The Contract Search Tool is a semantic search platform that enables effective analysis of complex, long-term contractual service agreement for machines such as gas turbines. The approach we developed can effectively identify paragraphs of text for specific legal concepts. Then the key content can be decomposed and organized by the semantics model that captures key elements of the concepts and links to specific paragraphs. This is achieved by performing semantic text analysis to capture implicitly-stated provisions and the definitions of provisions, and relevant information is returned in an organized manner. The tool can be applied to increase productivity of legal review, share legal knowledge with service managers, and reduce legal risk in contract review process.

Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules targeting the extraction of a particular kind of information. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be reapplied from scratch to the entire text corpus even though only a small part of the corpus might be affected. In this paper, we describe a novel approach for information extraction in which extraction needs are expressed in the form of database queries, which are evaluated and optimized by database systems. Using database queries for information extraction enables generic extraction and minimizes reprocessing of data by performing incremental extraction to identify which part of the data is affected by the change of components or goals. Furthermore, our approach provides automated query generation components so that casual users do not have to learn the query language in order to perform extraction. To demonstrate the feasibility of our incremental extraction approach, we performed experiments to highlight two important aspects of an information extraction system: efficiency and quality of extraction results. Our experiments show that in the event of deployment of a new module, our incremental extraction approach reduces the processing time by 89.64 percent as compared to a traditional pipeline approach. By applying our methods to a corpus of 17 million biomedical abstracts, our experiments show that the query performance is efficient for real-time applications. Our experiments also revealed that our approach achieves high quality extraction results.

Identifying drug target candidates is an important task for early development throughout the drug discovery process. This process is supported by the development of new high-throughput technologies that enable better understanding of disease mechanism. With the push for personalized medicine, more experimental data are produced to identify how the genetics differ among individuals with respect to disease mechanism and drug response. It becomes critical to facilitate effective analysis of the large amount of biological data. In this paper, we describe our solution in employing text mining as a technique for finding scientific information for target and biomarker discovery from the biomedical literature. Additionally, we discuss how the extracted knowledge can be an effective resource for the analysis of biological data such as next-generation sequencing data.

The BioNLP’09 Shared Task deals with extracting information on molecular events, such as gene expression and protein localization, from natural language text. Information in this benchmark are given as tuples including protein names, trigger terms for each event, and possible other participants such as bindings sites. We address all three tasks of BioNLP’09: event detection, event enrichment, and recognition of negation and speculation. Our method for the first two tasks is based on a deep parser; we store the parse tree of each sentence in a relational database scheme. From the training data, we collect the dependencies connecting any two relevant terms of a known tuple, that is, the shortest paths linking these two constituents. We encode all such linkages in a query language to retrieve similar linkages from unseen text. For the third task, we rely on a hierarchy of hand-crafted regular expressions to recognize speculation and negated events. In this paper, we added extensions regarding a post-processing step that handles ambiguous event trigger terms, as well as an extension of the query language to relax linkage constraints. On the BioNLP Shared Task test data, we achieve an overall F1-measure of 32%, 29%, and 30% for the successive Tasks 1, 2, and 3, respectively.

Information extraction systems are traditionally implemented as a pipeline of special-purpose processing modules. A major drawback of such an approach is that whenever a new extraction goal emerges or a module is improved, extraction has to be re-applied from scratch to the entire text corpus even though only a small part of the corpus might be affected. In this demonstration proposal, we describe a novel paradigm for information extraction: we store the parse trees output by text processing in a database, and then express extraction needs using queries, which can be evaluated and optimized by databases. Compared with the existing approaches, database queries for information extraction enable generic extraction and minimize reprocessing. However, such an approach also poses a lot of technical challenges, such as language design, optimization and automatic query generation. We will present the opportunities and challenges that we met when building GenerIE, a system that implements this paradigm.

MOTIVATION: Identifying drug-drug interactions (DDIs) is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of DDIs, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this article, we propose a novel approach that integrates text mining and automated reasoning to derive DDIs. Through the extraction of various facts of drug metabolism, not only the DDIs that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning. RESULTS: Our approach was able to find several potential DDIs that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential DDIs with scientific evidences explaining the mechanism of the interactions.

Proteins and their interactions govern virtually all cellular processes, such as regulation, signaling, metabolism, and structure. Most experimental findings pertaining to such interactions are discussed in research papers, which, in turn, get curated by protein interaction databases. Authors, editors, and publishers benefit from efforts to alleviate the tasks of searching for relevant papers, evidence for physical interactions, and proper identifiers for each protein involved. The BioCreative II.5 community challenge addressed these tasks in a competition-style assessment to evaluate and compare different methodologies, to make aware of the increasing accuracy of automated methods, and to guide future implementations. In this paper, we present our approaches for protein-named entity recognition, including normalization, and for extraction of protein-protein interactions from full text. Our overall goal is to identify efficient individual components, and we compare various compositions to handle a single full-text article in between 10 seconds and 2 minutes. We propose strategies to transfer document-level annotations to the sentence-level, which allows for the creation of a more fine-grained training corpus; we use this corpus to automatically derive around 5,000 patterns. We rank sentences by relevance to the task of finding novel interactions with physical evidence, using a sentence classifier built from this training corpus. Heuristics for paraphrasing sentences help to further remove unnecessary information that might interfere with patterns, such as additional adjectives, clauses, or bracketed expressions. In BioCreative II.5, we achieved an f-score of 22 percent for finding protein interactions, and 43 percent for mapping proteins to UniProt IDs; disregarding species, f-scores are 30 percent and 55 percent, respectively. On average, our best-performing setup required around 2 minutes per full text. All data and pattern sets as well as Java classes that extend- - third-party software are available as supplementary information (see Appendix).