PPLRE Research Question

(Redirected from PPLRE Research Topics)
Jump to: navigation, search

A PPLRE Research Question is an applied NLP research questions that have been recognized through the PPLRE Project.


1) PPLRE Research Topics - Document-based Analysis

  • Synopsis: Most current state or the art Relation Recognition Algorithms discover Semantic Relations by treating the Corpus as a Bag of Sentences (i.e. perform Sentence-level Analysis).
    • Precision could be improved by jointly analysing all of the sentences in a document (aka Discourse-level Analysis).
    • Possible approaches include: 1) The identification of the same relation expressed in another part of the document. If a relation is repeated in a document then it is more likely to be correct and would reinforce the confidence in a relation candidates. 2) Similarly the identification of a "conflicting relation" elsewhere in the document would may diminish our confidence in the prediction.

2) PPLRE Research Topics - Relations across Multiple Sentence

  • Synopsis: Most current state or the art Relation Recognition Algorithms only discover Semantic Relations that are contained within a single sentence. Recall performance could be improved by identifying relations that are expressed across multiple sentences. For example, in a biomedical document an organism is often identified early in the document and no longer explicitly restated in latter sentences that mention one of its proteins.
    • Possible approaches include: the addition of Anaphora Resolution and Coreference Resolution on named entities, building a Text Graph that joins on these entities and then performing search on the graph. Note that the spread of relations into multiple sentences is also more likely to occur as relations involve more than two entities (see Ternary Relations below).

3) PPLRE Research Topics - Ternary Relations

  • Synopsis: Recent research has focused on unary relations (NER) such as Composer(C) and on binary relations such as OrgHeadquarterLocation(O,L). The PPLRE task however is a ternary relation OPL(Organism, Protein, Location). Furthermore, N-ary Relations however are commonplace, for example Event Relations typically unite two Concepts with a Temporal Relation. While it is possible to divide an n-ary relation into two binary relations, a unified approach would have access to more information. Possibly approaches include the casting of the document into a Text Graph and then the identification of ternary patterns.

4) PPLRE Research Topics - Sentences with Many Relations

  • Synopsis: Past Relation Recognition Algorithms have been applied to mainly to tasks where the Sentences contain at most one instance of the sought relation and few if any extraneous entities to confound the pattern search. A sentence with a Company/Headquarter proposition typically will not mention more than one such relation, nor mention other companies or locations in the sentence. There is an opportunity to improve performance both in terms of recall and precision in domains, such as PPLRE, whose corpus is summarized information and with writing from a technical domain. One idea is to build a model that can predict whether two entities would share in all relations stated in the sentence.

5) PPLRE Research Topics - Many-to-many Relations

6) PPLRE Research Topics - Long-distance Sentence Patterns

  • Synopsis: Current research assumes that a relationship statements does not involve many intervening words between the entities. The PPLRE Task however involves documents with long sentences. An idea is to divide the sentence into chunks that can be disposed. Sources of information include Semantic Role Labeling and Discourse Relations.