Toward General-Purpose Learning for Information Extraction

Two trends are evident in the recent evolution of the field of information extraction: a preference for simple, often corpus-driven techniques over linguistically sophisticated ones; and a broadening of the central problem definition to include many non-traditional text domains. This development calls for information extraction systems which are as retargetable and general as possible. Here, we describe SRV, a learning architecture for information extraction which is designed for maximum generality and flexibility. SRV can exploit domain-specific information, including linguistic syntax and lexical information, in the form of features provided to the system explicitly as input for training. This process is illustrated using a domain created from Reuters corporate acquisitions articles. Features are derived from two general-purpose NLP systems, Sleator and Temperly's link grammar parser and Wordnet. Experiments compare the learner's performance with and without such linguistic information. Surprisingly, in many cases, the system performs as well without this information as with it.