We propose a novel Named Entity Recognition (NER) system based on a machine learning technique and a semantic network. .e NER system is able to exploit the advantages of semantic information, coming from Expert System proprietary technology, Cogito. NER is a task of Natural Language Processing (NLP) which consists in detecting, from an unforma.ed text source and classify Named Entities (NE), i.e. real-world entities that can be denoted with a rigid designator. To address this problem, the chosen approach is a combination of machine learning and deep semantic processing. .e machine learning method used is Conditional Random Fields (CRF). CRF is particularly suitable for the task because it analyzes an input sequence of tokens considering it as a whole, instead of one item at a time. CRF has been trained not only with classical information, available a.er a simple computation or anyway with li.le e.ort, but with the addition of semantic information. Semantic information is obtained with Sensigrafo and Semantic Disambiguator, which are the proprietary semantic network and semantic engine of Expert System, respectively. .e results are encouraging, as we can experimentally prove the improvements in the NER task obtained by exploiting semantics, in particular when the training data size decreases.

Conditional random fields with semantic enhancement for named-entity recognition / Bergamaschi, S.; Cappelli, A.; Circiello, A.; Varone, M.. - 129475:(2017), pp. 1-7. ((Intervento presentato al convegno 7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017 tenutosi a ita nel 2017 [10.1145/3102254.3102286].

Conditional random fields with semantic enhancement for named-entity recognition

Bergamaschi S.;Cappelli A.;Circiello A.;
2017

Abstract

We propose a novel Named Entity Recognition (NER) system based on a machine learning technique and a semantic network. .e NER system is able to exploit the advantages of semantic information, coming from Expert System proprietary technology, Cogito. NER is a task of Natural Language Processing (NLP) which consists in detecting, from an unforma.ed text source and classify Named Entities (NE), i.e. real-world entities that can be denoted with a rigid designator. To address this problem, the chosen approach is a combination of machine learning and deep semantic processing. .e machine learning method used is Conditional Random Fields (CRF). CRF is particularly suitable for the task because it analyzes an input sequence of tokens considering it as a whole, instead of one item at a time. CRF has been trained not only with classical information, available a.er a simple computation or anyway with li.le e.ort, but with the addition of semantic information. Semantic information is obtained with Sensigrafo and Semantic Disambiguator, which are the proprietary semantic network and semantic engine of Expert System, respectively. .e results are encouraging, as we can experimentally prove the improvements in the NER task obtained by exploiting semantics, in particular when the training data size decreases.
7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017
ita
2017
129475
1
7
Bergamaschi, S.; Cappelli, A.; Circiello, A.; Varone, M.
Conditional random fields with semantic enhancement for named-entity recognition / Bergamaschi, S.; Cappelli, A.; Circiello, A.; Varone, M.. - 129475:(2017), pp. 1-7. ((Intervento presentato al convegno 7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017 tenutosi a ita nel 2017 [10.1145/3102254.3102286].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1222564
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