Probabilistic Coreference in Information Extraction

Probabilistic Coreference in Information Andrew Kehler SRI International 333 Ravenswood Avenue Menlo Park, CA 94025 kehler@ai.sri.com Extraction Abstract Certain applications require that the out- put of an information extraction system be probabilistic, so that a downstream sys- tem can reliably .fuse the output with pos- sibly contradictory information from other sources. In this paper we consider the problem of assigning a probability distri- bution to alternative sets of coreference re- lationships among entity descriptions. We present the results of initial experiments with several approaches to estimating such distributions in an application using SRI's FASTUS information extraction system. 1 Introduction Natural language information extraction (IE) sys- tems take texts containing natural language as input and produce database templates populated with in- formation that is relevant to a particular application. These records may be fed as input to a downstream system for which the IE system is only one of sev- eral sources of information. In such a scenario, the downstream system must .fuse the incoming informa- tion from each of its sources, requiring the resolution of conflicts. To accomplish this, the fusion system must know the reliability of the information received from each source; in this way unreliable information from one source can be disregarded in favor of highly reliable information from another. Figure 1 exhibits this scenario with a typical IE system such as SRI's FASTUS system (Hobbs et al., 1996). The IE system has two components. The first component consists of a series of phases that recog- nize domain-relevant patterns in the text and create templates representing event and entity descriptions from them. The second component merges tem- plates created from different phrases in the text that overlap in reference. The resulting set of templates constitutes a formal description of the state of af- fairs as described in the text with respect to the application specification, which is then fed to the downstream system. As part of determining this state of affairs, the IE system must create templates describing the rel- evant entities that are reported on. This requires determining when two or more templates describe the same entity, as templates created from corefer- ring phrases need to be merged. We have performed an informal study of FASTUS's processing of a set of texts which indicates that the merging phase is where most of the ambiguities (as well as most of the errors) lie. However, most IE systems, including FASTUS, have pursued a deterministic strategy for merging and report only a single possible state of affairs. This limitation makes it difficult for a down- stream system to fuse the information with possibly contradictory information from other sources, as no information about the IE system's certainty of the results is passed along, nor is information about pos- sible alternative states of affairs and their associated levels of certainty. In this paper, we consider the problem of assign- ing a probability distribution to alternative sets of coreference relationships among entity descriptions. We present the results of initial experiments with several approaches …

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