Lexicalization and Grammar Development

In this paper we present a fully lexicalized grammar formalism as a particularly attractive framework for the specification of natural language grammars. We discuss in detail Feature-based, Lexicalized Tree Adjoining Grammars (FB-LTAGs), a representative of the class of lexicalized grammars. We illustrate the advantages of lexicalized grammars in various contexts of natural language processing, ranging from wide-coverage grammar development to parsing and machine translation. We also present a method for compact and efficient representation of lexicalized trees.Comment: ps file. English w/ German abstract. 10 page

Experiments towards model-based testing using Plan 9: Labelled transition file systems, stacking file systems, on-the-fly coverage measuring

We report on experiments that we did on Plan 9/Inferno to gain more experience with the file-system-as-tool-interface approach. We reimplemented functionality that we earlier worked on in Unix, trying to use Plan 9 file system interfaces. The application domain for those experiments was model-based testing.\ud \ud The idea we wanted to experiment with consists of building small, reusable pieces of functionality which are then composed to achieve the intended functionality. In particular we want to experiment with the idea of 'stacking' file servers (fs) on top of each other, where the upper fs acts as a 'filter' on the data and structure provided by the lower fs.\ud \ud For this experiment we designed a file system interface (ltsfs) that gives fine-grained access to a labelled transition system, and made two implementations of it.\ud We developed a small fs that, when 'stacked' on top of the ltsfs, extends it with additional files, and an application that uses the resulting file system.\ud \ud The hope was that an interface like the one offered by ltsfs could be used as a general interface between (specification language specific) programs that give access to state spaces and (specification language independent) programs that use (walk) those state spaces like simulators, model checkers, or test derivation programs.\ud \ud Initial results (obtained on a less-than-modern machine) suggest that, although the approach by itself is definitely feasible in principle, in practice the fine-grained access offered by ltsfs may involve many file (9p) transactions which may seriously affect performance. In Unix we used a more conservative approach where the access was less fine-grained which likely explains why there we did not suffer from this problem.\ud \ud In addition we report on experiments to use acid to obtain coverage information that is updated on-the-fly while the program is running. This worked quite well. The main observation from those experiments is that the basic block notion of this approach, which has a more 'semantical' nature, differs from the more 'syntactical' nature of the basic block notion in Unix coverage measurement tools\ud like tcov or gcov

Concurrent Lexicalized Dependency Parsing: The ParseTalk Model

A grammar model for concurrent, object-oriented natural language parsing is introduced. Complete lexical distribution of grammatical knowledge is achieved building upon the head-oriented notions of valency and dependency, while inheritance mechanisms are used to capture lexical generalizations. The underlying concurrent computation model relies upon the actor paradigm. We consider message passing protocols for establishing dependency relations and ambiguity handling.Comment: 90kB, 7pages Postscrip

Modelling and Verification of a Cluster-tree Formation Protocol Implementation for the IEEE 802.15.4 TSCH MAC Operation Mode

Correct and efficient initialization of wireless sensor networks can be challenging in the face of many uncertainties present in ad hoc wireless networks. In this paper we examine an implementation for the formation of a cluster-tree topology in a network which operates on top of the TSCH MAC operation mode of the IEEE 802.15.4 standard, and investigate it using formal methods. We show how both the mCRL2 language and toolset help us in identifying scenarios where the implementation does not form a proper topology. More importantly, our analysis leads to the conclusion that the cluster-tree formation algorithm has a super linear time complexity. So, it does not scale to large networks.Comment: In Proceedings MARS 2017, arXiv:1703.0581

Syntactic Computation as Labelled Deduction: WH a case study

This paper addresses the question "Why do WH phenomena occur with the particular cluster of properties observed across languages -- long-distance dependencies, WH-in situ, partial movement constructions, reconstruction, crossover etc." These phenomena have been analysed by invoking a number of discrete principles and categories, but have so far resisted a unified treatment. The explanation proposed is set within a model of natural language understanding in context, where the task of understanding is taken to be the incremental building of a structure over which the semantic content is defined. The formal model is a composite of a labelled type-deduction system, a modal tree logic, and a set of rules for describing the process of interpreting the string as a set of transition states. A dynamic concept of syntax results, in which in addition to an output structure associated with each string (analogous to the level of LF), there is in addition an explicit meta-level description of the process whereby this incremental process takes place. This paper argues that WH-related phenomena can be unified by adopting this dynamic perspective. The main focus of the paper is on WH-initial structures, WH in situ structures, partial movement phenomena, and crossover phenomena. In each case, an analysis is proposed which emerges from the general characterisatioan of WH structures without construction-specific stipulation.Articl

Program Synthesis using Natural Language

Interacting with computers is a ubiquitous activity for millions of people. Repetitive or specialized tasks often require creation of small, often one-off, programs. End-users struggle with learning and using the myriad of domain-specific languages (DSLs) to effectively accomplish these tasks. We present a general framework for constructing program synthesizers that take natural language (NL) inputs and produce expressions in a target DSL. The framework takes as input a DSL definition and training data consisting of NL/DSL pairs. From these it constructs a synthesizer by learning optimal weights and classifiers (using NLP features) that rank the outputs of a keyword-programming based translation. We applied our framework to three domains: repetitive text editing, an intelligent tutoring system, and flight information queries. On 1200+ English descriptions, the respective synthesizers rank the desired program as the top-1 and top-3 for 80% and 90% descriptions respectively