Kernel methods for relation extraction

Zelenko, Dmitry; Aone, Chinatsu; Richardella, Anthony

doi:10.3115/1118693.1118703

Cited by 517 publications

(681 citation statements)

References 20 publications

Supporting

Mentioning

674

Contrasting

Unclassified

Order By: Relevance

“…into a vector space F , called feature space, and searching for linear relation in the feature space. This embedding is defined implicitly, by specifying an inner product for the feature space via a symmetric and positive semidefinite kernel function: K(x, y) = Φ(x), Φ(y) , where Φ(x) and Φ(y) are the embeddings of data items, x and y [8].…”

Section: Kernel Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Tree Kernel-Based Method for Protein-Protein Interaction Mining from Biomedical Literature

Eom

Kim

et al. 2006

Knowledge Discovery in Life Science Literature

View full text Add to dashboard Cite

Abstract.As genomic research advances, the knowledge discovery from a large collection of scientific papers becomes more important for efficient biological and biomedical research. Even though current databases continue to update new protein-protein interactions, valuable information still remains in biomedical literature. Thus data mining techniques are required to extract the information. In this paper, we present a tree kernel-based method to mine protein-protein interactions from biomedical literature. The tree kernel is designed to consider grammatical structures for given sentences. A support vector machine classifier is combined with the tree kernel and trained on predefined interaction corpus and set of interaction patterns. Experimental results show that the proposed method gives promising results by utilizing the structure patterns.

show abstract

Section: Kernel Methodsmentioning

confidence: 99%

“…, a function defined as a dot product of the corresponding feature vectors is necessarily a kernel function [8].…”

Section: Kernel Methodsmentioning

confidence: 99%

A Tree Kernel-Based Method for Protein-Protein Interaction Mining from Biomedical Literature

Eom

Kim

et al. 2006

Knowledge Discovery in Life Science Literature

View full text Add to dashboard Cite

show abstract

“…The question of whether to use a shallow or deep level of text processing is determined by the design of experiment(s) and algorithm of machine learning. If shallow processing is sufficient, then there is no need to use deep processing (Zelenko et al, 2003).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Subsequently, the generated patterns can be applied to new contexts with unknown relations and derive meaningful relations. Commonly used machine learning models include the support vector machine (SVM) (Bunescu & Mooney, 2007;Culotta & Sorensen, 2004;Zelenko et al, 2003), clustering (Agichtein & Gravano, 2000, undirected graphical models (Culotta et al, 2006), and decision tree (Nahm & Mooney, 2000).…”

Section: Literature Reviewmentioning

confidence: 99%

Metadata for semantic and social applications<br />DC- 2008 Berlin

Greenberg¹,

Klas²

2008

View full text Add to dashboard Cite

“…They proposed a general language modeling method for quantifying the difficulty of information extraction by predicting performance of named entity recognition such as location, organization, person name and miscellaneous named entities, and relation extraction such as birth dates, death dates and invention name. Zelenko et al [24] proposed kernel methods with support vector machines (SVMs) for extracting relation among personaffiliation and organization-location. Culotta et al [21] experimented on the Automatic Content Extraction (ACE) corpus using different features such as WordNet, parts of speech and entity types.…”

Section: Introductionmentioning

confidence: 99%

Discovery of Predicate-Oriented Relations among Named Entities Extracted from Thai Texts

Tongtep

Theeramunkong

2012

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Nattapong TONGTEP †a) , Student Member and Thanaruk THEERAMUNKONG †b) , Member SUMMARYExtracting named entities (NEs) and their relations is more difficult in Thai than in other languages due to several Thai specific characteristics, including no explicit boundaries for words, phrases and sentences; few case markers and modifier clues; high ambiguity in compound words and serial verbs; and flexible word orders. Unlike most previous works which focused on NE relations of specific actions, such as work for, live in, located in, and kill, this paper proposes more general types of NE relations, called predicate-oriented relation (PoR), where an extracted action part (verb) is used as a core component to associate related named entities extracted from Thai Texts. Lacking a practical parser for the Thai language, we present three types of surface features, i.e. punctuation marks (such as token spaces), entity types and the number of entities and then apply five alternative commonly used learning schemes to investigate their performance on predicate-oriented relation extraction. The experimental results show that our approach achieves the F-measure of 97.76%, 99.19%, 95.00% and 93.50% on four different types of predicate-oriented relation (action-location, location-action, action-person and person-action) in crime-related news documents using a data set of 1,736 entity pairs. The effects of NE extraction techniques, feature sets and class unbalance on the performance of relation extraction are explored. key words: relation extraction, named entity, surface feature, information extraction IntroductionRecently several information extraction (IE) approaches have been proposed to transform an unstructured text into knowledge base, such as those in [1 [25] presented a so-called CORDER system to find relations among entities in an organization's documents on a social network. The mined knowledge was in the form of who works with whom, on which projects and with which customers, using strength measured for each co-occurring NE based on its co-occurrences and distances with the target. The CORDER comprised the steps of data selection, named entity recognition and ranking by relation strengths.As an integrated community project, tasks of entity and relation extraction from English, Chinese and Arabic texts were conducted in the Automatic Content Extraction (ACE) program * , including three sets of annotation tasks; Entity Detection and Tracking (EDT), Relation Detection and Characterization (RDC), and Event Detection and Characterization (EDC) [26]. Three main EDT tasks were the detection of entities mentioned in a document, the tracking of entities * http://projects.ldc.upenn.edu/ace/

show abstract

Kernel methods for relation extraction

Cited by 517 publications

References 20 publications

A Tree Kernel-Based Method for Protein-Protein Interaction Mining from Biomedical Literature

A Tree Kernel-Based Method for Protein-Protein Interaction Mining from Biomedical Literature

Metadata for semantic and social applications<br />DC- 2008 Berlin

Discovery of Predicate-Oriented Relations among Named Entities Extracted from Thai Texts

Contact Info

Product

Resources

About