We describe a general approach to the probabilistic parsing of context-free grammars. The method integrates context-sensitive statistical knowledge of various types (e.g., syntactic and semantic) and can be trained incrementally from a bracketed corpus. We introduce a variant of the GHR contextfree recognition algorithm, and explain how to adapt it for e cient probabilistic parsing. In splitcor...

We outline four ways in which uncertainty might affect comprehension difficulty in human sentence processing. These four hypotheses motivate a self-paced reading experiment, in which we used verb subcategorization distributions to manipulate the uncertainty over the next step in the syntactic derivation (single step entropy) and the surprisal of the verb’s complement. We additionally estimate w...

a r t i c l e i n f o a b s t r a c t This paper considers the intent inference problem in a video surveillance application involving person tracking. The video surveillance is carried out through a distributed network of cameras with large non-overlapping areas. The target dynamics are formulated using a novel grammar-modulated state space model. We identify and model trajectory shapes of inte...

The notion of infix probability has been introduced in the literature as a generalization of the notion of prefix (or initial substring) probability, motivated by applications in speech recognition and word error correction. For the case where a probabilistic context-free grammar is used as language model, methods for the computation of infix probabilities have been presented in the literature,...

Using sentence templates and a stochastic context-free grammar a large corpus (10,000 sentences) has been created, where prosodic phrase boundaries are labeled in the sentences automatically during sentence generation. With perception experiments on a subset of 500 utterances we verified that 92% of the automatically marked boundaries were perceived as prosodically marked. In initial automatic ...

Traditionally, syntactic parsing is done using probabilistic context-free grammars (PCFGs) or variants thereof, as there are standard efficient methods for parsing with PCFGs and for extracting them from a corpus. However, PCFGs do not accurately represent many dependencies in natural language. For example, many determiners can only occur with certain types of nouns. Determiners like a and anot...

We present a noun chunker for German which is based on a head-lexicalised probabilistic contextfree grammar. A manually developed grammar was semi-automatically extended with robustness rules in order to allow parsing of unrestricted text. The model parameters were learned from unlabelled training data by a probabilistic context-free parser. For extracting noun chunks, the parser generates all ...

Recent results have established that there is a family of languages that is exactly the class of languages generated by three independently developed grammar formalisms: Tree Adjoining Grammm~, Head Grammars, and Linear Indexed Grammars. In this paper we show that Combinatory Categorial Grammars also generates the same class of languages. We discuss the slruclm'al descriptions produced by Combi...

Linear Context-free Rewriting Systems (LCFRS) is an expressive grammar formalism with applications in syntax-based machine translation. The parsing complexity of an LCFRS is exponential in both the rank of a production, defined as the number of nonterminals on its right-hand side, and a measure for the discontinuity of a phrase, called fan-out. In this paper, we present an algorithm that transf...

We present a novel corpus-driven approach towards grammar approximation for a linguistically deep Head-driven Phrase Structure Grammar. With an unlexicalized probabilistic context-free grammar obtained by Maximum Likelihood Estimate on a largescale automatically annotated corpus, we are able to achieve parsing accuracy higher than the original HPSG-based model. Different ways of enriching the a...