Topic-influenced document relationship graphs

A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:

• obtaining, with one or more processors, a topic vector for a given topic, wherein:
• the given topic appears in a corpus of natural language text among a plurality of other topics,
• the topic vector includes a plurality of topic-specific scores for respective n-grams,
• the topic-specific scores are indicative of respective n-grams being associated with the given topic in natural language text, and
• at least some of the respective n-grams of the topic vector appear in the corpus;
• inferring, with one or more processors, a plurality of relationships from the corpus of natural language text at least in part by performing a computational linguistic analysis of the natural language text based on the topic vector,
• wherein, for a given n-gram having a given topic-specific score in the topic vector, and a given relationship among the plurality of relationships based on the given n-gram, the given relationship is suppressed or enhanced based on the given topic-specific score relative to a computational linguistic analysis of the natural language text that is not based on the topic vector by adjusting a contribution of a co-occurrence of the given n-gram in a pair of documents to a semantic similarity relationship between the pair of documents;
• and
• storing, with one or more processors, the plurality of relationships in memory.