Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity cha... more This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method using BabelNet relationships. In the English subtask challenge, we submitted three systems (runs) to assess the proposed method. In Run1, we used our proposed method coupled with a string kernel mapping function to calculate the textual similarity. In Run2, we used the method with a tree kernel function. In Run3, we averaged Run1 with a previously proposed surface-based approach as a kind of integration. The three runs are ranked 41 st , 57 th , and 20 th of 73 systems, with mean correlation 0.702, 0.597, and 0.759 respectively. For the interpretable task, we submitted a modified version of Run1 achieving mean F1 0.846, 0.461, 0.722, and 0.44 for alignment, type, score, and score with type respectively.
Semantic Textual Similarity (STS) is the task of assessing the degree of similarity between two t... more Semantic Textual Similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. Several approaches have been proposed in the literature to determine the semantic similarity between texts. The most promising work recently presented in the literature was supervised approaches. Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations. Word alignment has been widely used in stateof-the-art approaches. From this point, this paper has three contributions. First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet. Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL). Third, some limitations of state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach. UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance. The experimental results proved that the participation of the proposed aligner in STS is effective. Over all the evaluation data sets used, the proposed UESTS outperforms the state-of-the-art unsupervised approaches, which is a promising result.
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Simila... more This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, in which a proposed alignment similarity method coupled with new usage of a semantic network (BabelNet) is used. Additionally, a previously proposed integration between sense-based and surface-based semantic textual similarity approach is applied together with our proposed approach. For all the tracks in Task1, Run1 is a string kernel with alignments metric and Run2 is a sense-based alignment similarity method. The first run is ranked 10th, and the second is ranked 12th in the primary track, with correlation 0.619 and 0.617 respectively.
This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity cha... more This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method using BabelNet relationships. In the English subtask challenge, we submitted three systems (runs) to assess the proposed method. In Run1, we used our proposed method coupled with a string kernel mapping function to calculate the textual similarity. In Run2, we used the method with a tree kernel function. In Run3, we averaged Run1 with a previously proposed surface-based approach as a kind of integration. The three runs are ranked 41st, 57th, and 20th of 73 systems, with mean correlation 0.702, 0.597, and 0.759 respectively. For the interpretable task, we submitted a modified version of Run1 achieving mean F1 0.846, 0.461, 0.722, and 0.44 for alignment, type, score, and score with type respectively.
International Journal of Computer Science and Information Technology, 2010
Email Retrieval task has recently taken much attention to help the user retrieve the email(s) rel... more Email Retrieval task has recently taken much attention to help the user retrieve the email(s) related to the submitted query. Up to our knowledge, existing email retrieval ranking approaches sort the retrieved emails based on some heuristic rules, which are either search clues or some predefined user criteria rooted in email fields. Unfortunately, the user usually does not know the effective rule that acquires best ranking related to his query. This paper presents a new email retrieval ranking approach to tackle this problem. It ranks the retrieved emails based on a scoring function that depends on crucial email fields, namely subject, content, and sender. The paper also proposes an architecture to allow every user in a network/group of users to be able, if permissible, to know the most important network senders who are interested in his submitted query words. The experimental evaluation on Enron corpus prove that our approach outperforms known email retrieval ranking approaches.
Email Retrieval task has recently taken much attention to help the user retrieve the email(s) rel... more Email Retrieval task has recently taken much attention to help the user retrieve the email(s) related to the submitted query. Up to our knowledge, existing email retrieval ranking approaches sort the retrieved emails based on some heuristic rules, which are either search clues or some predefined user criteria rooted in email fields. Unfortunately, the user usually does not know the effective rule that acquires best ranking related to his query. This paper presents a new email retrieval ranking approach to tackle this problem. It ranks the retrieved emails based on a scoring function that depends on crucial email fields, namely subject, content, and sender. The paper also proposes an architecture to allow every user in a network/group of users to be able, if permissible, to know the most important network senders who are interested in his submitted query words. The experimental evaluation on Enron corpus prove that our approach outperforms known email retrieval ranking approaches.
