9,020 research outputs found
From treebank resources to LFG F-structures
We present two methods for automatically annotating treebank resources with functional structures. Both methods define systematic patterns of correspondence between partial PS configurations and functional structures. These are applied to PS rules extracted from treebanks, or directly to constraint set encodings of treebank PS trees
Preliminary experiments on human sensitivity to rhythmic structure in a grammar with recursive self-similarity
We present the first rhythm detection experiment using a Lindenmayer grammar, a self-similar recursive grammar shown previously to be learnable by adults using speech stimuli. Results show that learners were unable to correctly accept or reject grammatical and ungrammatical strings at the group level, although five (of 40) participants were able to do so with detailed instructions before the exposure phase
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
Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
To learn a semantic parser from denotations, a learning algorithm must search
over a combinatorially large space of logical forms for ones consistent with
the annotated denotations. We propose a new online learning algorithm that
searches faster as training progresses. The two key ideas are using macro
grammars to cache the abstract patterns of useful logical forms found thus far,
and holistic triggering to efficiently retrieve the most relevant patterns
based on sentence similarity. On the WikiTableQuestions dataset, we first
expand the search space of an existing model to improve the state-of-the-art
accuracy from 38.7% to 42.7%, and then use macro grammars and holistic
triggering to achieve an 11x speedup and an accuracy of 43.7%.Comment: EMNLP 201
Variation in English subject extraction : the case of hyperactive subjects
International audienceStarting from the well known observation that for some speakers of English, wh-subjects extracted across a transitive predicate can bear accusative case, we investigate the syntax of the pattern in which a subject is wh-moved across a passive predicate. For a minority of speakers, in this second pattern the moved wh-subject can trigger agreement with the predicate in the matrix clause, yielding an apparent case of finite raising which we will call wh-raising. In attempt to offer a unified account of these two structures, we suggest that both are possible in a grammar that allows for DPs to be 'hyperactive' (Carstens 2011) and to take part in A-operations (i.e. syntactic phenomena related to Case and agreement) in more than one clause. The analysis that we propose is couched in the cartographic framework, and adopts the approach to subject extraction from Rizzi (2006) and Rizzi & Shlonsky (2006, 2007)
Interaction Grammars
Interaction Grammar (IG) is a grammatical formalism based on the notion of
polarity. Polarities express the resource sensitivity of natural languages by
modelling the distinction between saturated and unsaturated syntactic
structures. Syntactic composition is represented as a chemical reaction guided
by the saturation of polarities. It is expressed in a model-theoretic framework
where grammars are constraint systems using the notion of tree description and
parsing appears as a process of building tree description models satisfying
criteria of saturation and minimality
A comparative evaluation of deep and shallow approaches to the automatic detection of common grammatical errors
This paper compares a deep and a shallow processing approach to the problem of classifying a sentence as grammatically wellformed or ill-formed. The deep processing
approach uses the XLE LFG parser and English grammar: two versions are presented, one which uses the XLE directly to perform the classification, and another one which uses a decision tree trained on features consisting of the XLE’s output statistics. The shallow processing approach predicts grammaticality based on n-gram frequency statistics:
we present two versions, one which uses frequency thresholds and one which uses a decision tree trained on the frequencies of the rarest n-grams in the input sentence.
We find that the use of a decision tree improves on the basic approach only for the deep parser-based approach. We also show that combining both the shallow and deep
decision tree features is effective. Our evaluation
is carried out using a large test set of grammatical and ungrammatical sentences. The ungrammatical test set is generated automatically by inserting grammatical errors
into well-formed BNC sentences
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