tcp_subcat_inductive (UCAM Sample Clauses

tcp_subcat_inductive (UCAM. Until recently, state of the art SCF acquisition systems used handcrafted rules to match natural language parser output to a set of pre-defined SCFs (Xxxxxxx and Xxxxxxx, 1997; Xxxxxxxx, 2002; Xxxxxx et al., 2007). Such approaches achieved F-measures of about 70 against a manually annotated gold standard. Recently, however, it has become more common to use an 'inductive' approach, in which the inventory of SCFs is induced directly from parsed corpus data (X'Xxxxxxx et al., 2005; Xxxxxx and Xxxxxx-Xxx, 2006; Xxxxx et al., 2008; Messiant, 2008; Xxxxx et al., 2008; Xxxxxxxxxx and Xxxxxx X Xxxxxxx, 2010; Xxxxxxxx and Xxxxxxxxx, 2010). Candidate frames are identified by grammatical relation (GR) co-occurrences, sometimes aided by language-specific heuristics. Statistical filtering or empirically-tuned thresholds are used to select frames for the final lexicon. These inductive approaches have achieved respectable accuracy (60-70 F-measure against a dictionary) and are more portable than earlier methods. They are suitable for languages and domains in which no pre-defined inventory of SCFs is available, as long as a parser is available. They are also highly scalable to large volumes of data, since the identification and selection of frames for the lexicon generally takes minimal time and resources compared to step of parsing the data. Due to the portability and scalability of inductive SCF acquisition, it was decided that the PANACEA web services would use this approach. UCAM developed and deployed the web service tpc_subcat_inductive, which was used for English and Spanish SCF acquisition. Here we describe the operation and parameters of tpc_subcat_inductive. The input to the web service is the output of a parser. The web service can accept parser output in one of two formats: either the output format of the RASP parser, or the CoNLL format [insert references]. These formats were chosen based on the parsers in use at UCAM and UPF, but adding input formats to the web service is quite straightforward, requiring only a definition of the GR format used by the parser. The web service user can define a set of verbs of interest. These are the verbs whose SCFs will appear in the lexicon. After reading the input file, the web service proceeds by identifying parsed sentences containing the target verb lemmas. For each verb lemma, the set of co- occurring GRs is tallied, and relative frequencies calculated. For instance, if the verb lemma consider appears eight times with a...
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