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity cha... more This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method using BabelNet relationships. In the English subtask challenge, we submitted three systems (runs) to assess the proposed method. In Run1, we used our proposed method coupled with a string kernel mapping function to calculate the textual similarity. In Run2, we used the method with a tree kernel function. In Run3, we averaged Run1 with a previously proposed surface-based approach as a kind of integration. The three runs are ranked 41 st , 57 th , and 20 th of 73 systems, with mean correlation 0.702, 0.597, and 0.759 respectively. For the interpretable task, we submitted a modified version of Run1 achieving mean F1 0.846, 0.461, 0.722, and 0.44 for alignment, type, score, and score with type respectively.
Semantic Textual Similarity (STS) is the task of assessing the degree of similarity between two t... more Semantic Textual Similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. Several approaches have been proposed in the literature to determine the semantic similarity between texts. The most promising work recently presented in the literature was supervised approaches. Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations. Word alignment has been widely used in stateof-the-art approaches. From this point, this paper has three contributions. First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet. Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL). Third, some limitations of state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach. UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance. The experimental results proved that the participation of the proposed aligner in STS is effective. Over all the evaluation data sets used, the proposed UESTS outperforms the state-of-the-art unsupervised approaches, which is a promising result.
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Simila... more This paper describes FCICU team systems that participated in SemEval-2017 Semantic Textual Similarity task (Task1) for monolingual and cross-lingual sentence pairs. A sense-based language independent textual similarity approach is presented, in which a proposed alignment similarity method coupled with new usage of a semantic network (BabelNet) is used. Additionally, a previously proposed integration between sense-based and surface-based semantic textual similarity approach is applied together with our proposed approach. For all the tracks in Task1, Run1 is a string kernel with alignments metric and Run2 is a sense-based alignment similarity method. The first run is ranked 10th, and the second is ranked 12th in the primary track, with correlation 0.619 and 0.617 respectively.
This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity cha... more This paper describes FCICU team participation in SemEval 2015 for Semantic Textual Similarity challenge. Our main contribution is to propose a word-sense similarity method using BabelNet relationships. In the English subtask challenge, we submitted three systems (runs) to assess the proposed method. In Run1, we used our proposed method coupled with a string kernel mapping function to calculate the textual similarity. In Run2, we used the method with a tree kernel function. In Run3, we averaged Run1 with a previously proposed surface-based approach as a kind of integration. The three runs are ranked 41st, 57th, and 20th of 73 systems, with mean correlation 0.702, 0.597, and 0.759 respectively. For the interpretable task, we submitted a modified version of Run1 achieving mean F1 0.846, 0.461, 0.722, and 0.44 for alignment, type, score, and score with type respectively.
International Journal of Computer Science and Information Technology, 2010
Email Retrieval task has recently taken much attention to help the user retrieve the email(s) rel... more Email Retrieval task has recently taken much attention to help the user retrieve the email(s) related to the submitted query. Up to our knowledge, existing email retrieval ranking approaches sort the retrieved emails based on some heuristic rules, which are either search clues or some predefined user criteria rooted in email fields. Unfortunately, the user usually does not know the effective rule that acquires best ranking related to his query. This paper presents a new email retrieval ranking approach to tackle this problem. It ranks the retrieved emails based on a scoring function that depends on crucial email fields, namely subject, content, and sender. The paper also proposes an architecture to allow every user in a network/group of users to be able, if permissible, to know the most important network senders who are interested in his submitted query words. The experimental evaluation on Enron corpus prove that our approach outperforms known email retrieval ranking approaches.
Email Retrieval task has recently taken much attention to help the user retrieve the email(s) rel... more Email Retrieval task has recently taken much attention to help the user retrieve the email(s) related to the submitted query. Up to our knowledge, existing email retrieval ranking approaches sort the retrieved emails based on some heuristic rules, which are either search clues or some predefined user criteria rooted in email fields. Unfortunately, the user usually does not know the effective rule that acquires best ranking related to his query. This paper presents a new email retrieval ranking approach to tackle this problem. It ranks the retrieved emails based on a scoring function that depends on crucial email fields, namely subject, content, and sender. The paper also proposes an architecture to allow every user in a network/group of users to be able, if permissible, to know the most important network senders who are interested in his submitted query words. The experimental evaluation on Enron corpus prove that our approach outperforms known email retrieval ranking approaches.
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Papers by Basma Hassan