Maximizing the joint dependency with a minimum size of variables is generally the main task of feature selection. For obtaining a minimal subset, while trying to maximize the joint dependency with the target variable, the redundancy among selected variables must be reduced to a minimum. In this paper, we propose a method based on recently popular minimum Redundancy-Maximum Relevance (mRMR) crit...

With the growth of database complexity and size it is becoming increasingly necessary to automate the analysis of data. Part of this automation is the development of inductive algorithms to search for characterising rules. The paper introduces the formal concept of an induced dependency as a descriptor for the relationships which characterise the data within a database. This is done within the ...

Long-term context is crucial to joint Chinese word segmentation and POS tagging (S&T) task. However, most of machine learning based methods extract features from a window of characters. Due to the limitation of window size, these methods can not exploit the long distance information. In this work, we propose a long dependency aware deep architecture for joint S&T task. Specifically, to simulate...

We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented with L-DMV, a lexicalized version of Dependency Model with Valence (Klein and Manning, 2004) and L-NDMV, our lexicalized extension of the Neural Dependency Model with Valence (Jiang et al., 2016). We find that L-DMV onl...

We present a cost-effective strategy for the creation of a mid-size fine-grained Spanish dependency tree bank of surface-, deep-syntactic and semantic structures as defined in the Meaning-Text Theory. The strategy starts from a small seed dependency corpus, the AnCora corpus, whose annotation is considerably more coarse-grained than our target annotation. We show that this discrepancy can be br...

We describe the system AProVE, an automated prover to verify (innermost) termination of term rewrite systems (TRSs). For this system, we have developed and implemented efficient algorithms based on classical simplification orders, dependency pairs, and the size-change principle. In particular, it contains many new improvements of the dependency pair approach that make automated termination prov...

We present a cost effective strategy for the creation of a mid-size fine-grained dependency treebank of surfaceand deep-syntactic structures as defined in the Meaning-Text Theory for Spanish. The strategy starts from a small seed dependency corpus, the AnCora corpus, whose annotation is considerably more coarse-grained than our target annotation. We show that this discrepancy can be bridged lar...

Despite the recent rapid progress in high throughput measurements of biological data, it is still difficult to gather all of the reaction speed data in biological pathways. This paper presents a Petri net-based algorithm that can derive estimated values for non-valid reaction speeds in a signaling pathway from biologically-valid data. In fact, these reaction speeds are reflected based on the de...