Abstract The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of ourFuzzy Believersystem presented in this paper is to extract and analyze statements of opinion from newspaper articles. Beliefs are modeled using the fuzzy set theory, applied after Natural Language Processing-based information extraction. The Fuzzy Believer models a human agent, deciding what statements to believe or reject based on a range of configurable strategies.

BACKGROUND: In recent years, biological event extraction has emerged as a key natural language processing task, aiming to address the information overload problem in accessing the molecular biology literature. The BioNLP shared task competitions have contributed to this recent interest considerably. The first competition (BioNLP'09) focused on extracting biological events from Medline abstracts from a narrow domain, while the theme of the latest competition (BioNLP-ST'11) was generalization and a wider range of text types, event types, and subject domains were considered. We view event extraction as a building block in larger discourse interpretation and propose a two-phase, linguistically-grounded, rule-based methodology. In the first phase, a general, underspecified semantic interpretation is composed from syntactic dependency relations in a bottom-up manner. The notion of embedding underpins this phase and it is informed by a trigger dictionary and argument identification rules. Coreference resolution is also performed at this step, allowing extraction of inter-sentential relations. The second phase is concerned with constraining the resulting semantic interpretation by shared task specifications. We evaluated our general methodology on core biological event extraction and speculation/negation tasks in three main tracks of BioNLP-ST'11 (GENIA, EPI, and ID). RESULTS: We achieved competitive results in GENIA and ID tracks, while our results in the EPI track leave room for improvement. One notable feature of our system is that its performance across abstracts and articles bodies is stable. Coreference resolution results in minor improvement in system performance. Due to our interest in discourse-level elements, such as speculation/negation and coreference, we provide a more detailed analysis of our system performance in these subtasks. CONCLUSIONS: The results demonstrate the viability of a robust, linguistically-oriented methodology, which clearly distinguishes general semantic interpretation from shared task specific aspects, for biological event extraction. Our error analysis pinpoints some shortcomings, which we plan to address in future work within our incremental system development methodology.

BACKGROUND: Bio-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. RESULTS: We have integrated nine event extraction systems in the U-Compare framework, making them intercompatible 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. CONCLUSIONS: While 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.

The scientific literature is the main source for comprehensive, up-to-date biological knowledge. Automatic extraction of this knowledge facilitates core biological tasks, such as database curation and knowledge discovery. We present here a linguistically inspired, rule-based and syntax-driven methodology for biological event extraction. We rely on a dictionary of trigger words to detect and characterize event expressions and syntactic dependency based heuristics to extract their event arguments. We refine and extend our prior work to recognize speculated and negated events. We show that heuristics based on syntactic dependencies, used to identify event arguments, extend naturally to also identify speculation and negation scope. In the BioNLP’09 Shared Task on Event Extraction, our system placed third in the Core Event Extraction Task (F-score of 0.4462), and first in the Speculation and Negation Task (F-score of 0.4252). Of particular interest is the extraction of complex regulatory events, where it scored second place. Our system significantly outperformed other participating systems in detecting speculation and negation. These results demonstrate the utility of a syntax-driven approach. In this article, we also report on our more recent work on supervised learning of event trigger expressions and discuss event annotation issues, based on our corpus analysis.

The rapid evolution and growth of social media software has enabled hundreds of millions to interact within on-line communities on a global scale. While they enable communication through a common set of metaphors, such as discussion threads and quoting text in replies, this software uses a variety of diverging ways of representing discussion. Since the meaning of a conversation is defined not only by the content of a piece of text, but also by the relationships between pieces of text, part of the meaning of the discussion is obscured from automated processing.

In this paper we propose a method for semantic inter-media photographic retrieval, exploiting the advantages of both textual and content-based frameworks: the relatively high initial precision and diversity of results from visual and text retrieval, and the robust overall precision and recall of text retrieval. The method employs simple block-based visual retrieval which, at early precision, outperforms MPEG-7 features ScalableColor, ColorLayout and EdgeHistogram, allowing for reliable auto-relevance feedback. The query expansion applied respects the semantic constraints of the original query and enhances precision and confidence through redundancy. The proposed method was tested on a benchmark data set of 20,000 diverse tourist photographs and obtained promising results.

BACKGROUND: Due to the nature of scientific methodology, research articles are rich in speculative and tentative statements, also known as hedges. We explore a linguistically motivated approach to the problem of recognizing such language in biomedical research articles. Our approach draws on prior linguistic work as well as existing lexical resources to create a dictionary of hedging cues and extends it by introducing syntactic patterns. Furthermore, recognizing that hedging cues differ in speculative strength, we assign them weights in two ways: automatically using the information gain (IG) measure and semi-automatically based on their types and centrality to hedging. Weights of hedging cues are used to determine the speculative strength of sentences. RESULTS: We test our system on two publicly available hedging datasets. On the fruit-fly dataset, we achieve a precision-recall breakeven point (BEP) of 0.85 using the semi-automatic weighting scheme and a lower BEP of 0.80 with the information gain weighting scheme. These results are competitive with the previously reported best results (BEP of 0.85). On the BMC dataset, using semi-automatic weighting yields a BEP of 0.82, a statistically significant improvement (p <0.01) over the previously reported best result (BEP of 0.76), while information gain weighting yields a BEP of 0.70. CONCLUSION: Our results demonstrate that speculative language can be recognized successfully with a linguistically motivated approach and confirms that selection of hedging devices affects the speculative strength of the sentence, which can be captured reasonably by weighting the hedging cues. The improvement obtained on the BMC dataset with a semi-automatic weighting scheme indicates that our linguistically oriented approach is more portable than the machine-learning based approaches. Lower performance obtained with the information gain weighting scheme suggests that this method may benefit from a larger, manually annotated corpus for automatically inducing the weights.

We introduce a technique based on diagonal white runs and vertical edges, that divides a document image into columns and blocks which are subsequently classified as text or graphics. A diagonal white run (drun) is a set of adjacent white pixels that are diagonally connected, and a vertical edge consists of the white area between two consecutive druns. This technique was designed as a layout-independent approach. Testing the proposed approach on document images with 14 different types and layouts, written in different languages, shows comparative and promising results.

Automatic processing of international documents presents a number of challenging problems because Optical Character Recognition (OCR) techniques are not available for all languages and all script classes. Document images must be categorized according to their script type first, in our case Roman, Ideographic, or Arabic. We present a set of statistical methods that first detect and correct the skew of a document image. Next, the page is segmented into text and graphical components. The textual components are then segmented into paragraphs and lines; and finally we classify the script type into one of three categories. The system predicts the correct script category in 91% of cases when tested on real-life documents of varying kinds, diverse formats and qualities from many sources.

The authors have extended existing methods to identify the language of an on-line document after the characters have been coded using 10 character classes based on visual characteristics. In particular, they exploit word bigrams and trigrams in both a linear combination of score values and an expert systems approach. Knowledge about each language as acquired from a large number of on-line texts. Using a small set of rules, the expert system outperforms the linear combination in accuracy and shows more stability when parameter settings are varied.