CRL at Ntcir2

We have developed systems of two types for NTCIR2. One is an enhenced version of the system we developed for NTCIR1 and IREX. It submitted retrieval results for JJ and CC tasks. A variety of parameters were tried with the system. It used such characteristics of newspapers as locational information in the CC tasks. The system got good results for both of the tasks. The other system is a portable system which avoids free parameters as much as possible. The system submitted retrieval results for JJ, JE, EE, EJ, and CC tasks. The system automatically determined the number of top documents and the weight of the original query used in automatic-feedback retrieval. It also determined relevant terms quite robustly. For EJ and JE tasks, it used document expansion to augment the initial queries. It achieved good results, except on the CC tasks.Comment: 11 pages. Computation and Language. This paper describes our results of information retrieval in the NTCIR2 contes

Parsing User Queries using Context Free Grammars

In legal information retrieval, query cooking can significantly improve recall and precision. Context free grammars can be used to effectively parse user queries, even if the number of items torecognize is high and recognition patterns are complicated

Doctor of Philosophy

dissertationElectronic Health Records (EHRs) provide a wealth of information for secondary uses. Methods are developed to improve usefulness of free text query and text processing and demonstrate advantages to using these methods for clinical research, specifically cohort identification and enhancement. Cohort identification is a critical early step in clinical research. Problems may arise when too few patients are identified, or the cohort consists of a nonrepresentative sample. Methods of improving query formation through query expansion are described. Inclusion of free text search in addition to structured data search is investigated to determine the incremental improvement of adding unstructured text search over structured data search alone. Query expansion using topic- and synonym-based expansion improved information retrieval performance. An ensemble method was not successful. The addition of free text search compared to structured data search alone demonstrated increased cohort size in all cases, with dramatic increases in some. Representation of patients in subpopulations that may have been underrepresented otherwise is also shown. We demonstrate clinical impact by showing that a serious clinical condition, scleroderma renal crisis, can be predicted by adding free text search. A novel information extraction algorithm is developed and evaluated (Regular Expression Discovery for Extraction, or REDEx) for cohort enrichment. The REDEx algorithm is demonstrated to accurately extract information from free text clinical iv narratives. Temporal expressions as well as bodyweight-related measures are extracted. Additional patients and additional measurement occurrences are identified using these extracted values that were not identifiable through structured data alone. The REDEx algorithm transfers the burden of machine learning training from annotators to domain experts. We developed automated query expansion methods that greatly improve performance of keyword-based information retrieval. We also developed NLP methods for unstructured data and demonstrate that cohort size can be greatly increased, a more complete population can be identified, and important clinical conditions can be detected that are often missed otherwise. We found a much more complete representation of patients can be obtained. We also developed a novel machine learning algorithm for information extraction, REDEx, that efficiently extracts clinical values from unstructured clinical text, adding additional information and observations over what is available in structured text alone

Terminology Retrieval: Towards a Synergy between Thesaurus and Free Text Searching

Abstract. Multilingual Information Retrieval usually forces a choice between free text indexing or indexing by means of multilingual thesaurus. However, since they share the same objectives, synergy between both approaches is possible. This paper shows a retrieval framework that make use of terminological information in free-text indexing. The Automatic Terminology Extraction task, which is used for thesauri construction, shifts to a searching of terminology and becomes an information retrieval task: Terminology Retrieval. Terminology Retrieval, then, allows cross-language information retrieval through the browsing of morpho-syntactic, semantic and translingual variations of the query. Although terminology retrieval doesn’t make use of them, controlled vocabularies become an appropriate framework for terminology retrieval evaluation.

