Extracting Named Entities Using Support Vector Machines

Wu, Yu-Chieh; Fan, Teng-Kai; Lee, Yue‐Shi; Yen, Show‐Jane

doi:10.1007/11683568_8

Cited by 38 publications

(29 citation statements)

References 14 publications

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“…As the expert classification is costly and time-consuming task, automatic entity classification traditionally has been part of the NER (Named Entity Recognition) research where the entity is first au-tomatically identified and then classified [11,12,13,14] according to a number of categories. Unfortunately, such automatic algorithms, require predefined concept-based seeds for training and manually defined rules, which complexity depends on the number of involved classes.…”

Section: Entity Annotation and Classificationmentioning

confidence: 99%

Using microtasks to crowdsource DBpedia entity classification: A study in workflow design

Qiong

Simperl

Zerr

et al. 2018

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Abstract. DBpedia is at the core of the Linked Open Data Cloud and widely used in research and applications. However, it is far from being perfect. Its content suffers from many flaws, as a result of factual errors inherited from Wikipedia or incomplete mappings from Wikipedia infobox to DBpedia ontology. In this work we focus on one class of such problems, un-typed entities. We propose a hierarchical tree-based approach to categorize DBpedia entities according to the DBpedia ontology using human computation and paid microtasks. We analyse the main dimensions of the crowdsourcing exercise in depth in order to come up with suggestions for workflow design and study three different workflows with automatic and hybrid prediction mechanisms to select possible candidates for the most specific category from the DBpedia ontology. To test our approach, we run experiments on CrowdFlower using a gold standard dataset of 120 previously unclassified entities. In our studies human-computation driven approaches generally achieved higher precision at lower cost when compared to workflows with automatic predictors. However, each of the tested workflows has its merit and none of them seems to perform exceptionally well on the entities that the DBpedia Extraction Framework fails to classify. We discuss these findings and their potential implications for the design of effective crowdsourced entity classification in DBpedia and beyond.

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Section: Entity Annotation and Classificationmentioning

confidence: 99%

Using microtasks to crowdsource DBpedia entity classification: A study in workflow design

Qiong

Simperl

Zerr

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Some of the ML methods that had been used for NER algorithm includes artificial neural network (ANN) [9], Hidden Markov Model (HMM) [14], Maximum Entropy Model (MaxEnt) [15], Decision Tree [16], Support Vector Machine [17] and etc. ML methods are applicable for different domain-specific NER systems but it requires a large collection of annotated data.…”

Section: Types Of Nermentioning

confidence: 99%

Malay Named Entity Recognition Based on Rule-Based Approach

Alfred¹,

Leong²,

On³

et al. 2014

IJMLC

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Abstract-A Named-Entity Recognition (NER) is part of the process in Text Mining and it is a very useful process for information extraction. This NER tool can be used to assist user in identifying and detecting entities such as person, location or organization. However, different languages may have different morphologies and thus require different NER processes. For instance, an English NER process cannot be applied in processing Malay articles due to the different morphology used in different languages. This paper proposes a Rule-Based Named-Entity Recognition algorithm for Malay articles. The proposed Malay NER is designed based on a Malay part-of-speech (POS) tagging features and contextual features that had been implemented to handle Malay articles. Based on the POS results, proper names will be identified or detected as the possible candidates for annotation. Besides that, there are some symbols and conjunctions that will also be considered in the process of identifying named-entity for Malay articles. Several manually constructed dictionaries will be used to handle three named-entities; Person, Location and Organizations. The experimental results show a reasonable output of 89.47% for the F-Measure value. The proposed Malay NER algorithm can be further improved by having more complete dictionaries and refined rules to be used in order to identify the correct Malay entities system.

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“…The author of [3] [5] had shown that Conditional Random Fields (CRFs) are undirected graphical models used to calculate the conditional probability of values on designated output nodes given values assigned to other designated input nodes. A conditional random field (CRF) is a type of discriminative probabilistic model used for the labeling sequential data such as natural language text.…”

Section: Related Workmentioning

confidence: 99%

“…A conditional random field (CRF) is a type of discriminative probabilistic model used for the labeling sequential data such as natural language text. The author of [6] had shown that the maximum entropy [ME] [3], framework estimates probabilities based on the principle of making as few assumptions as possible, other than the constraints imposed. Such constraints are derived from training data, expressing some relationship between features and outcome.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Named Entity Recognition for Nepali Text Using Support Vector Machines

Bam¹,

Shahi²

2014

IIM

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Named Entity Recognition aims to identify and to classify rigid designators in text such as proper names, biological species, and temporal expressions into some predefined categories. There has been growing interest in this field of research since the early 1990s. Named Entity Recognition has a vital role in different fields of natural language processing such as Machine Translation, Information Extraction, Question Answering System and various other fields. In this paper, Named Entity Recognition for Nepali text, based on the Support Vector Machine (SVM) is presented which is one of machine learning approaches for the classification task. A set of features are extracted from training data set. Accuracy and efficiency of SVM classifier are analyzed in three different sizes of training data set. Recognition systems are tested with ten datasets for Nepali text. The strength of this work is the efficient feature extraction and the comprehensive recognition techniques. The Support Vector Machine based Named Entity Recognition is limited to use a certain set of features and it uses a small dictionary which affects its performance. The learning performance of recognition system is observed. It is found that system can learn well from the small set of training data and increase the rate of learning on the increment of training size.

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Extracting Named Entities Using Support Vector Machines

Cited by 38 publications

References 14 publications

Using microtasks to crowdsource DBpedia entity classification: A study in workflow design

Using microtasks to crowdsource DBpedia entity classification: A study in workflow design

Malay Named Entity Recognition Based on Rule-Based Approach

Named Entity Recognition for Nepali Text Using Support Vector Machines

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