Skip to content

Qnarre - Quick Narrative Analyzer

Seeking out contradictions and inconsistencies in deceitful text with ever-adapting, fluidly learning, yet still reproducible systems is exactly what machine learning could have been invented for.

Version 0.2.0

Validate Complex Legal Schemes

Judicial "pruning" of "garden variety fraud" Civil RICO cases does not work: it's time for Congress to act. - DOJ Office Of Justice Programs.

"The latest available data from the federal
courts show that civil filings under the
Racketeer Influenced and Corrupt Organizations
Act (RICO) jumped significantly during FY 2018.
As of the end of fiscal year 2018, the
government reported a total of 1,405 civil
lawsuits filed in federal courts under RICO.
In fiscal year 2017, just 693 suits were filed.
- TRAC, Syracuse University or”

Detect Direct Contradictions

Consistency is simple, contradictions are limitless. Direct textual contradictions should be detected quickly.

"The feature extractor transfers the
text input into a feature vector. When
using word embeddings, it is possible
for words with similar meaning to have a
similar representation. Classifiers can
then selectively extrapolate the results
to sentences and paragraphs.”

Infer Indirect Contradictions

False conjectures lead to indirect contradictions. Weighing the cumulative "credibility" of conjectures allows us to infer indirect contradictions.

"Sentiment analysis is the process of
detecting positive or negative sentiment
in text. All utterances are uttered in
context. Analyzing subjective sentiment
without context is hard. For bipolar
text, context is learned by training or 
fine-tuning on both 'poles'."

"Conflict Graph" Of Contradictions

Connecting all the contradictions in a domain results in its perhaps intended "conflict graph". Credibility weights of edges gives us the quantitative means for analysis.

"An inferred conflict graph is most
effectively modeled by an ER model (or
entity-relationship model). Granularity
of such a model is based on learned and
inferred representations allowing for
later refinements and even versioning."

Infer Implicit Context

Natural languages rely extensively on the "understood" or implicit context of sentences. Inferring elements of this context (from "parts of speech" statistical analysis) allows us to "fill in" the variables of the lexical scopes of our sentences.

"Part-of-speech (POS) tagging categorizes
words in text depending on the definition
of the word and its context. Generated ER
models augment such inference mechanisms
to create necessary 'contexts' on the fly."

Construct Dependency Graphs

Compilers successfully analyze dependency graphs of variables for optimization. Natural language text is already optimized, and we need to reverse engineer it to arrive to an explicit, or "precise", formulation of sentences.

"Inferred contexts of sentences can be
expanded to paragraphs and even documents
using efficient and proven RDBMS 'tricks'
and on-demand synthesized SQL."

Train With 'Annotated Reality'

"Parts of speech" decomposition of sentences allows us to greatly expand our corpus with labeled samples. Targeted explicit contradictions can be introduced to guide the "attention" mechanism of our subsequent training sessions.

"Iterations of the training, inference
and ER synthesis loops create the same
dynamic that gave us the success of deep
learning neural networks, with the added
benefits of quantitative versioning.”