multi level alignments as an extensible representation basis for textual entailment algorithms

A major problem in research on Textual Entailment (TE) is the high implementation effort for TE systems. Recently, interoperable standards for annotation and preprocessing have been proposed. In contrast, the algorithmic level remains unstandardized, which makes component re-use in this area very difficult in practice. In this paper, we introduce multi-level alignments as a central, powerful representation for TE algorithms that encourages modular, reusable, multilingual algorithm development. We demonstrate that a pilot open-source implementation of multi-level alignment with minimal features competes with state-of-theart open-source TE engines in three languages

The VIL gene CRAWLING ELEPHANT controls maturation and differentiation in tomato via polycomb silencing

VERNALIZATION INSENSITIVE 3-LIKE (VIL) proteins are PHD-finger proteins that recruit the repressor complex Polycomb Repressive Complex 2 (PRC2) to the promoters of target genes. Most known VIL targets are flowering repressor genes. Here, we show that the tomato VIL gene CRAWLING ELEPHANT (CREL) promotes differentiation throughout plant development by facilitating the trimethylation of Histone H3 on lysine 27 (H3K27me3). We identified the crel mutant in a screen for suppressors of the simple-leaf phenotype of entire (e), a mutant in the AUX/IAA gene ENTIRE/SlIAA9, involved in compound-leaf development in tomato. crel mutants have increased leaf complexity, and suppress the ectopic blade growth of e mutants. In addition, crel mutants are late flowering, and have delayed and aber rant stem, root and flower development. Consistent with a role for CREL in recruiting PRC2, crel mutants show drastically reduced H3K27me3 enrichment at approximately half of the 14,789 sites enriched in wild-type plants, along with upregulation of many underlying genes. Interestingly, this reduction in H3K27me3 across the genome in crel is also associated with gains in H3K27me3 at a smaller number of sites that normally have modest levels of the mark in wild-type plants, suggesting that PRC2 activity is no longer limiting in the absence of CREL. Our results uncover a wide role for CREL in plant and organ differentiation in tomato and suggest that CREL is required for targeting PRC2 activity to, and thus silencing, a spe cific subset of polycomb targets

Neural Disambiguation of Causal Lexical Markers Based on Context

We propose a neural network architecture for the task of causality classification. We claim that the encoding of the meaning of a sentence is required for the disambiguation of its causal meaning. Our results show that our claim holds, and we outperform the state-of-the-art

Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution

Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task's importance, research focus was given mostly to within-document entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreference resolution. Inspired by Lee et al (2012), we jointly model entity and event coreference. We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures. Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus. Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success.Comment: ACL 201

A Consolidated Open Knowledge Representation for Multiple Texts

We propose progressing from Open Information Extraction (OIE) to Open Knowledge Representation (OKR), aiming to represent the information conveyed jointly in a set of texts in an open text-based manner. We do so by consolidating OIE extractions based on entity and predicate coreference, while modeling information containment between coreferring elements via lexical entailment. We suggest that generating OKR structures can be a useful step in the NLP pipeline, to get semantic applications an easy handle on consolidated information across multiple texts