The paper presents a formal lexicalized dependency grammar based on Meaning-Text theory. This grammar associates semantic graphs with sentences. We propose a fragment of a grammar for French, including the description of extractions. The main particularity of our grammar is it that it builds bubble trees as syntactic representations, that is, trees whose nodes can be filled by bubbles, which ca...

This contribution presents a dependency grammar (DG) account of control and raising in English. Due to the minimalism of DG analyses of sentence structure, the difference between control and raising cannot be captured in the syntactic structure alone. The situation forces the DG account to reach to some other aspect of dependency syntax other than the raw hierarchies of structure to account for...

This paper describes ICL’s participation at RTE-7. We chose the Main task. Textual entailment is a problem to predict whether an entailment holds for a given test-hypothesis pair. We built an inference model to solve this problem by means of using dependency syntax analysis (by Stanford Parser), lexical knowledge base (e.g. Wordnet), web information (e.g. Wikipedia) and probability method.

In this paper we describe a memory-based machine learning system that extracts biomedical events from texts relying on information from contextual and syntactic features. The main characteristics of the system are that it uses information from dependency syntax and that it integrates classifiers that learn event triggers and event participants jointly. The results show that this system is more ...

A characterization is given of the class of tree translations definable in monadic second-order logic (MSO), in terms of macro tree transducers. The first main result is that the MSO definable tree translations are exactly those tree translations realized by macro tree transducers (MTTs) with regular look-ahead that are single use restricted. For this the single use restriction known from attri...

Neural network (“deep learning”) models are taking over machine learning approaches for language by storm. In particular, recurrent neural networks (RNNs), which are flexible non-markovian models of sequential data, were shown to be effective for a variety of language processing tasks. Somewhat surprisingly, these seemingly purely sequential models are very capable at modeling syntactic phenome...

In this paper, we present a revision of the training set of the METU-Sabancı Turkish syntactic dependency treebank composed of 4997 sentences in accordance with the principles of the Meaning-Text Theory (MTT). MTT reflects the multilayered nature of language by a linguistic model in which each linguistic phenomenon is treated at its corresponding level(s). Our analysis of the METU-Sabancı synta...

We present SETIMES.HR— the first linguistically annotated corpus of Croatian that is freely available for all purposes. The corpus is built on top of the SETIMES parallel corpus of nine Southeast European languages and English. It is manually annotated for lemmas, morphosyntactic tags, named entities and dependency syntax. We couple the corpus with domain-sensitive test sets for Croatian and Se...

An adequate, complete, and economical linguistic theory is necessary for MT and the question is whether a consistent use of the often unduly neglected dependency syntax, including a systematic description of topic and focus, cannot serve as a reliable base for the grammar of an interlingua, or of a set of interrelated interface structures. i. As Slocum (198~)convinc~ly shows, the attitude towar...

We describe results of investigation of a specific type of discontinuous constructions, namely non-projective constructions concerning verbs and their arguments. This topic is especially important for languages with a relatively free word order, such as Czech, which is the language we have primarily worked with. For comparison, we have included some results for English. The corpora used for bot...