OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters.

It has been successfully applied to unsupervised learning for various NLP tasks such as coreference resolution (Poon and Domingos, 2008) and semantic parsing (Poon and Domingos, 2009).

2.3 Unsupervised Semantic Parsing

Semantic parsing aims to obtain a complete canonical meaning representation for input sentences.

This is a weaker form of supervision than the one traditionally considered in supervised semantic parsing , where the alignment is also usually provided in training (Chen and Mooney, 2008; Zettlemoyer and Collins, 2005).

semantic parsing ) accuracy is not possible on this dataset, as the data does not contain information which fields are discussed.

The alignment a defines how semantics is verbalized in the text w, and it can be represented by a meaning derivation tree in case of full semantic parsing (Poon and Domingos, 2009) or, e.g., by a hierarchical segmentation into utterances along with an utterance-field alignment in a more shallow variation of the problem.

In semantic parsing , we aim to find the most likely underlying semantics and alignment given the text:

In recent years, there has been increasing interest in statistical approaches to semantic parsing .

The obtained results are close to the state-of-art in FrameNet semantic parsing .

The availability of large scale semantic lexicons, such as FrameNet (Baker et al., 1998), allowed the adoption of a Wide family of learning paradigms in the automation of semantic parsing .

The above problems are particularly critical for frame-based shallow semantic parsing where, as opposed to more syntactic-oriented semantic labeling schemes (as Propbank (Palmer et al., 2005)), a significant mismatch exists between the semantic descriptors and the underlying syntactic annotation level.

In (J ohansson and Nugues, 2008b) the impact of different grammatical representations on the task of frame-based shallow semantic parsing is studied and the poor lexical generalization problem is outlined.