Unsupervised Formal Grammar Induction with Confidence

I present a novel algorithm for minimally supervised formal grammar induction using a linguistically-motivated grammar formalism. This algorithm, called the Missing Link algorithm (ML), is built off of classic chart parsing methods, but makes use of a probabilistic confidence measure to keep track of potentially ambiguous lexical items. Because ML uses a structured grammar formalism, each step of the algorithm can be easily understood by linguists, making it ideal for studying the learnability of different linguistic phenomena. The algorithm requires minimal annotation in its training data, but is capable of learning nuanced data from relatively small training sets and can be applied to a variety of grammar formalisms. Though evaluating an unsupervised syntactic model is difficult, I present an evaluation using the Corpus of Linguistic Acceptability and show state-of-the-art performance

Probabilistic Programming Concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been developed since more than 20 years

Virtual Epistemologies for the Producer-Consumer Problem

In recent years, much research has been de- voted to the construction of the lookaside buffer that made emulating and possibly eval- uating suffix trees a reality; however, few have synthesized the investigation of RPCs. Given the current status of random theory, futurists daringly desire the improvement of voice-over-IP that would allow for further study into telephony, demonstrates the ap- propriate importance of cryptography. In this paper, we propose a read-write tool for analyzing scatter/gather I/O (Yerba), which we use to confirm that hierarchical databases and multi-processors can connect to accomplish this goal

Growing Graphs with Hyperedge Replacement Graph Grammars

Discovering the underlying structures present in large real world graphs is a fundamental scientific problem. In this paper we show that a graph's clique tree can be used to extract a hyperedge replacement grammar. If we store an ordering from the extraction process, the extracted graph grammar is guaranteed to generate an isomorphic copy of the original graph. Or, a stochastic application of the graph grammar rules can be used to quickly create random graphs. In experiments on large real world networks, we show that random graphs, generated from extracted graph grammars, exhibit a wide range of properties that are very similar to the original graphs. In addition to graph properties like degree or eigenvector centrality, what a graph "looks like" ultimately depends on small details in local graph substructures that are difficult to define at a global level. We show that our generative graph model is able to preserve these local substructures when generating new graphs and performs well on new and difficult tests of model robustness.Comment: 18 pages, 19 figures, accepted to CIKM 2016 in Indianapolis, I

Editorial:Algebraic Methods in Language Processing

The papers in this volume are revised and extended versions of communications presented at the Third International AMAST Workshop on Algebraic Methods in Language Processing (AMiLP-3), held at the University of Verona, Verona, Italy, 25–27 August 2003