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  • Baldwin, Timothy, et al. (författare)
  • Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics
  • 2021
  • Ingår i: Dagstuhl Reports. - Dagstuhl. - 2192-5283. ; 11:7, s. 89-138
  • Tidskriftsartikel (refereegranskat)abstract
    • Computational linguistics builds models that can usefully process and produce language and thatcan increase our understanding of linguistic phenomena. From the computational perspective,language data are particularly challenging notably due to their variable degree of idiosyncrasy(unexpected properties shared by few peer objects), and the pervasiveness of non-compositionalphenomena such as multiword expressions (whose meaning cannot be straightforwardly deducedfrom the meanings of their components, e.g. red tape, by and large, to pay a visit and to pullone’s leg) and constructions (conventional associations of forms and meanings). Additionally, ifmodels and methods are to be consistent and valid across languages, they have to face specificitiesinherent either to particular languages, or to various linguistic traditions.These challenges were addressed by the Dagstuhl Seminar 21351 entitled “Universals ofLinguistic Idiosyncrasy in Multilingual Computational Linguistics”, which took place on 30-31 August 2021. Its main goal was to create synergies between three distinct though partlyoverlapping communities: experts in typology, in cross-lingual morphosyntactic annotation and inmultiword expressions. This report documents the program and the outcomes of the seminar. Wepresent the executive summary of the event, reports from the 3 Working Groups and abstracts ofindividual talks and open problems presented by the participants.
  • Ballesteros, Miguel, et al. (författare)
  • MaltOptimizer : Fast and Effective Parser Optimization
  • 2016
  • Ingår i: Natural Language Engineering. - 1351-3249 .- 1469-8110. ; 22:2, s. 187-213
  • Tidskriftsartikel (refereegranskat)abstract
    • Statistical parsers often require careful parameter tuning and feature selection. This is a nontrivial task for application developers who are not interested in parsing for its own sake, and it can be time-consuming even for experienced researchers. In this paper we present MaltOptimizer, a tool developed to automatically explore parameters and features for MaltParser, a transition-based dependency parsing system that can be used to train parser's given treebank data. MaltParser provides a wide range of parameters for optimization, including nine different parsing algorithms, an expressive feature specification language that can be used to define arbitrarily rich feature models, and two machine learning libraries, each with their own parameters. MaltOptimizer is an interactive system that performs parser optimization in three stages. First, it performs an analysis of the training set in order to select a suitable starting point for optimization. Second, it selects the best parsing algorithm and tunes the parameters of this algorithm. Finally, it performs feature selection and tunes machine learning parameters. Experiments on a wide range of data sets show that MaltOptimizer quickly produces models that consistently outperform default settings and often approach the accuracy achieved through careful manual optimization.
  • Basirat, Ali, 1982-, et al. (författare)
  • Real-valued syntactic word vectors
  • 2020
  • Ingår i: Journal of experimental and theoretical artificial intelligence (Print). - 0952-813X .- 1362-3079. ; 32:4, s. 557-579
  • Tidskriftsartikel (refereegranskat)abstract
    • We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its preceding word. An adaptive transformation function is also introduced that reshapes the data distribution to make it suitable for dimensionality reduction techniques. The final low-dimensional word vectors are formed by the singular vectors of a matrix of transformed data. We show that the resulting word vectors are as good as other sets of word vectors generated with popular word embedding methods.
  • Basirat, Ali, 1982-, et al. (författare)
  • Real-valued Syntactic Word Vectors (RSV) for Greedy Neural Dependency Parsing
  • 2017
  • Konferensbidrag (refereegranskat)abstract
    • We show that a set of real-valued word vectors formed by right singular vectors of a transformed co-occurrence matrix are meaningful for determining different types of dependency relations between words. Our experimental results on the task of dependency parsing confirm the superiority of the word vectors to the other sets of word vectors generated by popular methods of word embedding. We also study the effect of using these vectors on the accuracy of dependency parsing in different languages versus using more complex parsing architectures.
  • Basirat, Ali, Postdoctoral Researcher, 1982-, et al. (författare)
  • Syntactic Nuclei in Dependency Parsing – : A Multilingual Exploration
  • 2021
  • Ingår i: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. - Stroudsburg, PA, USA : Association for Computational Linguistics. - 9781954085022 ; , s. 1376-1387
  • Konferensbidrag (refereegranskat)abstract
    • Standard models for syntactic dependency parsing take words to be the elementary units that enter into dependency relations. In this paper, we investigate whether there are any benefits from enriching these models with the more abstract notion of nucleus proposed by Tesniere. We do this by showing how the concept of nucleus can be defined in the framework of Universal Dependencies and how we can use composition functions to make a transition-based dependency parser aware of this concept. Experiments on 12 languages show that nucleus composition gives small but significant improvements in parsing accuracy. Further analysis reveals that the improvement mainly concerns a small number of dependency relations, including relations of coordination, direct objects, nominal modifiers, and main predicates.
  • Bengoetxea, Kepa, et al. (författare)
  • On WordNet Semantic Classes and Dependency Parsing
  • 2014
  • Ingår i: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). ; , s. 649-655
  • Konferensbidrag (refereegranskat)
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