From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Computing the Affective-Aesthetic Potential of Literary Texts

In this paper, we compute the affective-aesthetic potential (AAP) of literary texts by using a simple sentiment analysis tool called SentiArt. In contrast to other established tools, SentiArt is based on publicly available vector space models (VSMs) and requires no emotional dictionary, thus making it applicable in any language for which VSMs have been made available (>150 so far) and avoiding issues of low coverage. In a first study, the AAP values of all words of a widely used lexical databank for German were computed and the VSM’s ability in representing concrete and more abstract semantic concepts was demonstrated. In a second study, SentiArt was used to predict ~2800 human word valence ratings and shown to have a high predictive accuracy (R2 > 0.5, p < 0.0001). A third study tested the validity of SentiArt in predicting emotional states over (narrative) time using human liking ratings from reading a story. Again, the predictive accuracy was highly significant: R2adj = 0.46, p < 0.0001, establishing the SentiArt tool as a promising candidate for lexical sentiment analyses at both the micro- and macrolevels, i.e., short and long literary materials. Possibilities and limitations of lexical VSM-based sentiment analyses of diverse complex literary texts are discussed in the light of these results

Institutional Forecasting: The Performance of Thin Virtual Stock Markets

We study the performance of Virtual Stock Markets (VSMs) in an institutional forecasting environment. We compare VSMs to the Combined Judgmental Forecast (CJF) and the Key Informant (KI) approach. We find that VSMs can be effectively applied in an environment with a small number of knowledgeable informants, i.e., in thin markets. Our results show that none of the three approaches differ in forecasting accuracy in a low knowledge-heterogeneity environment. However, where there is high knowledge-heterogeneity, the VSM approach outperforms the CJF approach, which in turn outperforms the KI approach. Hence, our results provide useful insight into when each of the three approaches might be most effectively applied.Forecasting;Electronic Markets;Information Markets;Virtual Stock Markets

Patent Analytics Based on Feature Vector Space Model: A Case of IoT

The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed

Dead-Time Effect on Two-Level Voltage Source Virtual Synchronous Machines

The Virtual Synchronous Machine (VSM) concept represents a valid solution to integrate renewable energy sources into the grid to provide straightforwardly grid services (e.g., inertial behavior, harmonic sink), grid support during faults and island operation. Under non–ideal (symmetric and sinusoidal) operating conditions, VSMs can behave as harmonic and un- balance sinks, improving the voltage quality at the point of connection to the grid. However, the inverter dead–time alters the harmonic and unbalance sink capability of voltage source VSMs. To demonstrate the negative influence of the dead– time effect, this paper uses a simplified method to predict the ideal behavior of voltage source VSMs under non–ideal grid voltage conditions. The paper demonstrates through experiments that: (1) the inverter dead–time effect limits the harmonic and unbalance sink capability of voltage source VSMs under non– ideal grid voltage conditions and (2) a dead–time compensation is needed to make the voltage source VSMs behave according to the theoretical analysis. Two experimental tests under a 5% grid voltage unbalance and a 10% grid voltage fifth harmonic distortion validate the negative influence of the dead–time and the beneficial effect of its compensation

The laurentian record of neoproterozoic glaciation, tectonism, and eukaryotic evolution in Death Vally, California

Neoproterozoic strata in Death Valley, California contain eukaryotic microfossils and glacial deposits that have been used to assess the severity of putative Snowball Earth events and the biological response to extreme environmental change. These successions also contain evidence for syn-sedimentary faulting that has been related to the rifting of Rodinia, and in turn the tectonic context of the onset of Snowball Earth. These interpretations hinge on local geological relationships and both regional and global stratigraphic correlations. Here we present new geological mapping, measured stratigraphic sections, carbon and strontium isotope chemostratigraphy, and micropaleontology from the Neoproterozoic glacial deposits and bounding strata in Death Valley. These new data enable us to refine regional correlations both across Death Valley and throughout Laurentia, and construct a new age model for glaciogenic strata and microfossil assemblages. Particularly, our remapping of the Kingston Peak Formation in the Saddle Peak Hills and near the type locality shows for the first time that glacial deposits of both the Marinoan and Sturtian glaciations can be distinguished in southeastern Death Valley, and that beds containing vase-shaped microfossils are slump blocks derived from the underlying strata. These slump blocks are associated with multiple overlapping unconformities that developed during syn-sedimentary faulting, which is a common feature of Cyrogenian strata along the margin of Laurentia from California to Alaska. With these data, we conclude that all of the microfossils that have been described to date in Neoproterozoic strata of Death Valley predate the glaciations and do not bear on the severity, extent or duration of Neoproterozoic Snowball Earth events