In this paper , we present an innovative chart mining technique for improving parse coverage based on partial parse outputs from precision grammars . The general approach of mining features from partial analyses is applicable to a range of lexical acquisition tasks , and is particularly suited to domain-specific lexical tuning and lexical acquisition using lowcoverage grammars . As an illustration of the functionality of our proposed technique , we develop a lexical acquisition model for English verb particle constructions which operates over unlexicalised features mined from a partial parsing chart . The proposed technique is shown to outperform a state-of-the-art parser over the target task , despite being based on relatively simplistic features . Parsing with precision grammars is increasingly achieving broad coverage over open-domain texts for a range of constraint-based frameworks ( e.g. , TAG , LFG , HPSG and CCG ) , and is being used in real-world applications including information extraction , question answering , grammar checking and machine translation ( Uszkoreit , 2002 ; Oepen et al . , 2004 ; Frank et al . , 2006 ; Zhang and Kordoni , 2008 ; MacKinlay et al . , 2009 ) . In this context , a " precision grammar " is a grammar which has been engineered to model grammaticality , and contrasts with a treebank-induced grammar , for example . Inevitably , however , such applications demand complete parsing outputs , based on the assumption that the text under investigation will be completely analysable by the grammar . As precision grammars generally make strong assumptions about complete lexical coverage and grammaticality of the input , their utility is limited over noisy or domain-specific data . This lack of complete coverage can make parsing with precision grammars less attractive than parsing with shallower methods . One technique that has been successfully applied to improve parser and grammar coverage over a given corpus is error mining ( van Noord , 2004 ; de Kok et al . , 2009 ) , whereby n-grams with low " parsability " are gathered from the large-scale output of a parser as an indication of parser or ( precision ) grammar errors . However , error mining is very much oriented towards grammar engineering : its results are a mixture of different ( mistreated ) linguistic phenomena together with engineering errors for the grammar engineer to work through and act upon . Additionally , it generally does not provide any insight into the cause of the parser failure , and it is difficult to identify specific language phenomena from the output . In this paper , we instead propose a chart mining technique that works on intermediate parsing results from a parsing chart . In essence , the method analyses the validity of different analyses for words or constructions based on the " lifetime " and probability of each within the chart , combining the constraints of the grammar with probabilities to evaluate the plausibility of each . For purposes of exemplification of the proposed technique , we apply chart mining to a deep lexical acquisition ( DLA ) task , using a maximum entropybased prediction model trained over a seed lexicon and treebank . The experimental set up is the following : given a set of sentences containing putative instances of English verb particle constructions , extract a list of non-compositional VPCs optionally with valence information . For comparison , we parse the same sentence set using a state-of-the-art statistical parser , and extract the VPCs from the parser output . Our results show that our chart mining method produces a model which is superior to the treebank parser . To our knowledge , the only other work that has looked at partial parsing results of precision grammars as a means of linguistic error analysis is that of Kiefer et al . ( 1999 ) and Zhang et al . ( 2007a ) , where partial parsing models were proposed to select a set of passive edges that together cover the input sequence . Compared to these approaches , our proposed chart mining technique is more general and can be adapted to specific tasks and domains . While we experiment exclusively with an HPSG grammar in this paper , it is important to note that the proposed method can be applied to any grammar formalism which is compatible with chart parsing , and where it is possible to describe an unlexicalised lexical entry for the different categories of lexical item that are to be extracted ( see Section 3.2 for details ) . The remainder of the paper is organised as follows . Section 2 defines the task of VPC extraction . Section 3 presents the chart mining technique and the feature extraction process for the VPC extraction task . Section 4 evaluates the model performance with comparison to two competitor models over several different measures . Section 5 further discusses the general applicability of chart mining . Finally , Section 6 concludes the paper . We have proposed a chart mining technique for lexical acquisition based on partial parsing with precision grammars . We applied the proposed method to the task of extracting English verb particle constructions from a prescribed set of corpus instances . Our results showed that simple unlexicalised features mined from the chart can be used to effectively extract VPCs , and that the model outperforms a probabilistic baseline and the Charniak parser at VPC extraction .

Challenge: Error mining aids the limitations of precision parsers on noisy or domain-specific data but it does not provide the cause of the parse failure.
Approach: They propose a chart mining technique to improve parse coverage based on partial parse outputs which works on outputs from precision parsers.
Outcome: The proposed method outperforms the state-of-the-art statistical treebank parser on a deep lexical acquisition task despite being based on simple features.