Conference
OntoSeg: a Novel Approach to Text Segmentation using Ontological Similarity ; The 5th ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, ICDM SENTIRE. Held in conjunction with the IEEE International Conference on Data Mining
| العنوان: | OntoSeg: a Novel Approach to Text Segmentation using Ontological Similarity ; The 5th ICDM Workshop on Sentiment Elicitation from Natural Text for Information Retrieval and Extraction, ICDM SENTIRE. Held in conjunction with the IEEE International Conference on Data Mining |
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| المؤلفون: | Bayomi, Mostafa, Levacher, Killian, Ghorab, M.Rami, Lawless, S?amus |
| سنة النشر: | 2017 |
| المجموعة: | The University of Dublin, Trinity College: TARA (Trinity's Access to Research Archive) |
| مصطلحات موضوعية: | Text Segmentation, Ontological similarity, Lexical Cohesion, Vector Space Model, Intelligent Content & Communications |
| الوصف: | Text segmentation (TS) aims at dividing long text into coherent segments which reflect the subtopic structure of the text. It is beneficial to many natural language processing tasks, such as Information Retrieval (IR) and document summarisation. Current approaches to text segmentation are similar in that they all use word-frequency metrics to measure the similarity between two regions of text, so that a document is segmented based on the lexical cohesion between its words. Various NLP tasks are now moving towards the semantic web and ontologies, such as ontology-based IR systems, to capture the conceptualizations associated with user needs and contents. Text segmentation based on lexical cohesion between words is hence not sufficient anymore for such tasks. This paper proposes OntoSeg, a novel approach to text segmentation based on the ontological similarity between text blocks. The proposed method uses ontological similarity to explore conceptual relations between text segments and a Hierarchical Agglomerative Clustering (HAC) algorithm to represent the text as a tree-like hierarchy that is conceptually structured. The rich structure of the created tree further allows the segmentation of text in a linear fashion at various levels of granularity. The proposed method was evaluated on a well known dataset, and the results show that using ontological similarity in text segmentation is very promising. Also we enhance the proposed method by combining ontological similarity with lexical similarity and the results show an enhancement of the segmentation quality. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| تدمد: | 2375-9259 |
| Relation: | Mostafa Bayomi, Killian Levacher, M.Rami Ghorab, S?amus Lawless, 'OntoSeg: a Novel Approach to Text Segmentation using Ontological Similarity', 2015; Y; http://hdl.handle.net/2262/77449; http://people.tcd.ie/bayomim; 111861 |
| الاتاحة: | http://hdl.handle.net/2262/77449 http://people.tcd.ie/bayomim |
| Rights: | Y ; openAccess |
| رقم الانضمام: | edsbas.5034917A |
| قاعدة البيانات: | BASE |
| تدمد: | 23759259 |
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