{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:53:32Z","timestamp":1760709212177},"reference-count":33,"publisher":"MIT Press - Journals","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Linguistics"],"published-print":{"date-parts":[[2017,4]]},"abstract":"<jats:p> Although there has been much work in recent years on data-driven natural language generation, little attention has been paid to the fine-grained interactions that arise during microplanning between aggregation, surface realization, and sentence segmentation. In this article, we propose a hybrid symbolic\/statistical approach to jointly model the constraints regulating these interactions. Our approach integrates a small handwritten grammar, a statistical hypertagger, and a surface realization algorithm. It is applied to the verbalization of knowledge base queries and tested on 13 knowledge bases to demonstrate domain independence. We evaluate our approach in several ways. A quantitative analysis shows that the hybrid approach outperforms a purely symbolic approach in terms of both speed and coverage. Results from a human study indicate that users find the output of this hybrid statistic\/symbolic system more fluent than both a template-based and a purely symbolic grammar-based approach. Finally, we illustrate by means of examples that our approach can account for various factors impacting aggregation, sentence segmentation, and surface realization. <\/jats:p>","DOI":"10.1162\/coli_a_00273","type":"journal-article","created":{"date-parts":[[2016,12,14]],"date-time":"2016-12-14T19:29:07Z","timestamp":1481743747000},"page":"1-30","source":"Crossref","is-referenced-by-count":7,"title":["A Statistical, Grammar-Based Approach to Microplanning"],"prefix":"10.1162","volume":"43","author":[{"given":"Claire","family":"Gardent","sequence":"first","affiliation":[{"name":"CNRS and Universit\u00e9 de Lorraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Perez-Beltrachini","sequence":"additional","affiliation":[{"name":"CNRS and Universit\u00e9 de Lorraine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"bib1","unstructured":"Angeli, Gabor, Percy Liang, and Dan Klein. 2010. A simple domain-independent probabilistic approach to generation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 502\u2013512."},{"key":"bib3","unstructured":"Banarescu, Laura, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013. Abstract meaning representation for sembanking. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pages 178\u2013186, Sofia."},{"key":"bib4","unstructured":"Bangalore, Srinivas and Aravind K. Joshi. 1999. Supertagging: An approach to almost parsing. Computational Linguistics, 25(2):237\u2013265."},{"key":"bib5","doi-asserted-by":"publisher","DOI":"10.3115\/1626481.1626503"},{"key":"bib6","unstructured":"Clark, Stephen and James R. Curran. 2004. The importance of supertagging for wide-coverage CCG parsing. 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