Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

Twitter sentiment analysis: how to hedge your bets In the stock markets

Emerging interest of trading companies and hedge funds in mining social web has created new avenues for intelligent systems that make use of public opinion in driving investment decisions. It is well accepted that at high frequency trading, investors are tracking memes rising up in microblogging forums to count for the public behavior as an important feature while making short term investment decisions. We investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed Twitter sentiments for more than 4 million tweets between June 2010 and July 2011 for DJIA, NASDAQ-100 and 11 other big cap technological stocks. Our results show high correlation (upto 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger’s Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of R-square (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76 % for Dow Jones Industrial Average (DJIA). We introduce and validate performance of market monitoring elements derived from public mood that can be exploited to retain a portfolio within limited risk state during typical market conditions

Učení intervalově ohodnocených fuzzy kognitivních map algoritmem PSO pro predikci abnormálních akciových výnosů

Stock return prediction is considered a challenging task in financial domain. The existence of inherent noise and volatility in daily stock price returns requires a highly complex prediction system. Generalizations of fuzzy systems have shown promising results for this task owing to their ability to handle strong uncertainty in dynamic financial markets. Moreover, financial variables are usually in difficult to interpret causal relationships. To overcome these problems, here we propose an interval-valued fuzzy cognitive map with PSO algorithm learning. This system is suitable for modelling complex nonlinear problems through causal reasoning. As the inputs of the system, we combine causally connected financial indicators and linguistic variables extracted from management discussion in annual reports. Here we show that the proposed method is effective for predicting abnormal stock return. In addition, we demonstrate that this method outperforms fuzzy cognitive maps and adaptive neuro-fuzzy rule-based systems with PSO learning.Predikce výnosů akcií je v oblasti financí považována za náročnou úlohu. Existence inherentního šumu a kolísání denních výnosů cen akcií vyžaduje velmi komplexní predikční systém. Generalizace fuzzy systémů ukazují slibné výsledky vzhledem k jejich schopnosti modelovat silnou nejistotu na dynamických finančních trzích. Finanční proměnné jsou navíc obvykle v obtížně interpretovatelných kauzálních vztazích. Abychom překonali tyto problémy, navrhujeme zde intervalovou fuzzy kognitivní mapu s učením pomocí PSO algoritmu. Tento systém je vhodný pro modelování komplexních nelineárních problémů pomocí kauzálního usuzování. Jako vstupy systému spojujeme kauzálně propojené finanční ukazatele a jazykové proměnné, které jsou získávány z diskuse managementu ve výročních zprávách. Ukazujeme, že navrhovaná metoda je účinná pro predikci abnormálního výnosu akcií. Navíc prokazujeme, že tato metoda překonává fuzzy kognitivní mapy a adaptivní systémy založené na neuro-fuzzy pravidlech s PSO učením