Evaluating effectiveness of linguistic technologies of knowledge identification in text collections

The possibility of using integral coefficients of recall and precision to evaluate effectiveness of linguistic technologies of knowledge identification in texts is analyzed in the paper. An approach is based on the method of test collections, which is used for experimental validation of received effectiveness coefficients, and on methods of mathematical statistics. The problem of maximizing the reliability of sample results in their propagation on the general population of the tested text collection is studied. The method for determining the confidence interval for the attribute proportion, which is based on Wilson’s formula, and the method for determining the required size of the relevant sample under specified relative error and confidence probability, are considered

Evaluating effectiveness of linguistic technologies of knowledge identification in text collections

The possibility of using integral coefficients of recall and precision to evaluate effectiveness of linguistic technologies of knowledge identification in texts is analyzed in the paper. An approach is based on the method of test collections, which is used for experimental validation of received effectiveness coefficients, and on methods of mathematical statistics. The problem of maximizing the reliability of sample results in their propagation on the general population of the tested text collection is studied. The method for determining the confidence interval for the attribute proportion, which is based on Wilson’s formula, and the method for determining the required size of the relevant sample under specified relative error and confidence probability, are considered

The logic and linguistic model for automatic extraction of collocation similarity

The article discusses the process of automatic identification of collocation similarity. The semantic analysis is one of the most advanced as well as the most difficult NLP task. The main problem of semantic processing is the determination of polysemy and synonymy of linguistic units. In addition, the task becomes complicated in case of word collocations. The paper suggests a logical and linguistic model for automatic determining semantic similarity between colocations in Ukraine and English languages. The proposed model formalizes semantic equivalence of collocations by means of semantic and grammatical characteristics of collocates. The basic idea of this approach is that morphological, syntactic and semantic characteristics of lexical units are to be taken into account for the identification of collocation similarity. Basic mathematical means of our model are logical-algebraic equations of the finite predicates algebra. Verb-noun and noun-adjective collocations in Ukrainian and English languages consist of words belonged to main parts of speech. These collocations are examined in the model. The model allows extracting semantically equivalent collocations from semi-structured and non-structured texts. Implementations of the model will allow to automatically recognize semantically equivalent collocations. Usage of the model allows increasing the effectiveness of natural language processing tasks such as information extraction, ontology generation, sentiment analysis and some others

Similar Text Fragments Extraction for Identifying Common Wikipedia Communities

Similar text fragments extraction from weakly formalized data is the task of natural language processing and intelligent data analysis and is used for solving the problem of automatic identification of connected knowledge fields. In order to search such common communities in Wikipedia, we propose to use as an additional stage a logical-algebraic model for similar collocations extraction. With Stanford Part-Of-Speech tagger and Stanford Universal Dependencies parser, we identify the grammatical characteristics of collocation words. WithWordNet synsets, we choose their synonyms. Our dataset includes Wikipedia articles from different portals and projects. The experimental results show the frequencies of synonymous text fragments inWikipedia articles that form common information spaces. The number of highly frequented synonymous collocations can obtain an indication of key common up-to-date Wikipedia communities

Toll-like receptor signaling and stages of addiction

Athina Markou and her colleagues discovered persistent changes in adult behavior following adolescent exposure to ethanol or nicotine consistent with increased risk for developing addiction. Building on Dr. Markou's important work and that of others in the field, researchers at the Bowles Center for Alcohol Studies have found that persistent changes in behavior following adolescent stress or alcohol exposure may be linked to induction of immune signaling in brain. This study aims to illuminate the critical interrelationship of the innate immune system (e.g., toll-like receptors [TLRs], high-mobility group box 1 [HMGB1]) in the neurobiology of addiction. This study reviews the relevant research regarding the relationship between the innate immune system and addiction. Emerging evidence indicates that TLRs in brain, particularly those on microglia, respond to endogenous innate immune agonists such as HMGB1 and microRNAs (miRNAs). Multiple TLRs, HMGB1, and miRNAs are induced in the brain by stress, alcohol, and other drugs of abuse and are increased in the postmortem human alcoholic brain. Enhanced TLR-innate immune signaling in brain leads to epigenetic modifications, alterations in synaptic plasticity, and loss of neuronal cell populations, which contribute to cognitive and emotive dysfunctions. Addiction involves progressive stages of drug binges and intoxication, withdrawal-negative affect, and ultimately compulsive drug use and abuse. Toll-like receptor signaling within cortical-limbic circuits is modified by alcohol and stress in a manner consistent with promoting progression through the stages of addiction

Biomarkers and staging of bipolar disorder: a systematic review

INTRODUCTION: A growing body of evidence suggests that bipolar disorder (BD) is a progressive disease according to clinical, biochemical and neuroimaging findings. This study reviewed the literature on the relationship between specific biomarkers and BD stages.METHODS: A comprehensive literature search of MEDLINE and PubMed was conducted to identify studies in English and Portuguese using the keywords biomarker, neurotrophic factors, inflammation, oxidative stress, neuroprogression and staging models cross-referenced with bipolar disorder.RESULTS: Morphometric studies of patients with BD found neuroanatomic abnormalities, such as ventricular enlargement, grey matter loss in the hippocampus and cerebellum, volume decreases in the prefrontal cortex and variations in the size of the amygdala. Other studies demonstrated that serum concentrations of neurotrophic factors, inflammatory mediators and oxidative stress may be used as BD biomarkers.CONCLUSIONS: The analysis of neurobiological changes associated with BD progression and activity may confirm the existence of BD biomarkers, which may be then included in staging models that will lead to improvements in treatment algorithms and more effective, individually tailored treatment regimens. Biomarkers may also be used to define early interventions to control disease progression