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

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

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

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

Position statement: Inference in Question Answering

One can only exploit inference in Question-Answering (QA) and assess its contribution systematically, if one knows what inference is contributing to. Thus we identify a set of tasks specific to QA and discuss what inference could contribute to their achievement. We conclude with a proposal for graduated test suites as a tool for assessing the performance and impact of inference

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

Creating Training Corpora for NLG Micro-Planning

International audienceIn this paper, we focus on how to create data-to-text corpora which can support the learning of wide-coverage micro-planners i.e., generation systems that handle lexicalisation, aggregation, surface re-alisation, sentence segmentation and referring expression generation. We start by reviewing common practice in designing training benchmarks for Natural Language Generation. We then present a novel framework for semi-automatically creating linguistically challenging NLG corpora from existing Knowledge Bases. We apply our framework to DBpedia data and compare the resulting dataset with (Wen et al., 2016)'s dataset. We show that while (Wen et al., 2016)'s dataset is more than twice larger than ours, it is less diverse both in terms of input and in terms of text. We thus propose our corpus generation framework as a novel method for creating challenging data sets from which NLG models can be learned which are capable of generating text from KB data