Abstract:
Given a set of documents relevant to a litigation hold and a seed set of custodians, a second set of custodians can be generated and suggested to a user. After receiving a seed set of keywords and/or custodians, documents are identified based on their relevance. Relevant documents are searched for custodian names, and appropriate custodian names are presented to a user. Additionally, based on a first set of custodians, a suggested set of custodians can be provided to a user based on relationships between the sets of custodians.
Abstract:
Given a set of documents relevant to a litigation hold and a seed set of custodians, a second set of custodians can be generated and suggested to a user. After receiving a seed set of keywords and/or custodians, documents are identified based on their relevance. Relevant documents are searched for custodian names, and appropriate custodian names are presented to a user. Additionally, based on a first set of custodians, a suggested set of custodians can be provided to a user based on relationships between the sets of custodians.
Abstract:
Given a set of documents relevant to a litigation hold and a seed set of keywords, a second set of keywords can be generated and suggested to a user. Each document in a training set of documents is given an indication of relevance. Based on the indication of relevance, a set of further keywords relevant to the litigation is extracted from the documents and suggested to a user. The suggested set of keywords may or may not include keywords in the seed set. Additionally, the suggested set of keywords may be related to the seed set of keywords.
Abstract:
Given a set of documents relevant to a litigation hold and a seed set of keywords, a second set of keywords can be generated and suggested to a user. Each document in a training set of documents is given an indication of relevance. Based on the indication of relevance, a set of further keywords relevant to the litigation is extracted from the documents and suggested to a user. The suggested set of keywords may or may not include keywords in the seed set. Additionally, the suggested set of keywords may be related to the seed set of keywords.
Abstract:
Methods and apparatus related to contextual weighting of words. Methods are provided for determining co-occurrence relationships between words in a corpus of word groupings and for contextually weighting words in a word grouping as a function of which other words are present in the word grouping.
Abstract:
In an embodiment, characteristics of an email thread are analyzed to find related email threads. Email threads are combined to identify duplicate emails and to generate a superset thread, which maintains the context of combined email threads. The superset thread is displayed to a reviewer for review, wherein each unique email is displayed only once. In an embodiment, a system for presenting a plurality of email threads includes a thread analyzer, a database manager, and a superset thread generator. The thread analyzer analyzes characteristics of an email thread. The database manager indexes and identifies email threads from networked databases. The superset thread generator combines the email threads to determine duplicate emails and generates a superset thread, which maintains the context of each of the combined email thread.