Information Retrieval as Statistical Translation (Adam Berger , John Lafferty, 1999)(About) > "**Turn the search problem around to predict the input**"
> We propose a new probabilistic approach to information retrieval based upon the ideas and methods of statistical machine translation. The central ingredient in this approach is **a statistical model of how a user might distill or "translate" a given document into a query**. To assess the relevance of a document to a user's query, **we estimate the probability that the query would have been generated as a translation of the document**, and factor in the user's general preferences in the form of a prior distribution over documents. We propose a simple, well motivated model of the document-to-query translation process, and describe an algorithm for learning the parameters of this model in an unsupervised manner from a collection of documents
[1806.04411] Named Entity Recognition with Extremely Limited Data (2018)(About) **"Named Entity Search (NES)"**
> We propose exploring **named entity recognition as a search task**, where the named entity class of interest is a query, and entities of that class are the relevant "documents". What should that query look like? Can we even perform NER-style labeling with tens of labels? This study presents an exploration of CRF-based NER models with handcrafted features and of how we might transform them into search queries.
> We do not propose this as a replacement
for NER, but as something to be used for an ephemeral or contextual
class of entity, when it does not make sense to label hundreds or
thousands of instances to learn a classifier
Modeling Uncertainty in Semantic Web Taxonomies(About) Information retrieval systems have to deal with uncertain knowledge and query results should reflect this uncertainty in some manner. We present a new probabilistic method to approach the problem. In our method, degrees of subsumption, i.e., overlap between concepts can be modeled and computed efficiently using Bayesian networks based on RDF(S) ontologies.