Information Retrieval (IR) is concerned with the identification of documents in a collection that are relevant to a given information need, usually represented as a query containing terms or keywords, which are supposed to be a good description of what the user is looking for. IR systems may improve their effectiveness (i.e., increasing the number of relevant documents retrieved) by using a process of query expansion, which automatically adds new terms to the original query posed by an user. In this paper we develop a method of query expansion based on Bayesian networks. Using a learning algorithm, we construct a Bayesian network that represents some of the relationships among the terms appearing in a given document collection; this network is then used as a thesaurus (specific for that collection). We also report the results obtained by our method on three standard test collections.

We present examples where the use of belief functions provided sound and elegant solutions to real life problems. These are essentially characterized by ?missing' information. The examples deal with 1) discriminant analysis using a learning set where classes are only partially known; 2) an information retrieval systems handling inter-documents relationships; 3) the combination of data from sensors competent on partially overlapping frames; 4) the determination of the number of sources in a multi-sensor environment by studying the inter-sensors contradiction. The purpose of the paper is to report on such applications where the use of belief functions provides a convenient tool to handle ?messy' data problems.

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for sampling, marginalization, conditioning, and other inference tasks. We provide a gentle introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and show how DPPs can be applied to real-world applications like finding diverse sets of high-quality search results, building informative summaries by selecting diverse sentences from documents, modeling non-overlapping human poses in images or video, and automatically building timelines of important news stories.

The human mind has a remarkable ability to store a vast amount of information in memory, and an even more remarkable ability to retrieve these experiences when needed. Understanding the representations and algorithms that underlie human memory search could potentially be useful in other information retrieval settings, including internet search. Psychological studies have revealed clear regularities in how people search their memory, with clusters of semantically related items tending to be retrieved together. These findings have recently been taken as evidence that human memory search is similar to animals foraging for food in patchy environments, with people making a rational decision to switch away from a cluster of related information as it becomes depleted. We demonstrate that the results that were taken as evidence for this account also emerge from a random walk on a semantic network, much like the random web surfer model used in internet search engines. This offers a simpler and more unified account of how people search their memory, postulating a single process rather than one process for exploring a cluster and one process for switching between clusters.

Derivative Free Optimization (DFO) is attractive when the objective function's derivatives are not available and evaluations are costly. Moreover, if the function evaluations are noisy, then approximating gradients by finite differences is difficult. This paper gives quantitative lower bounds on the performance of DFO with noisy function evaluations, exposing a fundamental and unavoidable gap between optimization performance based on noisy evaluations versus noisy gradients. This challenges the conventional wisdom that the method of finite differences is comparable to a stochastic gradient. However, there are situations in which DFO is unavoidable, and for such situations we propose a new DFO algorithm that is proved to be near optimal for the class of strongly convex objective functions. A distinctive feature of the algorithm is that it only uses Boolean-valued function comparisons, rather than evaluations. This makes the algorithm useful in an even wider range of applications, including optimization based on paired comparisons from human subjects, for example. Remarkably, we show that regardless of whether DFO is based on noisy function evaluations or Boolean-valued function comparisons, the convergence rate is the same.

Ironic utterances promise an expected meaning that never arrives, and deliver instead a meaning that exposes the failure of our expectations. Though they can appear contextually inappropriate, ironic statements succeed when they subvert their context of use, so it is the context rather than the utterance that is shown to be incongruous. Every ironic statement thus poses two related questions: the first, “what is unexpected about my meaning?” helps us answer the second, “what is unexpected about my context of use?”. Like metaphor, irony is not overtly marked, and relies instead on a listener’s understanding of stereotypical norms to unpack its true meaning. In this paper we consider how irony relies upon and subverts our stereotypical knowledge of a domain, and show how this knowledge can be exploited to both recognize and generate ironic similes for a topic.

We aim to identify and control unintentional humor occurring in human-computer interaction, and recreate it intentionally. In this research we focus on text prediction systems, a type of interactive programs employed in mobile phones, search engines, and word processors. More specifically, we identified two design principles, inspired by humor and emotion theories, and implemented them in a proof-of-concept tool simulating a specific type of text prediction.

In the context of two related NIH projects supporting scientific collaboration we seek to implement an environment for collaborative information retrieval and analysis based on utility theory.

In this paper we describe the architecture of the OntoGene Relation mining pipeline and some of its recent applications. With this research overview paper we intend to provide a contribution towards the recently started discussion towards standards for information extraction architectures in the biomedical domain. Our approach delivers domain entities mentioned in each input document, as well as candidate relationships, both ranked according to a confidency score computed by the system. This information is presented to the user through an advanced interface aimed at supporting the process of interactive curation.

PubMed ® and other literature databases contain a wealth of information on diseases and their diagnosis/treatment in the form of scientific publications. In order to take advantage of such rich information, several text-mining tools have been developed for automatically detecting mentions of disease names in the PubMed abstracts. The next important step is the normalization of the various disease names to standardized vocabulary entries and medical dictionaries. To this end, we present an automatic approach for mapping disease names in PubMed abstracts to their corresponding concepts in Medical Subject Headings (MeSH ® ) or Online Mendelian Inheritance in Man (OMIM ® ). For developing our algorithm, we merged disease concept annotations from two existing corpora. In addition, we hand annotated a separate test set of decease concepts for our method evaluation. Different from others, we reformulate the disease name normalization task as an information retrieval task where input queries are disease names and search results are disease concepts. As such, our inference method builds on existing Lucene search and further improves it by taking into account the string similarity of query terms to the disease concept name and synonyms. Evaluation results show that our method compares favorably to other state-of-the-art approaches. In conclusion, we find that our approach is a simple and effective way for linking disease names to controlled vocabularies and that the merged disease corpus provides added value for the development of text mining tools for named entity recognition from biomedical text. Data is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Fellows/Dogan/disease.html