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Linking Stanford Typed Dependencies to Support Text Analytics
- 1. LINKING STANFORD TYPED DEPENDENCIES TO SUPPORT TEXT ANALYTICS Fouad Zablith, Ibrahim H. Osman American University of Beirut
- 2. Problem • Text documents naturally include dependency relations among textual elements • Such dependencies enable readers to cognitively infer the flow of thoughts and how the various elements are semantically affected • Automatically identifying textual dependencies has been the focus of various approaches. However we observe that aggregating, accessing and reusing dependencies for further processing is still a challenge
- 3. Question • Our aim is to answer the following question: how can we make text dependencies more accessible for consumption and reuse in text analysis? • For that we focus on the following requirements: • To have unique references to textual elements • To preserve dependency links across text sources • To store and serve the data for further consumption
- 6. RDF Model Sentence RDFS:hasDescription Sentence text DCT:hasPart Term STD:… RDFS:subClassOf STD:Dependent STD:JJ STD:VB STD:NN STD:CD STD:auxiliary RDFS:subPropertyOf STD:passiveAuxiliary RDFS:subPropertyOf STD:copula STD:modifier STD:adjectivalModifier RDFS:subPropertyOf STD:quantifierModifier STD:… (all other dependency relations) Text/ context DCT:hasPart Term Label RDFS:label
- 7. Example NS:sentence/4c7aa81ba8fbcd3ad42996eb6bac18dc RDFS:hasDescription It is an efficient service NS:term/PRP/It/4c7aa81ba8fbcd3ad42996eb6bac18dc_1 It RDFS:label NS:term/VBZ/is/4c7aa81ba8fbcd3ad42996eb6bac18dc_2 Is RDFS:label NS:term/DT/an/4c7aa81ba8fbcd3ad42996eb6bac18dc_3 an RDFS:label NS:term/JJ/efficient/4c7aa81ba8fbcd3ad42996eb6bac18dc_4 efficient RDFS:label NS:term/NN/service/4c7aa81ba8fbcd3ad42996eb6bac18dc_5 service RDFS:label DCT:hasPart STD:nsubj STD:det STD:PRP STD:VBZ STD:DT STD:JJ STD:NN ISA ISA ISA ISA ISA
- 8. Scenario 3140 User Comments on eGovernment Services 174,862 triples Input Processing Output
- 9. Processing Dependencies through SPARQL – Example 1 • What were the adjectives used by users to describe their experience from the most frequent, to the less frequent?
- 10. Processing Dependencies through SPARQL – Example 2 • What were the “things” that users found “easy”?
- 11. Processing Dependencies through SPARQL – Example 3 • How is the term “Service” described by users in the comments? • users?
- 12. So What? • This graph based manipulation of dependencies would add potential benefits such as: • Aggregating and transforming distributed pieces of text as a coherent query enabled dependency layer • The possibility of “hardwiring” text dependency patterns at a query level, and hook them to further analytical tools and techniques (e.g. visualization) • The ability to easily extend the text-based graph to capture further data entities such as polarity dictionaries and perform further analytics
- 13. Future Directions • At the level of dependency RDF generator, the extractor can be improved by providing filtering mechanisms that can be controlled by the analyst • We are building an online tool that would enable users to upload a corpus, and generate the corresponding dependency RDF to be downloaded or pushed to a triplestore • We are planning to focus next on exploiting this graph representation to perform business analytics around decision models (e.g. user satisfaction and performance models)
- 14. Conclusions • We presented our work on generating a linked dependency layer on top of text documents • We highlighted the preliminary value of this layer by applying the linking process on 3,140 disparate user comments • We believe that this layer will open the path for improving the consumption and reuse of text dependencies in the context of text and business analytics