Johan Bos

Linguistically Motivated Large-Scale NLP with C&C and Boxer

The statistical modelling of language, together with advances in wide-coverage grammar development, have led to high levels of robustness and efficiency in NLP systems and made linguistically motivated large-scale language processing a possibility (Matsuzaki et al., 2007; Kaplan et al., 2004). This paper describes an NLP system which is based on syntactic and semantic formalisms from theoretical linguistics, and which we have used to analyse the entire Gigaword corpus (1 billion words) in less than 5 days using only 18 processors. This combination of detail and speed of analysis represents a break-through in NLP technology.

Predicate logic unplugged

Die vorliegende Arbeit wurde im Rahmen des Verbundvorhabens Verbmobil vom Bundes-ministerium f ur Bildung, Wissenschaft, Forschung und Technologie (BMBF) unter dem FF orderkennzeichen 01 IV 101 R geff ordert. Die Verantwortung f ur den Inhalt dieser Arbeit liegt bei dem Autor.

Recognising Textual Entailment with Logical Inference

We use logical inference techniques for recognising textual entailment. As the performance of theorem proving turns out to be highly dependent on not readily available background knowledge, we incorporate model building, a technique borrowed from automated reasoning, and show that it is a useful robust method to approximate entailment. Finally, we use machine learning to combine these deep semantic analysis techniques with simple shallow word overlap; the resulting hybrid model achieves high accuracy on the RTE testset, given the state of the art. Our results also show that the different techniques that we employ perform very differently on some of the subsets of the RTE corpus and as a result, it is useful to use the nature of the dataset as a feature.

Wide-coverage semantic representations from a CCG parser

This paper shows how to construct semantic representations from the derivations produced by a wide-coverage CCG parser. Unlike the dependency structures returned by the parser itself, these can be used directly for semantic interpretation. We demonstrate that well-formed semantic representations can be produced for over 97% of the sentences in unseen WSJ text. We believe this is a major step towards widecoverage semantic interpretation, one of the key objectives of the field of NLP.

Training personal robots using natural language instruction

As domestic robots become pervasive, uninitiated users will need a way to instruct them to adapt to their particular needs. The authors are designing a practical system that uses natural language to instruct a vision-based robot.

Talking to Godot: dialogue with a mobile robot

Godot is a mobile robot platform that serves as a testbed for the interface between a sophisticated low-level robot navigation and a symbolic high-level spoken dialogue system. The interesting feature of this combined system is that information flows in two directions: (1) the navigation system. supplies landmark; information from the cognitive map used for the interpretation of the users utterances in the dialogue system; and (2) the semantic content of utterances analysed by the dialogue system are used to adjust probabilities about the robots position in the navigation system.

Compositional semantics in Verbmobil

The paper discusses how compositional semantics is implemented in the Verbmobil speech-to-speech translation system using LUD, a description language for underspecified discourse representation structures. The description language and its formal interpretation in DRT are described as well as its implementation together with the architecture of the systems entire syntactic-semantic processing module. We show that a linguistically sound theory and formalism can be properly implemented in a system with (near) real-time requirements.

The Meaning Factory: Formal Semantics for Recognizing Textual Entailment and Determining Semantic Similarity

Shared Task 1 of SemEval-2014 comprised two subtasks on the same dataset of sentence pairs: recognizing textual entailment and determining textual similarity. We used an existing system based on formal semantics and logical inference to participate in the first subtask, reaching an accuracy of 82%, ranking in the top 5 of more than twenty participating systems. For determining semantic similarity we took a supervised approach using a variety of features, the majority of which was produced by our system for recognizing textual entailment. In this subtask our system achieved a mean squared error of 0.322, the best of all participating systems.

Computational Semantics in Discourse: Underspecification, Resolution, and Inference

In this paper I introduce a formalism for natural language understandingbased on a computational implementation of Discourse RepresentationTheory. The formalism covers a wide variety of semantic phenomena(including scope and lexical ambiguities, anaphora and presupposition),is computationally attractive, and has a genuine inference component. Itcombines a well-established linguistic formalism (DRT) with advancedtechniques to deal with ambiguity (underspecification), and isinnovative in the use of first-order theorem proving techniques.The architecture of the formalism for natural language understandingthat I advocate consists of three levels of processing:underspecification, resolution, andinference. Each of these levels has a distinct function andtherefore employs a different kind of semantic representation. Themappings between these different representations define the interfacesbetween the levels.I show how underspecified semantic representations can be built in acompositional way (for a fragment of English Grammar) using standardtechniques borrowed from the λ-calculus, how inferences can becarried out on discourse representations using a translation tofirst-order logic, and how existing research prototypes (discourseprocessing and spoken-dialogue systems) implement the formalism.

Meaningful conversation with a mobile robot

We describe an implementation integrating a spoken dialogue system with a mobile robot, which the user can direct to specific locations, ask for information about its status, and supply information about its environment. The robot uses an internal map for navigation, and communicates its current orientation and accessible locations to the dialogue system using a topological map as interface.