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- Nivre, Joakim
(författare)
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Inductive Dependency Parsing of Natural Language Text
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis investigates new methods for syntactic parsing of unrestricted natural language text under requirements of robustness and disambiguation. A parsing system is required to assign to every sentence in a text at least one analysis (robustness) and at most one analysis (disambiguation). The single analysis should be correct as often as possible (accuracy), and the computation should consume as little time and memory as possible (efficiency).The parsing methods proposed are formalized in a general framework of inductive dependency parsing, where dependency parsing is defined as the derivation of labeled dependency graphs, satisfying such constraints as single-headedness, acyclicity, connectedness and projectivity, and where inductive machine learning is used to guide the parser at nondeterministic choice points.The main contribution is a new algorithm for deterministic parsing that derives labeled projective dependency graphs in a single left-to-right pass over the input. The algorithm is proven optimal with respect to robustness, disambiguation and efficiency, meaning that it derives a single dependency graph for every input sentence in time that is linear in the length of the sentence.The parsing algorithm is combined with inductive machine learning using a history-based model where the next parser action is predicted from features of the current parser configuration, features that include the part-of-speech, word form and dependency type of relevant input tokens. It is shown how memory-based learning and classification can be used to solve the learning problem by inducing classifiers from treebank data that predict the next parser action.The memory-based deterministic dependency parser is evaluated using treebank data from Swedish and English. The results show parsing accuracy close to the state of the art while maintaining robustness, disambiguation and good efficiency.
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| 3. |
- Rehm, Georg, et al.
(författare)
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The strategic impact of META-NET on the regional, national and international level
- 2016
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Ingår i: Language resources and evaluation. - : Springer Science and Business Media LLC. - 1574-020X .- 1572-8412 .- 1574-0218. ; 50:2, s. 351-374
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Tidskriftsartikel (refereegranskat)abstract
- This article provides an overview of the dissemination work carried out in META-NET from 2010 until 2015; we describe its impact on the regional, national and international level, mainly with regard to politics and the funding situation for LT topics. The article documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.
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| 4. |
- Verbeke, Mathias, et al.
(författare)
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Lazy and Eager Relational Learning Using Graph-Kernels
- 2014
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Ingår i: Statistical Language and Speech Processing. - Cham : Springer. - 9783319113975 - 9783319113968 ; , s. 171-184
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Konferensbidrag (refereegranskat)abstract
- Machine learning systems can be distinguished along two dimensions. The first is concerned with whether they deal with a feature based (propositional) or a relational representation; the second with the use of eager or lazy learning techniques. The advantage of relational learning is that it can capture structural information. We compare several machine learning techniques along these two dimensions on a binary sentence classification task (hedge cue detection). In particular, we use SVMs for eager learning, and kNN for lazy learning. Furthermore, we employ kLog, a kernel-based statistical relational learning framework as the relational framework. Within this framework we also contribute a novel lazy relational learning system. Our experiments show that relational learners are particularly good at handling long sentences, because of long distance dependencies.
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