Lexical Adaptation of Link Grammar to the Biomedical Sublanguage: a Comparative Evaluation of Three Approaches

We study the adaptation of Link Grammar Parser to the biomedical sublanguage with a focus on domain terms not found in a general parser lexicon. Using two biomedical corpora, we implement and evaluate three approaches to addressing unknown words: automatic lexicon expansion, the use of morphological clues, and disambiguation using a part-of-speech tagger. We evaluate each approach separately for its effect on parsing performance and consider combinations of these approaches. In addition to a 45% increase in parsing efficiency, we find that the best approach, incorporating information from a domain part-of-speech tagger, offers a statistically signicant 10% relative decrease in error. The adapted parser is available under an open-source license at http://www.it.utu.fi/biolg

A Survey of Paraphrasing and Textual Entailment Methods

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of Informatics, Athens University of Economics and Business, Greece, 201

U-Compare bio-event meta-service: compatible BioNLP event extraction services

AbstractBackgroundBio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes.ResultsWe have integrated nine event extraction systems in the U-Compare framework, making them inter-compatible and interoperable with other U-Compare components. The U-Compare event meta-service provides various meta-level features for comparison and ensemble of multiple event extraction systems. Experimental results show that the performance improvements achieved by the ensemble are significant. ConclusionsWhile individual event extraction systems themselves provide useful features for bio text mining, the U-Compare meta-service is expected to improve the accessibility to the individual systems, and to enable meta-level uses over multiple event extraction systems such as comparison and ensemble.This research was partially supported by KAKENHI 18002007 [YK, MM, JDK, SP, TO, JT]; JST PRESTO and KAKENHI 21500130 [YK]; the Academy of Finland and computational resources were provided by CSC -- IT Center for Science Ltd [JB, FG]; the Research Foundation Flanders (FWO) [SVL]; UK Biotechnology and Biological Sciences, Research Council (BBSRC project BB/G013160/1 Automated Biological Event Extraction from the Literature for Drug Discovery) and JISC, National Centre for Text Mining [SA]; the Spanish grant BIO2010-17527 [MN, APM]; NIH Grant U54 DA021519 [AO, DRR]Peer Reviewe

Negation of protein-protein interactions: analysis and extraction

Sanchez Graillet O, Poesio M. Negation of protein-protein interactions: analysis and extraction. Bioinformatics. 2007;23(13):i424--i432.**Motivation**: Negative information about protein–protein interactions—from uncertainty about the occurrence of an interaction to knowledge that it did not occur—is often of great use to biologists and could lead to important discoveries. Yet, to our knowledge, no proposals focusing on extracting such information have been proposed in the text mining literature. **Results**: In this work, we present an analysis of the types of negative information that is reported, and a heuristic-based system using a full dependency parser to extract such information. We performed a preliminary evaluation study that shows encouraging results of our system. Finally, we have obtained an initial corpus of negative protein–protein interactions as basis for the construction of larger ones. **Availability**:The corpus is available by request from the authors

A DATA DRIVEN APPROACH TO IDENTIFY JOURNALISTIC 5WS FROM TEXT DOCUMENTS

Textual understanding is the process of automatically extracting accurate high-quality information from text. The amount of textual data available from different sources such as news, blogs and social media is growing exponentially. These data encode significant latent information which if extracted accurately can be valuable in a variety of applications such as medical report analyses, news understanding and societal studies. Natural language processing techniques are often employed to develop customized algorithms to extract such latent information from text. Journalistic 5Ws refer to the basic information in news articles that describes an event and include where, when, who, what and why. Extracting them accurately may facilitate better understanding of many social processes including social unrest, human rights violations, propaganda spread, and population migration. Furthermore, the 5Ws information can be combined with socio-economic and demographic data to analyze state and trajectory of these processes. In this thesis, a data driven pipeline has been developed to extract the 5Ws from text using syntactic and semantic cues in the text. First, a classifier is developed to identify articles specifically related to social unrest. The classifier has been trained with a dataset of over 80K news articles. We then use NLP algorithms to generate a set of candidates for the 5Ws. Then, a series of algorithms to extract the 5Ws are developed. These algorithms based on heuristics leverage specific words and parts-of-speech customized for individual Ws to compute their scores. The heuristics are based on the syntactic structure of the document as well as syntactic and semantic representations of individual words and sentences. These scores are then combined and ranked to obtain the best answers to Journalistic 5Ws. The classification accuracy of the algorithms is validated using a manually annotated dataset of news articles

Ontology Enrichment from Free-text Clinical Documents: A Comparison of Alternative Approaches

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships, as well as difficulty in updating the ontology as domain knowledge changes. Methodologies developed in the fields of Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), and Machine Learning (ML) provide techniques for automating the enrichment of ontology from free-text documents. In this dissertation, I extended these methodologies into biomedical ontology development. First, I reviewed existing methodologies and systems developed in the fields of NLP, IR, and IE, and discussed how existing methods can benefit the development of biomedical ontologies. This previously unconducted review was published in the Journal of Biomedical Informatics. Second, I compared the effectiveness of three methods from two different approaches, the symbolic (the Hearst method) and the statistical (the Church and Lin methods), using clinical free-text documents. Third, I developed a methodological framework for Ontology Learning (OL) evaluation and comparison. This framework permits evaluation of the two types of OL approaches that include three OL methods. The significance of this work is as follows: 1) The results from the comparative study showed the potential of these methods for biomedical ontology enrichment. For the two targeted domains (NCIT and RadLex), the Hearst method revealed an average of 21% and 11% new concept acceptance rates, respectively. The Lin method produced a 74% acceptance rate for NCIT; the Church method, 53%. As a result of this study (published in the Journal of Methods of Information in Medicine), many suggested candidates have been incorporated into the NCIT; 2) The evaluation framework is flexible and general enough that it can analyze the performance of ontology enrichment methods for many domains, thus expediting the process of automation and minimizing the likelihood that key concepts and relationships would be missed as domain knowledge evolves