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NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Named entity recognition (NER) is widely used in natural language proces...
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Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers
Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNE...
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Cascaded Models for Better Fine-Grained Named Entity Recognition
Named Entity Recognition (NER) is an essential precursor task for many n...
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CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
In this paper, we introduce the NER dataset from CLUE organization (CLUE...
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Semantic Entity Retrieval Toolkit
Unsupervised learning of low-dimensional, semantic representations of wo...
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Toward Automatic Understanding of the Function of Affective Language in Support Groups
Understanding expressions of emotions in support forums has considerable...
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A Pragmatic Guide to Geoparsing Evaluation
Empirical methods in geoparsing have thus far lacked a standard evaluati...
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TexSmart: A Text Understanding System for Fine-Grained NER and Enhanced Semantic Analysis
This technique report introduces TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. Compared to most previous publicly available text understanding systems and tools, TexSmart holds some unique features. First, the NER function of TexSmart supports over 1,000 entity types, while most other public tools typically support several to (at most) dozens of entity types. Second, TexSmart introduces new semantic analysis functions like semantic expansion and deep semantic representation, that are absent in most previous systems. Third, a spectrum of algorithms (from very fast algorithms to those that are relatively slow but more accurate) are implemented for one function in TexSmart, to fulfill the requirements of different academic and industrial applications. The adoption of unsupervised or weakly-supervised algorithms is especially emphasized, with the goal of easily updating our models to include fresh data with less human annotation efforts. The main contents of this report include major functions of TexSmart, algorithms for achieving these functions, how to use the TexSmart toolkit and Web APIs, and evaluation results of some key algorithms.
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