Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.

A Correlation Analysis on LSA and HAL Semantic Space Models

In this paper, we compare a well-known semantic spacemodel, Latent Semantic Analysis (LSA) with another model, Hyperspace Analogue to Language (HAL) which is widely used in different area, especially in automatic query refinement. We conduct this comparative analysis to prove our hypothesis that with respect to ability of extracting the lexical information from a corpus of text, LSA is quite similar to HAL. We regard HAL and LSA as black boxes. Through a Pearsonrsquos correlation analysis to the outputs of these two black boxes, we conclude that LSA highly co-relates with HAL and thus there is a justification that LSA and HAL can potentially play a similar role in the area of facilitating automatic query refinement. This paper evaluates LSA in a new application area and contributes an effective way to compare different semantic space models.

Leximancer concept mapping of patient case studies

Quantitative databases are limited to information identified as important by their creators, while databases containing natural language are limited by our ability to analyze large unstructured bodies of text. Leximancer is a tool that uses semantic mapping to develop concept maps from natural language. We have applied Leximancer to educational based pathology case notes to demonstrate how real patient records or databases of case studies could be analyzed to identify unique relationships. We then discuss how such analysis could be used to conduct quantitative analysis from databases such as the Coronary Heart Disease Database.

Nitrogen transfer within and between plants through common mycorrhizal networks (CMNs)

Mycorthizae play a critical role in nutrient capture from soils. Arbuscular mycorrhizae (AM) and ectomycorrhizae (EM) are the most important mycorrhizae in agricultural and natural ecosystems. AM and EM fungi use inorganic NH4+ and NO3-, and most EM fungi are capable of using organic nitrogen. The heavier stable isotope N-15 is discriminated against during biogeochemical and biochemical processes. Differences in N-15 (atom%) or delta(15)N (parts per thousand) provide nitrogen movement information in an experimental system. A range of 20 to 50% of one-way N-transfer has been observed from legumes to nonlegumes. Mycorrhizal fungal mycelia can extend from one plant's roots to another plant's roots to form common mycorrhizal networks (CMNs). Individual species, genera, even families of plants can be interconnected by CMNs. They are capable of facilitating nutrient uptake and flux. Nutrients such as carbon, nitrogen and phosphorus and other elements may then move via either AM or EM networks from plant to plant. Both N-15 labeling and N-15 natural abundance techniques have been employed to trace N movement between plants interconnected by AM or EM networks. Fine mesh (25similar to45 mum) has been used to separate root systems and allow only hyphal penetration and linkages but no root contact between plants. In many studies, nitrogen from N-2-fixing mycorrhizal plants transferred to non-N-2-fixing mycorrhizal plants (one-way N-transfer). In a few studies, N is also transferred from non-N-2-fixing mycorrhizal plants to N-2-fixing mycorrhizal plants (two-way N-transfer). There is controversy about whether N-transfer is direct through CMNs, or indirect through the soil. The lack of convincing data underlines the need for creative, careful experimental manipulations. Nitrogen is crucial to productivity in most terrestrial ecosystems, and there are potential benefits of management in soil-plant systems to enhance N-transfer. Thus, two-way N-transfer warrants further investigation with many species and under field conditions.

Top-down influences on lexical selection during spoken word production: A 4T fMRI investigation of refractory effects in picture naming

Spoken word production is assumed to involve stages of processing in which activation spreads through layers of units comprising lexical-conceptual knowledge and their corresponding phonological word forms. Using high-field (4T) functional magnetic resonance imagine (fMRI), we assessed whether the relationship between these stages is strictly serial or involves cascaded-interactive processing, and whether central (decision/control) processing mechanisms are involved in lexical selection. Participants performed the competitor priming paradigm in which distractor words, named from a definition and semantically related to a subsequently presented target picture, slow picture-naming latency compared to that with unrelated words. The paradigm intersperses two trials between the definition and the picture to be named, temporally separating activation in the word perception and production networks. Priming semantic competitors of target picture names significantly increased activation in the left posterior temporal cortex, and to a lesser extent the left middle temporal cortex, consistent with the predictions of cascaded-interactive models of lexical access. In addition, extensive activation was detected in the anterior cingulate and pars orbitalis of the inferior frontal gyrus. The findings indicate that lexical selection during competitor priming is biased by top-down mechanisms to reverse associations between primed distractor words and target pictures to select words that meet the current goal of speech.