Developing a Concept Extraction Technique with

1. Developing a Concept Extraction Technique with Ensemble Pathway Prat Tanapaisankit (NJIT), Min Song (NJIT), and Edward A. Fox (Virginia Tech) • Training Data • 1748 tuples • 6000 sentences from the Ensemble Pathway and the web as positive examples. • 4000 sentences collected from the web, which are used as negative examples. • Contributions • We apply Conditional Random Fields (CRFs) to concept extraction. • We propose an automatic procedure to build the training data. • We use CEED to apply concept extraction to an educational collection, extending how concept extraction has been applied to digital libraries. • We provide RESTful web services for concept extraction. • AcknowledgmentsPartial support for this research was provided by the National Science Foundation under grants DUE- 0937629 and 0840719, and by the New Jersey Institute of Technology. • Ensemble: • www.computingportal.org • Abstract • In this poster, we describe our Concept Extraction technique for Educational Digital libraries (CEED) which applies Conditional Random Fields (CRFs) to extract concepts from the Ensemble Pathway collection. • Ensemble • NSF NSDL Pathways project working to establish a national, distributed digital library for computing education. • Support the multidisciplinary aspects of computing education communities. • Encourages contribution, use, reuse, review, and evaluation of educational materials of all kinds. • Serves as a computing portal for a collection of information that is distributed in location and in ownership. • 9 content providers and 9 sub-collections. • 9901 articles in its collection at time of study • Harvesting Metadata • We retrieved metadata records from the Ensemble OAI provider at http://figo.cc.vt.edu:8080/fedora3/oai. • We used jOAI, which is a Java-based open source Open Archives Initiative (OAI) data provider and harvester tool developed by Digital Learning Sciences (DLS). • The repository site is OAI-compliant according to the OAI Implementation Guidelines, so other harvesting tools that conform to the OAI-PMH protocol can be employed as well. • Indexing Metadata • We indexed the Ensemble Pathway collections with our tool, QICs. After indexing we have found that the collection contains a good number of metadata records although the majority of them do not provide an abstract (description). The Ensemble Pathway served a total of 9901 educational resources at the time of the study. An example of input and output Overall Data Flow of CEED Positive Example The computer uses a modem to access the Web. Negative Example Test Data (Sentence) Trained Model Concept Tuple Index List of Tags CEED CEED (Concept Extraction technique for Education Digital library) The <general>computer</general> uses a <hardware>modem </hardware> to access the <computer-communication-networks>Web<computer-communication-networks>. Sentence With Concept Tags Training Data • System Description • CEED is a CRFs-based concept extraction technique. Its core engine is a CRFs-based tagger which takes a sentence as an input and returns the sentence along with a concept tag for important terms. • The system has 28 tags used for different important terms. Before performing the extraction task, CEED needs to be properly trained to build a model. • Concept Tuple • The format of a tuple is denoted as follows: • (Computing concept, description, class) • For example • (Algorithm, Model of computation and algorithm, Theory of Computation) • Computing concepts are taken from “The Free On-line Dictionary of Computing” (http://foldoc.org/). • Classes based on the ACM Classification are assigned to each concept manually. • Description provides more information of a class.