A semantic method to information extraction for decision support systems
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ghosh, Ranadhir
- Date: 2006
- Type: Text , Conference proceedings
- Full Text: false
- Description: In this paper, we describe a novel schema for a more semantic text mining process which results in more comprehensive decision making activity by decision support systems via providing more effective and accurate textual information. The utility of two semantic lexical resources; Frame Net and Word Net, in extracting required text snippets from unstructured free texts yields a better and more accurate information extraction process to deliver more precise information either to a DSS or to a decision maker. We explain how the usage of these lexical resources could elevate a focused text mining process which could be applied to an information provider system in a decision support paradigm. The preliminary results obtained after a starter experiment show that the hybrid information extraction schema performs well on some semantic failure situations.
- Description: 2003010644
Learning parse-free event-based features for textual entailment recognition
- Authors: Ofoghi, Bahadorreza , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 Vol. 6464 LNAI, p. 184-193
- Full Text: false
- Reviewed:
- Description: We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these features can improve the effectiveness of the identification of entailment and no-entailment relationships. © 2010 Springer-Verlag.
The impact of frame semantic annotation levels, frame-alignment techniques, and fusion methods on factoid answer processing
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Liping, Ma
- Date: 2009
- Type: Text , Journal article
- Relation: Journal of the American Society for Information Science and Technology Vol. 60, no. 2 (2009), p. 247-263
- Full Text: false
- Reviewed:
- Description: The impact of frame semantic enrichment of texts on the task of factoid question answering (QA) is studied in this paper. In particular, we consider different techniques for answer processing with frame semantics: the level of semantic class identification and role assignment to texts, and the fusion of frame semantic-based answerprocessing approaches with other methods used in the Text REtrieval Conference (TREC). The impact of each of these aspects on the overall performance of a QA system is analyzed in this paper. The TREC 2004 and TREC 2006 factoid question sets were used for the experiments. These demonstrate that the exploitation of encapsulated frame semantics in FrameNet in a shallow semantic parsing process can enhance answer-processing performance in factoid QA systems. This improvement is dependent on the level of semantic annotation, the frame semantic alignment method, and the method of fusing frame semantic-based answer-processing models with other existing models. A more comprehensively annotated environment with all different part-of-speech target predicates provides a higher chance of correct factoid answer retrieval where semantic alignment is based on both semantic classes and a relaxed set of semantic roles for answer span identification. Our experiments on fusion techniques of frame semantic-based and entity-based answer-processing models show that merging answer lists with respect to their scores and redundancy by exploiting a fusion function leads to a more effective overall factoid QA system compared to the use of individual models.
Two-step comprehensive open domain text annotation with frame semantics
- Authors: Ofoghi, Bahadorreza , Yearwood, John , Ma, Liping
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at Australasian Language Technology Workshop 2007, Melbourne Zoo, Melbourne, Victoria : 10th-11th December 2007 p. 83-91
- Full Text:
- Description: With shallow semantic parsing tasks receiving more attention in many natural language applications, there is a need for labelled corpora for learning the specific tags under consideration. In this paper, we discuss a two-step semantic class and semantic role assignment based on the FrameNet elements over a subset of the AQUAINT collection with a reasonable coverage over the semantic frames in FrameNet. The quality of the annotation task is examined through inter-annotator agreement. The methodology described in this work for measuring inter-annotator agreement can be adapted for similar tasks. Some central aspects of the task are also detailed in this paper.
- Description: 2003005522