Pursuing accurate and robust recognizers has been a long-lasting goal for scene text recognition (STR) researchers. Recently, attention-based methods have demonstrated their effectiveness achieved impressive results on public benchmarks. The attention mechanism enables models to recognize with severe visual distortions by leveraging contextual information. However, recent studies revealed that ...

One of the most difficult parts of the natural language understanding process is forming a semantic interpretation of the text. A reader must often make multiple inferences to understand the motives of actors and to causally connect actions that are unrelated on the basis of surface semantics alone. The inference process is complicated by the fact that text is often ambiguous both lexically and...

In this work, we report our contributions to the BioC Track of BioCreative V for the task of identifying genetic interaction evidence passages. Text describing genetic interactions is difficult to identify due to no simple definition for these interactions and lack of training data. We prepared two manually annotated datasets containing 1793 PubMed abstract and 1000 full text sentences, respect...

The Word Expert Parser is a computer program that analyzes fragments of natural language text in order to extract their meaning in context. The construction of the program has led to the development of a linguistic theory based on notions orthogonal to those traditionally found at the heart of such theories. Word Expert Parsing explains the understanding of textual fragments containing highly i...

While Message Sequence Charts (MSCs) and related notations are valuable in representing basic point-to-point (p2p) communication, they lack adequate support for important aspects of interaction modeling, including broadcasting, preemption, progress/liveness specifications, and overlapping interactions. Such support, however, is needed particularly in the context of service-oriented specificatio...

In hierarchical phrase-based machine translation, a rule table is automatically learned by heuristically extracting synchronous rules from a parallel corpus. As a result, spuriously many rules are extracted which may be composed of various incorrect rules. The larger rule table incurs more disk and memory resources, and sometimes results in lower translation quality. To resolve the problems, we...

Manual curation of biomedical literature has become extremely tedious process due to its exponential growth in recent years. To extract meaningful information from such large and unstructured text, newer and more efficient mining tool is required. Here, we introduce PALM-IST, a computational platform that not only allows users to explore biomedical abstracts using keyword based text mining but ...

We present a statistical machine translation model that uses hierarchical phrases—phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a parallel text without any syntactic annotations. Thus it can be seen as combining fundamental ideas from both syntax-based translation and phrase-based translation. We describe our system’s training and ...