Compositional Semantic Parsing on Semi-Structured Tables

Two important aspects of semantic parsing for question answering are the breadth of the knowledge source and the depth of logical compositionality. While existing work trades off one aspect for another, this paper simultaneously makes progress on both fronts through a new task: answering complex questions on semi-structured tables using question-answer pairs as supervision. The central challenge arises from two compounding factors: the broader domain results in an open-ended set of relations, and the deeper compositionality results in a combinatorial explosion in the space of logical forms. We propose a logical-form driven parsing algorithm guided by strong typing constraints and show that it obtains significant improvements over natural baselines. For evaluation, we created a new dataset of 22,033 complex questions on Wikipedia tables, which is made publicly available

The incremental use of morphological information and lexicalization in data-driven dependency parsing

Typological diversity among the natural languages of the world poses interesting challenges for the models and algorithms used in syntactic parsing. In this paper, we apply a data-driven dependency parser to Turkish, a language characterized by rich morphology and flexible constituent order, and study the effect of employing varying amounts of morpholexical information on parsing performance. The investigations show that accuracy can be improved by using representations based on inflectional groups rather than word forms, confirming earlier studies. In addition, lexicalization and the use of rich morphological features are found to have a positive effect. By combining all these techniques, we obtain the highest reported accuracy for parsing the Turkish Treebank

Dependency parsing of Turkish

The suitability of different parsing methods for different languages is an important topic in syntactic parsing. Especially lesser-studied languages, typologically different from the languages for which methods have originally been developed, poses interesting challenges in this respect. This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative free constituent order language that can be seen as the representative of a wider class of languages of similar type. Our investigations show that morphological structure plays an essential role in finding syntactic relations in such a language. In particular, we show that employing sublexical representations called inflectional groups, rather than word forms, as the basic parsing units improves parsing accuracy. We compare two different parsing methods, one based on a probabilistic model with beam search, the other based on discriminative classifiers and a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless of parsing method.We examine the impact of morphological and lexical information in detail and show that, properly used, this kind of information can improve parsing accuracy substantially. Applying the techniques presented in this article, we achieve the highest reported accuracy for parsing the Turkish Treebank

Improving dependency label accuracy using statistical post-editing: A cross-framework study

We present a statistical post-editing method for modifying the dependency labels in a dependency analysis. We test the method using two English datasets, three parsing systems and three labelled dependency schemes. We demonstrate how it can be used both to improve dependency label accuracy in parser output and highlight problems with and differences between constituency-to-dependency conversions

Attempto Controlled English (ACE)

Attempto Controlled English (ACE) allows domain specialists to interactively formulate requirements specifications in domain concepts. ACE can be accurately and efficiently processed by a computer, but is expressive enough to allow natural usage. The Attempto system translates specification texts in ACE into discourse representation structures and optionally into Prolog. Translated specification texts are incrementally added to a knowledge base. This knowledge base can be queried in ACE for verification, and it can be executed for simulation, prototyping and validation of the specification.Comment: 13 pages, compressed, uuencoded Postscript, to be presented at CLAW 96, The First International Workshop on Controlled Language Applications, Katholieke Universiteit Leuven, 26-27 March 199

Parser Hybridisation for Natural Languages

Identifying and establishing structural relations between words in natural language sentences is called Parsing. Ambiguities in natural languages make parsing a difficult task. Parsing is more difficult when dealing with a structurally complex natural language such as Arabic, which contains a number of properties that make it particularly difficult to handle. In this paper, we briefly highlight some of the complex structure of Arabic, and we identify different parsing approaches (grammar-driven and data-driven approaches) and briefly discuss their limitations. Our main goal is to combine different parsing approaches and produce a hybrid parser, which retains the advantages of data-driven approaches but is guided by grammatical rules to produce more accurate results. We describe a novel technique for directly combining different parsing approaches. Results for initial experiments that we have conducted in this work, and our plans for future work is also presented

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