We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses synchronous context free grammars (SCFG) to jointly model the input sentence and output meaning representation. We generalize this SCFG framework to allow not one, but multiple outputs. Using this formalism, we constr...

In this paper we present a novel approach of utilizing Semantic Role Labeling (SRL) information to improve Hierarchical Phrasebased Machine Translation. We propose an algorithm to extract SRL-aware Synchronous Context-Free Grammar (SCFG) rules. Conventional Hiero-style SCFG rules will also be extracted in the same framework. Special conversion rules are applied to ensure that when SRL-aware SCF...

We present a method for induction of concise and accurate probabilistic contextfree grammars for efficient use in early stages of a multi-stage parsing technique. The method is based on the use of statistical tests to determine if a non-terminal combination is unobserved due to sparse data or hard syntactic constraints. Experimental results show that, using this method, high accuracies can be a...

A description will be given of a procedure to asslgn the most likely probabilitles to each of the rules of a given context-free grammar. The grammar developed by S. Kuno at Harvard University was picked as the basis and was successfully augmented with rule probabilities. A brief exposition of the method with some preliminary results, whenused as a device for disamblguatingparsing English texts ...

Let ? be some context-free graph. We give suucient conditions on a semi-group of bisimulations H ensuring that the quotient Hn? is context-free. Using these suucient conditions we show that the quotient Aut(?)n? of ? by its full group of automorphisms is always context-free. We then give examples showing optimality (in some sense) of the above result.

In hierarchical phrase-based machine translation, a rule table is automatically learned by heuristically extracting synchronous rules from a parallel corpus. As a result, spuriously many rules are extracted which may be composed of various incorrect rules. The larger rule table incurs more disk and memory resources, and sometimes results in lower translation quality. To resolve the problems, we...

We describe an N-gram based syntactic analysis using a dependency grammar. Instead of generalizing syntactic rules, N-gram information of parts of speech is used to segment a sequence of words into two clauses. A special part of speech, called segmentation word, which corresponds to the beginning or end symbol of clauses is introduced to express a sentence structure. Segmentation words for each...

We investigated the applicability of probabilistic context-free grammars to syllabi cation and grapheme-to-phoneme conversion. The results show that the standard probability model of context-free grammars performs very well in predicting syllable boundaries. However, our results indicate that the standard probability model does not solve grapheme-to-phoneme conversion su ciently although, we va...

The assignment of probabilities to the productions of a context-free grammar may generate an improper distribution: the probability of all finite parse trees is less than one. The condition for proper assignment is rather subtle. Production probabilities can be estimated from parsed or unparsed sentences, and the question arises as to whether or not an estimated system is automatically proper. ...