Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and
            Knowledge-based Methods to Measure Semantic Word Similarity

Ranjbar, Niloofar; Mashhadirajab, Fatemeh; Shamsfard, Mehrnoush; pour, Rayeheh Hosseini; pour, Aryan Vahid

doi:10.18653/v1/s17-2040

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…In the end we received a wide variety of participants: proposing distributional semantic models learnt directly from raw corpora, using syntactic features, exploiting knowledge from lexical resources, and hybrid approaches combining corpus-based and knowledge-based clues. Due to lack of space we cannot describe all the systems in detail, but we recommend the reader to refer to the system description papers for more information about the individual systems: HCCL (He et al, 2017), Citius (Gamallo, 2017), jmp8 (Melka and Bernard, 2017), l2f (Fialho et al, 2017), QLUT (Meng et al, 2017), RUFINO (Jimenez et al, 2017), MERALI (Mensa et al, 2017), Luminoso (Speer and Lowry-Duda, 2017), hhu (Qasem-iZadeh and Kallmeyer, 2017), Mahtab (Ranjbar et al, 2017), SEW (Delli Bovi and Raganato, 2017) and Wild Devs (Rotari et al, 2017), and OoO.…”

Section: Participating Systemsmentioning

confidence: 99%

SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

Camacho-Collados

Pilehvar

Collier

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

118

132

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This paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish. High quality datasets were manually curated for the five languages with high inter-annotator agreements (consistently in the 0.9 ballpark). These were used for semi-automatic construction of ten cross-lingual datasets. 17 teams participated in the task, submitting 24 systems in subtask 1 and 14 systems in subtask 2. Results show that systems that combine statistical knowledge from text corpora, in the form of word embeddings, and external knowledge from lexical resources are best performers in both subtasks. More information can be found on the task website: http://alt.qcri. org/semeval2017/task2/ .

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Section: Participating Systemsmentioning

confidence: 99%

SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

Camacho-Collados

Pilehvar

Collier

et al. 2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

118

132

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“…Notably, the idea of creating semantic similarity ensembles has sparked considerable interest [59,60]. Over the years, many methods based on different paradigms have been proposed [61,62,63,64,65]. These approaches have contributed to the ongoing exploration and development of semantic similarity measurement.…”

Section: Hybrid Methodsmentioning

confidence: 99%

30 Years of Research on Semantic Similarity Measurement

Sharma¹

2023

Preprint

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Over the past three decades, researchers have been interested in measuring the semantic similarity between words or concepts in natural language processing. This interest has arisen due to the need for more effective techniques for information retrieval, text classification, and other tasks. The concept of semantic similarity refers to the degree of relatedness between two words or concepts based on their meaning or context. Semantic similarity measurement involves various methods such as knowledge-based, corpus-based, and hybrid methods. In this paper, we review the evolution of semantic similarity measurement over the past 30 years, including the different approaches used and their advantages and limitations. We also discuss the challenges in developing accurate semantic similarity metrics and highlight the emerging trends in this field, such as the use of deep learning techniques to improve the accuracy of semantic similarity measurement. Finally, we conclude by suggesting some directions for future research in this field.

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“…This method can obtain the semantic similarity information between words by directly measuring the statistical information of the words in the corpus [6]. The traditional method mainly uses co-occurrence [7] statistical features of the words in context [8] to measure the semantic similarity between words.…”

Section: Methods Based On Text Statisticsmentioning

confidence: 99%

Measuring Semantic Similarity between Words Based on Multiple Relational Information

Duan

Wang

et al. 2020

IEICE Trans. Inf. & Syst.

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The similarity of words extracted from the rich text relation network is the main way to calculate the semantic similarity. Complex relational information and text content in Wikipedia website, Community Question Answering and social network, provide abundant corpus for semantic similarity calculation. However, most typical research only focused on single relationship. In this paper, we propose a semantic similarity calculation model which integrates multiple relational information, and map multiple relationship to the same semantic space through learning representing matrix and semantic matrix to improve the accuracy of semantic similarity calculation. In experiments, we confirm that the semantic calculation method which integrates many kinds of relationships can improve the accuracy of semantic calculation, compared with other semantic calculation methods.

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Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity

Cited by 7 publications

References 13 publications

SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity

30 Years of Research on Semantic Similarity Measurement

Measuring Semantic Similarity between Words Based on Multiple Relational Information

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