Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.

/pdf/wikipedia-based-semantic-interpretation-for-natural-language-4ikazfyj50.pdf

Wikipedia-based semantic interpretation for natural language processing

Semantic Web in data mining and knowledge discovery

Current text classification systems typically use term stems for representing document content. Semantic Web technologies allow the usage of features on a higher semantic level than single words for text classification purposes. In this paper we propose such an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting, a successful machine learning technique is used for classification. Comparative experimental evaluations in three different settings support our approach through consistent improvement of the results. An analysis of the results shows that this improvement is due to two separate effects.

/pdf/boosting-for-text-classification-with-semantic-features-1esgkxwys0.pdf

Boosting for text classification with semantic features

Mining the Web of Linked Data with RapidMiner

Most existing methods for text categorization employ induction algorithms that use the words appearing in the training documents as features. While they perform well in many categorization tasks, these methods are inherently limited when faced with more complicated tasks where external knowledge is essential. Recently, there have been efforts to augment these basic features with external knowledge, including semi-supervised learning and transfer learning. In this work, we present a new framework for automatic acquisition of world knowledge and methods for incorporating it into the text categorization process. Our approach enhances machine learning algorithms with features generated from domain-specific and common-sense knowledge. This knowledge is represented by ontologies that contain hundreds of thousands of concepts, further enriched through controlled Web crawling. Prior to text categorization, a feature generator analyzes the documents and maps them onto appropriate ontology concepts that augment the bag of words used in simple supervised learning. Feature generation is accomplished through contextual analysis of document text, thus implicitly performing word sense disambiguation. Coupled with the ability to generalize concepts using the ontology, this approach addresses two significant problems in natural language processing---synonymy and polysemy. Categorizing documents with the aid of knowledge-based features leverages information that cannot be deduced from the training documents alone. We applied our methodology using the Open Directory Project, the largest existing Web directory built by over 70,000 human editors. Experimental results over a range of data sets confirm improved performance compared to the bag of words document representation.

/pdf/harnessing-the-expertise-of-70-000-human-editors-knowledge-2db6ukl191.pdf

Harnessing the Expertise of 70,000 Human Editors: Knowledge-Based Feature Generation for Text Categorization

We introduced a novel method employing a hierarchical domain ontology structure to extract features representing documents in our previous publication (Wang 2002). All raw words in the training documents are mapped to concepts in a concept hierarchy derived from the domain ontology. Based on these concepts, a concept hierarchy is established for the training document space, using is-a relationships defined in the domain ontology. An optimum concept set may be obtained by searching the concept hierarchy with an appropriate heuristic function. This may be used as the feature space to represent the training dataset. The proposed method aims to solve some drawbacks suffered by text classification algorithms and feature selection algorithms. In this paper, we conducted a series of experiments to compare our approach with other comparable feature-selection and feature-extraction methods. The results indicated that our approach has advantages in many aspects.

A comparative study for domain ontology guided feature extraction

Using the evolutionary modeling of system of ordinary differential equations (ODEs) as the test problem, this paper primarily investigates the influences of some important parallel control parameters within parallel genetic programming (GP), including the degree of connectivity between demes, the migration rate, the migration generation interval, and the migration policy, on the performance of the parallel evolutionary modeling algorithm (PEMA), which is measured from two perspectives: the solution quality and the parallel speedup. We compare the results with previous theoretical and experimental work in parallel genetic algorithms (GAs), and try to give some plausible analysis and explanations. The results may help to offer some useful design guidelines for researchers using parallel GP.

R. I. Bob Mckay

Papers

A comparative study for domain ontology guided feature extraction

An experimental study of some control parameters in parallel genetic programming