Linking syntactic and semantic arguments in a dependency-based formalism

We propose a formal characterization of variation in the syntactic realization of semantic arguments, using hierarchies of syntactic relations and thematic roles, and a mechanism of lexical inheritance to obtain emph{valency frames} from individual emph{linking types}. We embed the formalization in the new lexicalized, dependency-based grammar formalism of Topological Dependency Grammar (TDG) cite{rade-acl2001}. We account for arguments that can be alternatively realized as a NP or a PP, and model thematic role alternations. We also treat auxiliary constructions, where the correspondance between syntactic and semantic argumenthood is indirect

Learning Language from a Large (Unannotated) Corpus

A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described. The suggested approach builds on the authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well as on a number of prior papers and approaches from the statistical language learning literature. If successful, this approach would enable the mining of all the information needed to power a natural language comprehension and generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa

Factoring Predicate Argument and Scope Semantics : underspecified Semantics with LTAG

In this paper we propose a compositional semantics for lexicalized tree-adjoining grammar (LTAG). Tree-local multicomponent derivations allow separation of the semantic contribution of a lexical item into one component contributing to the predicate argument structure and a second component contributing to scope semantics. Based on this idea a syntax-semantics interface is presented where the compositional semantics depends only on the derivation structure. It is shown that the derivation structure (and indirectly the locality of derivations) allows an appropriate amount of underspecification. This is illustrated by investigating underspecified representations for quantifier scope ambiguities and related phenomena such as adjunct scope and island constraints

LTAG semantics with semantic unification

This paper sets up a framework for LTAG (Lexicalized Tree Adjoining Grammar) semantics that brings together ideas from different recent approaches addressing some shortcomings of TAG semantics based on the derivation tree. Within this framework, several sample analyses are proposed, and it is shown that the framework allows to analyze data that have been claimed to be problematic for derivation tree based LTAG semantics approaches

Large-Scale information extraction from textual definitions through deep syntactic and semantic analysis

We present DEFIE, an approach to large-scale Information Extraction (IE) based on a syntactic-semantic analysis of textual definitions. Given a large corpus of definitions we leverage syntactic dependencies to reduce data sparsity, then disambiguate the arguments and content words of the relation strings, and finally exploit the resulting information to organize the acquired relations hierarchically. The output of DEFIE is a high-quality knowledge base consisting of several million automatically acquired semantic relations

D-STAG: a Formalism for Discourse Analysis based on SDRT and using Synchronous TAG

We propose D-STAG, a new formalism for the automatic analysis of discourse. The analyses computed by d-stag are hierarchical discourse structures annotated with discourse relations, which are compatible with discourse structures computed in sdrt. A discursive STAG grammar pairs up trees anchored by discourse connectives with trees anchored by (functors associated with) discourse relations.D-STAG est un nouveau formalisme pour l'analyse automatique de discours

Discovery of Linguistic Relations Using Lexical Attraction

This work has been motivated by two long term goals: to understand how humans learn language and to build programs that can understand language. Using a representation that makes the relevant features explicit is a prerequisite for successful learning and understanding. Therefore, I chose to represent relations between individual words explicitly in my model. Lexical attraction is defined as the likelihood of such relations. I introduce a new class of probabilistic language models named lexical attraction models which can represent long distance relations between words and I formalize this new class of models using information theory. Within the framework of lexical attraction, I developed an unsupervised language acquisition program that learns to identify linguistic relations in a given sentence. The only explicitly represented linguistic knowledge in the program is lexical attraction. There is no initial grammar or lexicon built in and the only input is raw text. Learning and processing are interdigitated. The processor uses the regularities detected by the learner to impose structure on the input. This structure enables the learner to detect higher level regularities. Using this bootstrapping procedure, the program was trained on 100 million words of Associated Press material and was able to achieve 60% precision and 50% recall in finding relations between content-words. Using knowledge of lexical attraction, the program can identify the correct relations in syntactically ambiguous sentences such as ``I saw the Statue of Liberty flying over New York.''Comment: dissertation, 56 page