dependency-based unification grammar where each piece of lexical information calls a separate rule, yielding a super granular grammar. Subcategorization, raising and control verbs, auxiliaries and copula, passivization, and tough-movement are discussed. We focus on the semantics-syntax interface and offer a new perspective on syntact...

According to the Menzerath-Altmann law, there is a relation between the size of the whole and the mean size of its parts. The validity of the law was demonstrated on relations between several language units, e.g., the longer a word, the shorter the syllables the word consists of. In this paper it is shown that the law is valid also in syntactic dependency structure in Czech. In particular, long...

This paper describes the participation of iKernels system in the Semantic Textual Similarity (STS) shared task at *SEM 2013. Different from the majority of approaches, where a large number of pairwise similarity features are used to learn a regression model, our model directly encodes the input texts into syntactic/semantic structures. Our systems rely on tree kernels to automatically extract a...