Difference between revisions of "Vulcan/SystemStatus"

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(→‎Inference)
(→‎Inference)
 
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5. Compute textual transforms for T. For each T':
 
5. Compute textual transforms for T. For each T':
 
     -- Find evidence for T' using textual evidence finder.
 
     -- Find evidence for T' using textual evidence finder.
−
     -- Compute textual matching score for T' with N add add a rule Score(N, T'): N => T'
+
     -- Compute textual matching score for T' with N and add a rule Score(N, T'): N => T'
  
 
6. Go to step 2.
 
6. Go to step 2.

Latest revision as of 20:29, 13 September 2013

Overview

The input to our inference system is a natural language sentence that corresponds to an answer choice. (e.g., Iron nail is a good conductor of electricity). The output is a score that represents the system's belief regarding the validity of the input sentence.

At a high-level the system performs the following steps:

  1. Process sentence through Open IE 4.0 and convert it into a tuple. We refer to this query tuple as a proposition.
  2. Find textual evidence that could support the input proposition. Find tuples and sentences that match the input proposition. Convert this textual evidence into axioms and rules in Tuffy's MLN format.
  3. Run Tuffy with both textual and KB evidence. Output the marginal inference scores.

The system consists of Inference (online) and Axiom and Rule generation (offline) components.

Inference

Idea
Iteratively perform inference on new evidence until some termination criterion is met.
Procedure
1. Select evidence for input proposition (T) using textual evidence finder. [This procedure is a bit hazily defined at the moment.]
    -- Extract CNC and hypernym predicates from arguments in proposition.

2. Run MLN inference.

3. Check Termination criterion: Terminate if probability > threshold or if iterations exceed limit.

4. For each newly inferred fact (N): 
    -- Compute textual matching score for N and T, and add a rule Score(N, T): N => T with the textual matching score as its weight.
    -- Extract CNC and hypernym predicates from the new arguments.

5. Compute textual transforms for T. For each T':
    -- Find evidence for T' using textual evidence finder.
    -- Compute textual matching score for T' with N and add a rule Score(N, T'): N => T'

6. Go to step 2.
System Architecture
System Architecture: Online components

Status

Last updated Aug 23, 2013.

Component Description I/O Status To Do
Proposition Extractor Extracts propositions to be verified by the system Input: Sentence asserting the answer choice.
Output: A proposition represented as a open IE tuple. 		Stub implemented: Sentence piped into Open IE 4.0. Output tuples that fit a specified pattern are converted into a proposition. Select the best tuple. Stem, normalize, extract polarity, handle n-ary tuples.
Textual Evidence Finder Provides evidence for the input proposition using weak text-based methods. Input: Proposition
Output: Rules/Axioms that derive the proposition (in one-step) weighted according to the confidence provided by each method. 		Tuple matcher implementation underway. Uses Open IE solr instance to find matching sentences/tuples.
Pattern matcher not implemented.
 Implement pattern matching, improve tuple matching, add scores.
MLN Instance Generator Converts the evidence into MLN input format. Input:Axioms/Rules from weaker methods.
Output:Translated into Tuffy's input format.
 Stub implemented: Does a faithful translation to MLN syntax. Fix bug in predicate definitions.
End-to-End Driver Command line application. Input: Sentence asserting an answer choice.
Output:Score 		Stub implemented. Create web interface.

Textual Evidence Finder

Greg is going to be developing this component.

Textual Evidence Finder Details
System Architecture: Textual Evidence Finder

Axiom and Rule Generation Components

System Architecture
System Architecture: Offline components
Component Description I/O Status
Axioms Extractor Generates axioms from multiple knowledge sources. Input:
Output:		 None.
Rules Generator Uses second order rule templates to generate first-order inference rules. Input:
Output:		 None.
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