Using foreign inclusion detection to improve parsing performance

Inclusions from other languages can be a significant source of errors for monolin-gual parsers. We show this for English in-clusions, which are sufficiently frequent to present a problem when parsing German. We describe an annotation-free approach for accurately detecting such inclusions, and de-velop two methods for interfacing this ap-proach with a state-of-the-art parser for Ger-man. An evaluation on the TIGER cor-pus shows that our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion de-tection, and even outperforms a parser with access to gold standard part-of-speech tags.

A comparative evaluation of deep and shallow approaches to the automatic detection of common grammatical errors

This paper compares a deep and a shallow processing approach to the problem of classifying a sentence as grammatically wellformed or ill-formed. The deep processing approach uses the XLE LFG parser and English grammar: two versions are presented, one which uses the XLE directly to perform the classification, and another one which uses a decision tree trained on features consisting of the XLE’s output statistics. The shallow processing approach predicts grammaticality based on n-gram frequency statistics: we present two versions, one which uses frequency thresholds and one which uses a decision tree trained on the frequencies of the rarest n-grams in the input sentence. We find that the use of a decision tree improves on the basic approach only for the deep parser-based approach. We also show that combining both the shallow and deep decision tree features is effective. Our evaluation is carried out using a large test set of grammatical and ungrammatical sentences. The ungrammatical test set is generated automatically by inserting grammatical errors into well-formed BNC sentences

GEMINI: A Natural Language System for Spoken-Language Understanding

Gemini is a natural language understanding system developed for spoken language applications. The paper describes the architecture of Gemini, paying particular attention to resolving the tension between robustness and overgeneration. Gemini features a broad-coverage unification-based grammar of English, fully interleaved syntactic and semantic processing in an all-paths, bottom-up parser, and an utterance-level parser to find interpretations of sentences that might not be analyzable as complete sentences. Gemini also includes novel components for recognizing and correcting grammatical disfluencies, and for doing parse preferences. This paper presents a component-by-component view of Gemini, providing detailed relevant measurements of size, efficiency, and performance.Comment: 8 pages, postscrip

Similarity rules! Exploring methods for ad-hoc rule detection

We examine the role of similarity in ad hoc rule detection and show how previous methods can be made more corpus independent and more generally applicable. Specifically, we show that the similarity of a rule to others in the grammar is a crucial factor in determining the reliability of a rule, providing information unavailable in frequency. We also include a way to score rules which are not in the training data, thereby providing a platform for grammar generalization

How to compare treebanks

Recent years have seen an increasing interest in developing standards for linguistic annotation, with a focus on the interoperability of the resources. This effort, however, requires a profound knowledge of the advantages and disadvantages of linguistic annotation schemes in order to avoid importing the flaws and weaknesses of existing encoding schemes into the new standards. This paper addresses the question how to compare syntactically annotated corpora and gain insights into the usefulness of specific design decisions. We present an exhaustive evaluation of two German treebanks with crucially different encoding schemes. We evaluate three different parsers trained on the two treebanks and compare results using EVALB, the Leaf-Ancestor metric, and a dependency-based evaluation. Furthermore, we present TePaCoC, a new testsuite for the evaluation of parsers on complex German grammatical constructions. The testsuite provides a well thought-out error classification, which enables us to compare parser output for parsers trained on treebanks with different encoding schemes and provides interesting insights into the impact of treebank annotation schemes on specific constructions like PP attachment or non-constituent coordination