Abstract
Concepts are used to solve the term-mismatch problem. However, we need an effective similarity measure between concepts. Word embedding presents a promising solution. We present in this study three approaches to build concepts vectors based on words vectors. We use a vector-based measure to estimate inter-concepts similarity. Our experiments show promising results. Furthermore, words and concepts become comparable. This could be used to improve conceptual indexing process.
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Notes
- 1.
Concepts have many definitions [1]. A concept here refers to a category ID that encompasses synonymous words and phrases, e.g. UMLS concepts, WordNet synsets.
- 2.
- 3.
- 4.
www.ncbi.nlm.nih.gov/pmc/, PubMed collection contains: 1177879 vocabularies.
References
Abdulahhad, K.: Information Retrieval (IR) modeling by logic and lattice. Application to Conceptual IR. Theses. Université de Grenoble, May 2014
Choi, Y., Chiu, C.Y.I., Sontag, D.: Learning low-dimensional representations of medical concepts. In: AMIA Summits on Translational Science Proceedings 2016, p. 41 (2016)
Clinchant, S., Perronnin, F.: Aggregating continuous word embeddings for information retrieval. In: Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality, pp. 100–109 (2013)
Crestani, F.: Exploiting the similarity of non-matching terms at retrievaltime. Inf. Retr. 2, 27–47 (2000)
De Vine, L., Zuccon, G., Koopman, B., Sitbon, L., Bruza, P.: Medical semantic similarity with a neural language model. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, pp. 1819–1822 (2014)
Diaz, F., Mitra, B., Craswell, N.: Query expansion with locally-trained word embeddings. CoRR abs/1605.07891 (2016). http://arxiv.org/abs/1605.07891
Guo, J., Fan, Y., Ai, Q., Croft, W.B.: Semantic matching by non-linear word transportation for information retrieval. In: the 25th ACM International on Conference on Information and Knowledge Management, CIKM 2016, pp. 701–710 (2016)
Kenter, T., de Rijke, M.: Short text similarity with word embeddings. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 1411–1420 (2015)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on International Conference on Machine Learning, ICML 2014, vol. 32, pp. II-1188–II-1196 (2014). JMLR.org
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: 26th International Conference on Neural Information Processing Systems, NIPS 2013, pp. 3111–3119 (2013)
Pedersen, T., Pakhomov, S.V.S., Patwardhan, S., Chute, C.G.: Measures of semantic similarity and relatedness in the biomedical domain. J. Biomed. Inform. 40(3), 288–299 (2007)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. EMNLP 14, 1532–1543 (2014)
Zamani, H., Croft, W.B.: Estimating embedding vectors for queries. In: Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, ICTIR 2016, pp. 123–132 (2016)
Zuccon, G., Koopman, B., Bruza, P., Azzopardi, L.: Integrating and evaluating neural word embeddings in information retrieval. In: Proceedings of the 20th Australasian Document Computing Symposium, ADCS 2015, pp. 12:1–12:8 (2015)
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Abdulahhad, K. (2018). Concept Embedding for Information Retrieval. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_45
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