423 research outputs found
Sloppy Identity
Although sloppy interpretation is usually accounted for by theories of
ellipsis, it often arises in non-elliptical contexts. In this paper, a theory
of sloppy interpretation is provided which captures this fact. The underlying
idea is that sloppy interpretation results from a semantic constraint on
parallel structures and the theory is shown to predict sloppy readings for
deaccented and paycheck sentences as well as relational-, event-, and
one-anaphora. It is further shown to capture the interaction of sloppy/strict
ambiguity with quantification and binding.Comment: 20 page
A specification language for Lexical Functional Grammars
This paper defines a language L for specifying LFG grammars. This enables
constraints on LFG's composite ontology (c-structures synchronised with
f-structures) to be stated directly; no appeal to the LFG construction
algorithm is needed. We use L to specify schemata annotated rules and the LFG
uniqueness, completeness and coherence principles. Broader issues raised by
this work are noted and discussed.Comment: 6 pages, LaTeX uses eaclap.sty; Procs of Euro ACL-9
Semantic construction in feature-based TAG
We propose a semantic construction method for Feature-Based Tree Adjoining Grammar which is based on the derived tree, compare it with related proposals and briefly discuss some implementation possibilities
Grouping Synonyms by Definitions
We present a method for grouping the synonyms of a lemma according to its
dictionary senses. The senses are defined by a large machine readable
dictionary for French, the TLFi (Tr\'esor de la langue fran\c{c}aise
informatis\'e) and the synonyms are given by 5 synonym dictionaries (also for
French). To evaluate the proposed method, we manually constructed a gold
standard where for each (word, definition) pair and given the set of synonyms
defined for that word by the 5 synonym dictionaries, 4 lexicographers specified
the set of synonyms they judge adequate. While inter-annotator agreement ranges
on that task from 67% to at best 88% depending on the annotator pair and on the
synonym dictionary being considered, the automatic procedure we propose scores
a precision of 67% and a recall of 71%. The proposed method is compared with
related work namely, word sense disambiguation, synonym lexicon acquisition and
WordNet construction
Generating Text from Anonymised Structures
International audienceSurface realisation maps a meaning representation (MR) to a text, usually a single sentence. In this paper, we introduce a new parallel dataset of deep meaning representations and French sentences and we present a novel method for MR-to-text generation which seeks to generalise by abstracting away from lexical content. Most current work on natural language generation focuses on generating text that matches a reference using BLEU as evaluation criteria. In this paper, we additionally consider the model's ability to reintroduce the function words that are absent from the deep input meaning representations. We show that our approach increases both BLEU score and the scores used to assess function words generation
Semi-automatic Propbanking for French
Proceedings of the Ninth International Workshop
on Treebanks and Linguistic Theories.
Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti.
NEALT Proceedings Series, Vol. 9 (2010), 67-78.
© 2010 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/15891
French Order Without Order
International audienceTo account for the semi-free word order of French, Unification Categorial Grammar is extended in two ways. First, verbal valencies are contained in a set rather than in a list. Second, type-raised NP's are described as two-sided functors. The new framework does not overgenerate, i.e. it accepts all and only the sentences which are grammatical. This follows partly from the elimination of false lexical ambiguities -- i.e. ambiguities introduced in order to account for all the possible positions a word can be in within a sentence -- and partly from a system of features constraining the possible combinations
Analysing Data-To-Text Generation Benchmarks
Recently, several data-sets associating data to text have been created to
train data-to-text surface realisers. It is unclear however to what extent the
surface realisation task exercised by these data-sets is linguistically
challenging. Do these data-sets provide enough variety to encourage the
development of generic, high-quality data-to-text surface realisers ? In this
paper, we argue that these data-sets have important drawbacks. We back up our
claim using statistics, metrics and manual evaluation. We conclude by eliciting
a set of criteria for the creation of a data-to-text benchmark which could help
better support the development, evaluation and comparison of linguistically
sophisticated data-to-text surface realisers
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