Multimedia Information Retrieval

With recent advances in screen and mass storage technology, together with the on-going advances in computer power, many users of personal computers and low end workstations are now regularly manipulating non-textual information. This information may be in the form of drawings, graphs, animations, sound, or video (for example). With the increased usage of these media on computer systems there has not, however, been much work in the provision of access methods to non-textual computer based information. An increasingly common method for accessing large document bases of textual information is free text retrieval. In such systems users typically enter natural language queries. These are then matched against the textual documents in the system. It is often possible for the user to re-formulate a query by providing relevance feedback, this usually takes the form of the user informing the system that certain documents are indeed relevant to the current search. This information, together with the original query, is then used by the retrieval engine to provide an improved list of matched documents. Although free text retrieval provides reasonably effective access to large document bases it does not provide easy access to non-textual information. Various query based access methods to nontextual document bases are presented, but these are all restricted to specific domains and cannot be used in mixed media systems. Hypermedia, on the other hand, is an access method for document bases which is based on the user browsing through the document base rather than issuing queries. A set of interconnected paths are constructed through the base which the user may follow. Although providing poorer access to large document bases the browsing approach does provide very natural access to non-textual information. The recent explosion in hypermedia systems and discussion has been partly due to the requirement for access to mixed media document bases. Some work is reported which presents an integration of free text retrieval based queries with hypermedia. This provides a solution to the scaling problem of browsing based systems, these systems provide access to textual nodes by query or by browsing. Non-textual nodes are, however, still only accessible by browsing - either from the starting point of the document base or from a textual document which matched the query. A model of retrieval for non-textual documents is developed, this model is based on document's context within the hypermedia document base, as opposed to the document's content. If a non-textual document is connected to several textual documents, by paths in the hypermedia, then it is likely that the non-textual document will match the query whenever a high enough proportion of the textual documents match. This model of retrieval uses clustering techniques to calculate a descriptor for non-textual nodes so that they may be retrieved directly in response to a query. To establish that this model of retrieval for non-textual documents is worthwhile an experiment was run which used the text only CACM collection. Each record within the collection was initially treated as if it were non-textual and had a cluster based description calculated based on citations, this cluster based descriptor was then compared with the actual descriptor (calculated from the record's content) to establish how accurate the cluster descriptor was. As a base case the experiment was repeated using randomly created links, as opposed to citations. The results showed that for citation based links the cluster based descriptions had a mean correlating of 0.230 with the content based description (on a range from 0 to 1, where 1 represents a perfect match) and performed approximately six times better than when random links were used (mean random correlation was 0.037). This shows that citation based cluster descriptions of documents are significantly closer to the actual descriptions than random based links, and although the correlation is quite low, the cluster approach provides a useful technique for describing documents. The model of retrieval presented for non-textual documents relies upon a hypermedia structure existing in the document base, since the model cannot work if the documents are not linked together. A user interface to a document base which gives access to a retrieval engine and to hypermedia links can be based around three main categories: browsing only access, use the retrieval engine to support link creation; query only access, use links to provide access to non-text; query and browsing access Although the last user interface may initially appear most suitable for a document base which can support queries and browsing it is also potentially the most complex interface, and may require a more complex model of retrieval for users to successfully search the document base. A set of user tests were carried out to establish user behaviour and to consider interface issues concerning easy access to documents which are held on such document bases. These tests showed that, overall, no access method was clearly better or poorer than any other method. (Abstract shortened by ProQuest.)

Document generality: its computation for ranking

The increased variety of information makes it critical to retrieve documents which are not only relevant but also broad enough to cover as many different aspects of a certain topic as possible. The increased variety of users also makes it critical to retrieve documents that are jargon free and easy-to-understand rather than the specific technical materials. In this paper, we propose a new concept namely document generality computation. Generality of document is of fundamental importance to information retrieval. Document generality is the state or quality of docu- ment being general. We compute document general- ity based on a domain-ontology method that analyzes scope and semantic cohesion of concepts appeared in the text. For test purposes, our proposed approach is then applied to improving the performance of doc- ument ranking in bio-medical information retrieval. The retrieved documents are re-ranked by a combined score of similarity and the closeness of documents’ generality to that of a query. The experiments have shown that our method can work on a large scale bio-medical text corpus OHSUMED (Hersh, Buckley, Leone & Hickam 1994), which is a subset of MEDLINE collection containing of 348,566 medical journal references and 101 test queries, with an encouraging performance

DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image

3D reconstruction from a single image is a key problem in multiple applications ranging from robotic manipulation to augmented reality. Prior methods have tackled this problem through generative models which predict 3D reconstructions as voxels or point clouds. However, these methods can be computationally expensive and miss fine details. We introduce a new differentiable layer for 3D data deformation and use it in DeformNet to learn a model for 3D reconstruction-through-deformation. DeformNet takes an image input, searches the nearest shape template from a database, and deforms the template to match the query image. We evaluate our approach on the ShapeNet dataset and show that - (a) the Free-Form Deformation layer is a powerful new building block for Deep Learning models that manipulate 3D data (b) DeformNet uses this FFD layer combined with shape retrieval for smooth and detail-preserving 3D reconstruction of qualitatively plausible point clouds with respect to a single query image (c) compared to other state-of-the-art 3D reconstruction methods, DeformNet quantitatively matches or outperforms their benchmarks by significant margins. For more information, visit: https://deformnet-site.github.io/DeformNet-website/ .Comment: 11 pages, 9 figures, NIP

A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs

While contemporary semantic search systems offer to improve classical keyword-based search, they are not always adequate for complex domain specific information needs. The domain of prescription drug abuse, for example, requires knowledge of both ontological concepts and 'intelligible constructs' not typically modeled in ontologies. These intelligible constructs convey essential information that include notions of intensity, frequency, interval, dosage and sentiments, which could be important to the holistic needs of the information seeker. We present a hybrid approach to domain specific information retrieval (or knowledge-aware search) that integrates ontology-driven query interpretation with synonym-based query expansion and domain specific rules, to facilitate search in social media. Our framework is based on a context-free grammar (CFG) that defines the query language of constructs interpretable by the search system. The grammar provides two levels of semantic interpretation: 1) a top-level CFG that facilitates retrieval of diverse textual patterns, which belong to broad templates and 2) a low-level CFG that enables interpretation of certain specific expressions that belong to such patterns. These low-level expressions occur as concepts from four different categories of data: 1) ontological concepts, 2) concepts in lexicons (such as emotions and sentiments), 3) concepts in lexicons with only partial ontology representation, called lexico-ontology concepts (such as side effects and routes of administration (ROA)), and 4) domain specific expressions (such as date, time, interval, frequency and dosage) derived solely through rules. Our approach is embodied in a novel Semantic Web platform called PREDOSE developed for prescription drug abuse epidemiology. Keywords: Knowledge-Aware Search, Ontology, Semantic Search, Background Knowledge, Context-Free GrammarComment: Accepted for publication: Journal of Web Semantics, Elsevie

The Database Query Support Processor (QSP)

The number and diversity of databases available to users continues to increase dramatically. Currently, the trend is towards decentralized, client server architectures that (on the surface) are less expensive to acquire, operate, and maintain than information architectures based on centralized, monolithic mainframes. The database query support processor (QSP) effort evaluates the performance of a network level, heterogeneous database access capability. Air Force Material Command's Rome Laboratory has developed an approach, based on ANSI standard X3.138 - 1988, 'The Information Resource Dictionary System (IRDS)' to seamless access to heterogeneous databases based on extensions to data dictionary technology. To successfully query a decentralized information system, users must know what data are available from which source, or have the knowledge and system privileges necessary to find out this information. Privacy and security considerations prohibit free and open access to every information system in every network. Even in completely open systems, time required to locate relevant data (in systems of any appreciable size) would be better spent analyzing the data, assuming the original question was not forgotten. Extensions to data dictionary technology have the potential to more fully automate the search and retrieval for relevant data in a decentralized environment. Substantial amounts of time and money could be saved by not having to teach users what data resides in which systems and how to access each of those systems. Information describing data and how to get it could be removed from the application and placed in a dedicated repository where it belongs. The result simplified applications that are less brittle and less expensive to build and maintain. Software technology providing the required functionality is off the shelf. The key difficulty is in defining the metadata required to support the process. The database query support processor effort will provide quantitative data on the amount of effort required to implement an extended data dictionary at the network level, add new systems, adapt to changing user needs, and provide sound estimates on operations and maintenance costs and savings