Named Entity Recognition and Classification (NERC) is a well-studied NLP task typically focused on coarse-grained named entity (NE) classes. NERC for more fine-grained semantic NE classes has not been systematically studied. This paper quantifies the difficulty of fine-grained NERC (FG-NERC) when performed at large scale on the people domain. We apply unsupervised acquisition methods to constru...

Tree-to-string translation rules are widely used in linguistically syntax-based statistical machine translation systems. In this paper, we propose to use deep syntactic information for obtaining fine-grained translation rules. A head-driven phrase structure grammar (HPSG) parser is used to obtain the deep syntactic information, which includes a fine-grained description of the syntactic property...

In this paper, we present a simple and effective fine-grained feature generation scheme for dependency parsing. We focus on the problem of grammar representation, introducing fine-grained features by splitting various POS tags to different degrees using HowNet hierarchical semantic knowledge. To prevent the oversplitting, we adopt a threshold-constrained bottomup strategy to merge the derived s...

As entity type systems become richer and more fine-grained, we expect the number of types assigned to a given entity to increase. However, most fine-grained typing work has focused on datasets that exhibit a low degree of type multiplicity. In this paper, we consider the high-multiplicity regime inherent in data sources such as Wikipedia that have semi-open type systems. We introduce a set-pred...

Named Entity Recognition (NER) is a Natural Language Processing (NLP) task, which aims to extract useful information from unstructured textual data by detecting and classifying Named Entity (NE) phrases into predefined semantic classes. This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and sho...

We present a new integrated environment for cellular computing and other fine grained applications. It is based upon previous developments concerning cellular computing environments (the ParCeL family) and coarse grained algorithms (the SSCRAP toolbox). It is aimed to be portable and efficient, and at the same time to offer a comfortable abstraction for the developer of fine grained programs. A...

For long, fine-grained version control for software documents has been neglected severely. Typically, software configuration management systems support the management of text or binary files. Unfortunately, text-based tools for fine-grained version control are not adequate for software documents produced in earlier phases in the software life cycle. Frequently, these documents have a graphical ...

Successful fine-grained image classification methods learn subtle details between visually similar (sub-)classes, but the problem becomes significantly more challenging if the details are missing due to low resolution. Encouraged by the recent success of Convolutional Neural Network (CNN) architectures in image classification, we propose a novel resolution-aware deep model which combines convol...

In this research, the effects of a sedimentary basin, environmental conditions, and the passage of time were investigated on consolidation processes and engineering characteristics of fine-grained soils in Kerman city. For this purpose, the natural consolidation curves of soil samples extracted from different locations of Kerman city were compared with the Kerman city intrinsic consolidation li...

The fine-grained soil including the problematic ones can cause many difficulties in project accomplishment. Settlement and swelling are among the problems of the fine-grained soils. The present study compared the effects of the polypropylene waste plastic (PWP) and glass fiber reinforced polymer (GFRP) on geotechnical properties of the clayey soils for the subgrade design. To this end, the PWP ...