3,144 research outputs found
KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition
KnowNER is a multilingual Named Entity Recognition (NER) system that
leverages different degrees of external knowledge. A novel modular framework
divides the knowledge into four categories according to the depth of knowledge
they convey. Each category consists of a set of features automatically
generated from different information sources (such as a knowledge-base, a list
of names or document-specific semantic annotations) and is used to train a
conditional random field (CRF). Since those information sources are usually
multilingual, KnowNER can be easily trained for a wide range of languages. In
this paper, we show that the incorporation of deeper knowledge systematically
boosts accuracy and compare KnowNER with state-of-the-art NER approaches across
three languages (i.e., English, German and Spanish) performing amongst
state-of-the art systems in all of them
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201
Chinese Named Entity Recognition Method for Domain-Specific Text
The Chinese named entity recognition (NER) is a critical task in natural language processing, aiming at identifying and classifying named entities in text. However, the specificity of domain texts and the lack of large-scale labelled datasets have led to the poor performance of NER methods trained on public domain corpora on domain texts. In this paper, a named entity recognition method incorporating sentence semantic information is proposed, mainly by adaptively incorporating sentence semantic information into character semantic information through an attention mechanism and a gating mechanism to enhance entity feature representation while attenuating the noise generated by irrelevant character information. In addition, to address the lack of large-scale labelled samples, we used data self-augmentation methods to expand the training samples. Furthermore, we introduced a Weighted Strategy considering that the low-quality samples generated by the data self-augmentation process can have a negative impact on the model. Experiments on the TCM prescriptions corpus showed that the F1 values of our method outperformed the comparison methods
Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling
In this paper we propose and carefully evaluate a sequence labeling framework
which solely utilizes sparse indicator features derived from dense distributed
word representations. The proposed model obtains (near) state-of-the art
performance for both part-of-speech tagging and named entity recognition for a
variety of languages. Our model relies only on a few thousand sparse
coding-derived features, without applying any modification of the word
representations employed for the different tasks. The proposed model has
favorable generalization properties as it retains over 89.8% of its average POS
tagging accuracy when trained at 1.2% of the total available training data,
i.e.~150 sentences per language
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