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In this paper, We presents a Chinese medical term recognition system submitted to the competition held by China Conference on Knowledge Graph and Semantic Computing. I compare the performance of Linear Chain Conditional Random Field (CRF) with that of Bi-Directional Long Short Term Memory (LSTM) with Convolutional Neural Network (CNN) and CRF layers performance and find that CRF with augmented ...

Named entity (NE) recognition is a fundamental task in biological relationship mining. This paper considers protein/gene collocates extracted from biological corpora as restrictions to enhance the precision rate of protein/gene name recognition. In addition, we integrate the results of multiple NE recognizers to improve the recall rates. Yapex and KeX, and ABGene and Idgene are taken as example...

The recognition of biomedical concepts in natural text (named entity recognition, NER) is a key technology for automatic or semi-automatic analysis of textual resources. Precise NER tools are a prerequisite for many applications working on text, such as information retrieval, information extraction or document classification. Over the past years, the problem has achieved considerable attention ...

We propose named entity abstraction methods with fine-grained named entity labels for improving statistical machine translation (SMT). The methods are based on a bilingual named entity recognizer that uses a monolingual named entity recognizer with transliteration. Through experiments, we demonstrate that incorporating fine-grained named entities into statistical machine translation improves th...

This paper describe the runs submitted by the UBC team at TAC-KBP 2014 for both English Entity Discovery and Linking (EDL) and Diagnostic Entity Linking (DEL) tasks. Our main interest was to compare the performance between two totally different name entity recognizer systems and to combine them with three different name entity disambiguation systems that were developed for the TACKBP 2013 EL ta...

This paper presents a system for visualization of large amounts of new stories. In the first phase, the new stories are preprocessed for the purpose of name -entity extraction. Next, a graph of relationships between the extracted name entities is created, where each name entity represents one vertex in the graph and two name entities are connected if they appear in the same document. The graph ...

Web documents are heterogeneous and complex. There are complicated associations within a single Web document and there can be complex relations with other documents as well. The high interactions between the terms of the documents show merely vague and thinly ambiguous meanings. Efficient and efficient grouping methods are required to discover latent and consistent meanings in context. This art...

We describe experiments with building a recognizer for disease names in Bulgarian clinical epicrises, where both the language and the domain are different from those in mainstream research, which has focused on PubMed articles in English. We show that using a general framework such as GATE and an appropriate pragmatic methodology can yield significant speed up of the manual annotation: we achie...