A hybrid machine learning approach for information extraction from free text

被引：1

作者：

Neumann, G ^{[1
]}

机构：

[1] DFKI Saarbrucken, LT Lab, D-66123 Saarbrucken, Germany

来源：

From Data and Information Analysis to Knowledge Engineering | 2006年

关键词：

D O I：

10.1007/3-540-31314-1_47

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a hybrid machine learning approach for information extraction from unstructured documents by integrating a learned classifier based on the Maximum Entropy Modeling (MEM), and a classifier based on our work on Data-Oriented Parsing (DOP). The hybrid behavior is achieved through a voting mechanism applied by an iterative tag-insertion algorithm. We have tested the method on a corpus of German newspaper articles about company turnover, and achieved 85.2% F-measure using the hybrid approach, compared to 79.3% for MEM and 51.9% for DOP when running them in isolation.

引用

页码：390 / 397

页数：8

共 50 条

[1] A machine learning approach to information extraction
Téllez-Valero, A
Montes-y-Gómez, M
Villaseñor-Pineda, L
[J]. COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, 2005, 3406 : 539 - 547
[2] A Hybrid Approach for Spatial Information Extraction from Natural Language Text
Hassini, Nesrine
Mahmoudi, Khaoula
Faiz, Sami
[J]. 2023 20TH ACS/IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, AICCSA, 2023,
[3] Information extraction and classification from free text using a neural approach
Gallo, Ignazio
Binagbi, Elisabetta
[J]. PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS, 2007, 4756 : 921 - 929
[4] Selection of diagnosis with oncologic relevance information from histopathology free text reports: A machine learning approach
Viscosi, Carmelo
Fidelbo, Paolo
Benedetto, Andrea
Varvara, Massimo
Ferrante, Margherita
[J]. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2022, 160
[5] A novel machine learning approach for scene text extraction
Ansari, Ghulam Jillani
Shah, Jamal Hussain
Yasmin, Mussarat
Sharif, Muhammad
Fernandes, Steven Lawrence
[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2018, 87 : 328 - 340
[6] Information Extraction and Machine Learning: Auto-Marking Short Free Text Responses to Science Questions
Sukkarieh, Jana Z.
Pulman, Stephen G.
[J]. ARTIFICIAL INTELLIGENCE IN EDUCATION: SUPPORTING LEARNING THROUGH INTELLIGENT AND SOCIALLY INFORMED TECHNOLOGY, 2005, 125 : 629 - 637
[7] Combining Relations for Information Extraction from Free Text
Maslennikov, Mstislav
Chua, Tat-Seng
[J]. ACM TRANSACTIONS ON INFORMATION SYSTEMS, 2010, 28 (03)
[8] A Supervised Machine Learning Approach for Temporal Information Extraction
Kolya, Anup Kumar
Ekbal, Asif
Bandyopadhyay, Sivaji
[J]. PROCEEDINGS OF THE 24TH PACIFIC ASIA CONFERENCE ON LANGUAGE, INFORMATION AND COMPUTATION, 2010, : 447 - 454
[9] A maximum entropy approach to Information Extraction from semi-structured and free text
Chien, HL
Ng, HT
[J]. EIGHTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-02)/FOURTEENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-02), PROCEEDINGS, 2002, : 786 - 791
[10] Hybrid approach for text categorization based on machine learning and rules
Villena-Roman, Julio
Collada-Perez, Sonia
Lana-Serrano, Sara
Carlos Gonzalez-Cristobal, Jose
[J]. PROCESAMIENTO DEL LENGUAJE NATURAL, 2011, (46): : 35 - 42

← 1 2 3 4 5 →