45,791 research outputs found
Automatic acquisition of LFG resources for German - as good as it gets
We present data-driven methods for the acquisition of LFG resources from two German treebanks. We discuss problems specific to semi-free word order languages as well as problems arising fromthe data structures determined
by the design of the different treebanks. We compare two ways of encoding semi-free word order, as done in the two German treebanks, and argue that the design of the TiGer treebank is more adequate for the acquisition of LFG
resources. Furthermore, we describe an architecture for LFG grammar acquisition for German, based on the two German treebanks, and compare our results with a hand-crafted German LFG grammar
Treebank-based acquisition of LFG parsing resources for French
Motivated by the expense in time and other resources to produce hand-crafted grammars, there has been increased interest in automatically obtained wide-coverage grammars from treebanks for natural language processing. In particular, recent years have seen the growth in interest in automatically obtained deep resources that can represent information absent from simple CFG-type structured treebanks
and which are considered to produce more language-neutral linguistic representations, such as dependency syntactic trees. As is often the case in early pioneering work on natural language processing, English has provided the focus of first efforts towards acquiring deep-grammar resources, followed by successful treatments of, for example, German, Japanese, Chinese and Spanish. However, no comparable large-scale automatically acquired deep-grammar resources have been obtained for French to date. The goal of this paper is to present the application of treebank-based language acquisition to the case of French. We show that with modest changes to the established parsing architectures, encouraging results can be obtained for French, with a best dependency structure f-score of 86.73%
A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging
In this paper, we propose a new approach to construct a system of
transformation rules for the Part-of-Speech (POS) tagging task. Our approach is
based on an incremental knowledge acquisition method where rules are stored in
an exception structure and new rules are only added to correct the errors of
existing rules; thus allowing systematic control of the interaction between the
rules. Experimental results on 13 languages show that our approach is fast in
terms of training time and tagging speed. Furthermore, our approach obtains
very competitive accuracy in comparison to state-of-the-art POS and
morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the
European Journal on Artificial Intelligence. Version 3: Resubmitted after
major revisions. Version 4: Resubmitted after minor revisions. Version 5: to
appear in AI Communications (accepted for publication on 3/12/2015
MBT: A Memory-Based Part of Speech Tagger-Generator
We introduce a memory-based approach to part of speech tagging. Memory-based
learning is a form of supervised learning based on similarity-based reasoning.
The part of speech tag of a word in a particular context is extrapolated from
the most similar cases held in memory. Supervised learning approaches are
useful when a tagged corpus is available as an example of the desired output of
the tagger. Based on such a corpus, the tagger-generator automatically builds a
tagger which is able to tag new text the same way, diminishing development time
for the construction of a tagger considerably. Memory-based tagging shares this
advantage with other statistical or machine learning approaches. Additional
advantages specific to a memory-based approach include (i) the relatively small
tagged corpus size sufficient for training, (ii) incremental learning, (iii)
explanation capabilities, (iv) flexible integration of information in case
representations, (v) its non-parametric nature, (vi) reasonably good results on
unknown words without morphological analysis, and (vii) fast learning and
tagging. In this paper we show that a large-scale application of the
memory-based approach is feasible: we obtain a tagging accuracy that is on a
par with that of known statistical approaches, and with attractive space and
time complexity properties when using {\em IGTree}, a tree-based formalism for
indexing and searching huge case bases.} The use of IGTree has as additional
advantage that optimal context size for disambiguation is dynamically computed.Comment: 14 pages, 2 Postscript figure
Minimally-Supervised Morphological Segmentation using Adaptor Grammars
This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.12 page(s
Gender assignment and gender agreement in advanced French interlanguage: a cross-sectional study
An analysis of 519 gender errors (out of 9,378 modifiers) in the advanced French interlanguage of 27 Dutch L1 speakers confirms earlier findings that gender assignment and/or agreement remain problematic for learners at all levels. A hypothesis derived from Pienemann's Processability Theory (1998a) that accuracy rates would be higher for gender agreement in structures involving no exchange of grammatical information between constituents was not confirmed. The analysis of interindividual and intra-individual variation in gender accuracy rates revealed effects from avoidance and generalisation strategies, from linguistic variables, sociobiographical variables and psycholinguistic variables. We argue that gender errors can originate at the lemma level, at the gender node level, or at the lexeme level. Different psycholinguistic scenarios are presented to account for intra-individual variation in gender assignment and agreement
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