People have an impressive capacity for storing information about particular events. This "episodic" memory allows us to recall the context of specific experiences, such as what we did on our last holiday, and is now fairly well understood both psychologically (Tulving, 1983) and at the neural level (McClelland, McNaughton, & O'Reilly, 1995). We also have an ability, however, to acquire knowledg...

This paper proposes a formulation of grammar learning in which meaning plays a fundamental role. We present a computational model that aims to satisfy convergent constraints from cognitive linguistics and crosslinguistic developmental evidence within a statistically driven framework. The target grammar, input data and goal of learning are all designed to allow a tight coupling between language ...

Completely data-driven grammar training is prone to over-fitting. Human-defined word class knowledge is useful to address this issue. However, the manual word class taxonomy may be unreliable and irrational for statistical natural language processing, aside from its insufficient linguistic phenomena coverage and domain adaptivity. In this paper, a formalized representation of function word subc...

In recent years, whole cell modeling approaches, which combine existing knowledge and machine learning results, have received considerable attention. These approaches are potentially very efficient for simulating and analyzing physiological function of cells. In this work, entity grammar system is proposed as formalism for knowledge representation and multistratgy learning techniques in systems...

In this paper, we introduce a new normal form for context-free grammars, called reversible context-free grammars, for the problem of learning context-free grammars from positive-only examples. A context-free grammar G = (N, Z, P, S) is said to be reversible if (1) A + G( and B -+ a in P implies A = B and (2) A -+ a@ and A --f aCfl in P implies B = C. We show that the class of reversible context...

Parsing a natural languagewith its substantial structural complexity and ambiguity has turned out to be a puzzler. While the most of attempts in this area so far has relied on hand-generated parsers, difficulties inherent in the manual construction of natural language grammar lead up to efforts to induce the grammar automatically. Our approach to the automatic grammar induction presented in thi...