1. Technical Field
The present invention relates to the processing of verbal communications, and more particularly, to resolving the coreference resolution problem.
2. Discussion of the Related Art
A mention is an instance of a reference to an object found in one or more documents. Mentions have types, examples including: a person, an organization, or a location. The collection of mentions that refer to the same object forms an entity. The following is illustrative.
In the following paragraph, mentions are marked with square brackets. Mention types are identified by the labels after the left bracket “[”. For example, “American Medical Association” is an “ORG(anization)”; “heir apparent” is a “PER(son).” The number following a mention type identifier is for the convenience of reference. An entity is identified by the string before “]”. Note that “ORG-1”, “ORG-2”, and “ORG-3” form an entity “E1” since they all refer to the organization “American Medical Association”. Similarly, “PER-1” and “PER-2” form another entity “E2” since both refer to the same person. Mention “PER-3” forms a single-mention entity “E3”.                The [ORG-1 American Medical Association E1] voted yesterday to install the [PER-1 heir apparent E2] as [ORG-2 its E1] [PER-2 president-elect E2], rejecting a strong, upstart challenge by a [PER-3 District doctor E3] who argued that the nation's largest physicians' [ORG-3 group E1] needs stronger ethics and new leadership.        
In many natural language applications, there is a need to know, to what entity a mention refers. This is the coreference resolution problem, also known as entity tracking. It concerns correctly grouping (also known as chaining), the mentions from one or more text documents, whose types have been marked, into entities.
A typical approach to the problem is defining a mention-pair quantity, measuring how likely the two belong to the same entity, and clustering mentions into entities based on the mention-pair measure. A drawback of this approach is the disconnection between the mention-pair modeling and decisions in the clustering step. The mention-pair measure alone is not enough to decide whether a mention should be linked with an entity, proper thresholds are needed for the system to work. Other work tries to remedy this drawback with a graphical model, which predicts an entity outcome directly, given a document and its mentions. However, computing a score of entities from mentions in a single step suffers from the high complexity of the model, and consequently, it is very difficult to well train the model.
Machine learning-based systems typically train a model that computes a binary number indicating whether two mentions link or not (i.e., hard-decision), or a real number measuring how likely it is that a pair of mentions belong to an entity (i.e., soft-decision). Information used to compute this number may include spelling, distance, gender, number, and other linguistic knowledge (e.g., apposition) of the mention pair.
Once the model is trained, a system scans mentions in a document. For each mention the system tests all mentions preceding it, and the one resulting in the “best” score is picked as the antecedent of the current mention. If none of the candidate mentions is good enough, the current mention is not linked with any preceding mention. Note that an instant decision is made as to whether the current mention links with any candidate and no alternative is kept when searching for the best candidate. Therefore, these systems can generate only one result.