An important problem related to the probabilistic estimation of stochastic context-free
grammars (SCFGs) is guaranteeing the consistency of the estimated model. This problem …

This paper is devoted to the estimation of stochastic context-free grammars (SCFGs) and
their use as language models. Classical estimation algorithms, together with new ones that …

This paper introduces adaptor grammars, a class of probabilistic models of language that
generalize probabilistic context-free grammars (PCFGs). Adaptor grammars augment the …

Stochastic unification-based grammars (SUBGs) define exponential distributions over the
parses generated by a unificationbased grammar (UBG). Existing algorithms for parsing and …

Among other topics, The Informational Complexity of Learning: Perspectives on Neural
Networks and Generative Grammar brings together two important but very different learning …

Some of the alternative tag sequences may be grammatically ill-formed. For example, a
sequence such as article, adverb and noun is not allowed in Italian, and can be excluded on …

Ordinary differential equations (ODEs) are a widely used formalism for the mathematical
modeling of dynamical systems, a task omnipresent in scientific domains. The paper …

Abstract Functional Distributional Semantics (FDS) is a recent lexical semantics framework
that represents word meaning as a function from the latent space of entities to a probability …

Die natürliche Sprache spiegelt die menschlichen intellektuellen Fähigkeiten wider, die bei
weitem nicht voll verstanden sind. Solange wir die Prozesse, die bei Denken und Verstehen …

We present a method for constructing a statistical machine translation system automatically
from unannotated examples in a manner consistent with the principles of dependency …