Structured Text Retrieval Models

1. Structured Text Retrieval Models

2. Str. Text Retrieval • Text Retrieval retrieves documents based on index terms. • Observation: Documents have implicit structure. • Regular text retrieval and indexing strategies lose the information available within the structure. • Text Retrieval desired based on structure. e.g. All documents having “George Bush” in the caption of a photo.

3. Models for Str. Text Retrieval • PAT Expressions • Overlapped Lists • Proximal Nodes • List of References • Tree-based • Query Languages (SFQL,CCL)

4. Proximal Nodes By Gonzalo Navarro and Ricardo Baeza-Yates • Based on hierarchical structure of documents • Structure computation is static and all structural elements are defined. “nodes” • Model attempts to define operators on these nodes based on their definition and content. • Only nodes at a particular hierarchy are returned as results.

5. Proximal Nodes Document Chapter Chapter Section Section Section

6. Proximal Nodes • Nodes are structural in nature, e.g. Chapter, Section, etc. • Each node has a defined segment (Contiguous part of text) • Operators are defined with respect to this model. • Structure operators and Text operators.

7. Proximal Nodes • Structure Operators • Name • Inclusion • Positional Inclusion • Distance operators • Child/Parent operators • Set Manipulation operators • Text Operators • Match

8. Retrieval on Evidence By Mounia Lalmas • Based on documents made up of objects. • Objects are modeled as independent entities and can be in different media, language or locations. • Document indexing – degree of uncertainty that the index term actually represents the object. • Uncertainty must be captured to get better results. • Use the Dempster-Shafer theory of evidence

9. Retrieval on Evidence • Model takes into consideration disparity between indexing vocabularies. • Aggregation of indexing vocabulary and also the aggregation of the uncertainty. Object o Є O and a type t Є T, the function type is defined as O →∂(T) Aggregation is defined over objects and composite object types contain all the types of the contained objects

10. Retrieval on Evidence • Indexing vocabulary is defined over a proposition-space. e.g. Wine (english,text), Blue(colour,feature) • Sentence space defines that indexes in the same proposition space can be used together. • Semantic between indexing vocabulary is maintained using the the notion of worlds.

11. Retrieval on Evidence • Each type t has S, W, v, π • St is the sentence space for a type • W is the possible worlds associated with St • vt is {true, false} over Wt x Pt • Πt is {true, false} over Wt x St Logical and equivalence between sentences is built around the notion of their semantics being equivalent in all or most worlds.

12. Retrieval on Evidence • However, the uncertainty of the representation remains. • This is represented by the weighting function based on the Dempster Shafer model. • These objects and their syntactic and semantic models are aggregated for the objects which contain them. E.g. A section containing sentences indexed by terms a,b,c,d.. Will be equivalent to sentences over the worlds also implying a,b,c,d…

13. Comparisons • Proximal Nodes is based on structured documents. It presents the matter clearly and provides approaches towards building a software architecture. It presents findings of conducted experiments. • The Evidence paper tries to model heterogeneous documents, made up of different media, languages, etc. Overall the model is complex and no results are given to its implementation and performance.