Abstract Recognizing named entities (NEs) is commonly treated as a classification problem, and class tag for word or an NE candidate in sentence predicted. In recent neural network developments, deep structures that map categorized features into continuous representations have been adopted. Using this approach, dense space saturated with high-order abstract semantic information unfolded, the pr...

The task of named entity recognition can be transformed into a machine reading comprehension by associating the query and its context, which contains information, with encoding layer. In this process, model learns priori knowledge about entity, from query, to achieve good results. However, as length context increases, struggles an increasing number less relevant words, distract it task. Althoug...

Abstract Named Entity Recognition (NER) models have achieved good performance in recent years but also some shortcomings. Existing regard NER as a sequence labeling task for label prediction, without considering the impact of different stages entity recognition process on final result. can be viewed two separate subtasks: boundary detection and type prediction task. The subtasks transmit inform...

This report describes the methods and results of a system developed for the GREC Named Entity Recognition and GREC Named Entity Regeneration Challenges 2010. We explain our process of automatically annotating surface text, as well as how we use this output to select improved referring expressions for named entities.

This paper explores the possible role of named entities in an automatic indexing process, based on text in subtitles. This is done by analyzing entity types, name density and name frequencies in subtitles and metadata records from different TV programs. The name density in metadata records is much higher than the name density in subtitles, and named entities with high frequencies in the subtitl...

In the paper we present an adaptation of Liner2 framework to solve the BSNLP 2017 shared task on multilingual named entity recognition. The tool is tuned to recognize and lemmatize named entities for Polish.

Named entities in topics are a major factor contributing to the quality of retrieval results. In this paper, we report on an analysis on the correlation between the number of named entities present in a topic and the retrieval quality achieved for these topics by retrieval systems within CLEF. We found that a medium positive correlation exists for German, English and Spanish topics. Furthermore...

We describe a pipeline system, Named Entity Recognizer of Chemicals (NEROC), that aims to identify chemical entities mentioned in free texts. The system is based on a machine learning approach, a Conditional Random Field (CRF), and a selection of feature sets that are used to capture specific characteristics of chemical named entities. In this paper, we report results that produced by CRF model...