Examining Variations of Prominent Features in Genre Classification.

This paper investigates the correlation between features of three types (visual, stylistic and topical types) and genre classes. The majority of previous studies in automated genre classification have created models based on an amalgamated representation of a document using a combination of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. In this paper we use classifiers independently modeled on three groups of features to examine six genre classes to show that the strongest features for making one classification is not necessarily the best features for carrying out another classification.

Feature Type Analysis in Automated Genre Classification

In this paper, we compare classifiers based on language model, image, and stylistic features for automated genre classification. The majority of previous studies in genre classification have created models based on an amalgamated representation of a document using a multitude of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. By independently modeling and comparing classifiers based on features belonging to three types, describing visual, stylistic, and topical properties, we demonstrate that different genres have distinctive feature strengths.

INVESTIGATING THE ROLES OF MECHANORECEPTIVE CHANNELS IN TACTILE APPARENT MOTION PERCEPTION: A VIBROTACTILE STUDY

Tactile apparent motion (TAM) is a perceptual phenomenon in which consecutive presentation of multiple tactile stimuli creates an illusion of motion. Employing a novel tactile display device, the Latero, allowed us to investigate this. The current study focused on the Rapidly Adapting (RA) channel and Slowly Adapting I (SAI) channel on the index finger. The experiment implemented vibrotactile masking stimuli to target the mechanoreceptive channels with the goal of gaining better insight into the involvement of mechanoreceptive channels in the perception of TAM. Masking stimuli were used because previous studies have used them to differentiate between different channels; a certain masking stimulus will impact a mechanoreceptive channel more than others. The experiment began by measuring participants’ threshold for TAM stimuli by varying the stimulus intensity in a two-choice task (left vs right); participants received test trials consisting of TAM stimuli with 25 Hz and 6 Hz testing for the RA and SAI channels, respectively. Next, participants performed a series of test trials with vibrotactile masking stimuli that preceded the TAM stimuli mentioned above. The vibrotactile masking stimulus varied in duration (4 seconds vs 8 seconds) and intensity (two times vs three times the intensity of the TAM stimuli). The results suggest that there was no difference in accuracy when testing for the RA and SAI channels. The results also showed that the introduction of the masking stimuli significantly lowered accuracy. Overall, neither the RA nor the SAI channel may be uniquely involved in TAM perception. However, further improvement on the current design may aid in isolating each channel to help better understand the channel’s role in TAM perception

Detecting Family Resemblance: Automated Genre Classification.

This paper presents results in automated genre classification of digital documents in PDF format. It describes genre classification as an important ingredient in contextualising scientific data and in retrieving targetted material for improving research. The current paper compares the role of visual layout, stylistic features and language model features in clustering documents and presents results in retrieving five selected genres (Scientific Article, Thesis, Periodicals, Business Report, and Form) from a pool of materials populated with documents of the nineteen most popular genres found in our experimental data set.

Automating Metadata Extraction: Genre Classification

A problem that frequently arises in the management and integration of scientific data is the lack of context and semantics that would link data encoded in disparate ways. To bridge the discrepancy, it often helps to mine scientific texts to aid the understanding of the database. Mining relevant text can be significantly aided by the availability of descriptive and semantic metadata. The Digital Curation Centre (DCC) has undertaken research to automate the extraction of metadata from documents in PDF([22]). Documents may include scientific journal papers, lab notes or even emails. We suggest genre classification as a first step toward automating metadata extraction. The classification method will be built on looking at the documents from five directions; as an object of specific visual format, a layout of strings with characteristic grammar, an object with stylo-metric signatures, an object with meaning and purpose, and an object linked to previously classified objects and external sources. Some results of experiments in relation to the first two directions are described here; they are meant to be indicative of the promise underlying this multi-faceted approach.

Formulating representative features with respect to document genre classification

Genre classification (e.g. whether a document is a scientific article or magazine article) is closely bound to the physical and conceptual structure of document as well as the level of depth involved in the text. Hence, it provides a means of ranking documents retrieved by search tools according to metrics other than topical similarity. Moreover, the structural information derived from genre classification can be used to locate target information within the text. In previous studies, the detection of genre classes has been attempted by using some normalised frequency of terms or combinations of terms in the document (here, we are using term as a reference to words, phrases, syntactic units, sentences and paragraphs, as well as other patterns derived from deeper linguistic or semantic analysis). These approaches largely neglect how the term is distributed throughout the document. Here, we report the results of automated experiments based on distributive statistics of words in order to present evidence that term distribution pattern is a better indicator of genre class than term frequency.

Building a document genre corpus: a profile of the KRYS I corpus

This paper describes the KRYS I corpus, consisting of documents classified into 70 genre classes. It has been constructed as part of an effort to automate document genre classification as distinct from topic detection. Previously there has been very little work on building corpora of texts which have been classified using a nontopical genre palette. The reason for this is partly due to the fact that genre as a concept, is rooted in philosophy, rhetoric and literature, and highly complex and domain dependent in its interpretation ([11]). The usefulness of genre in everyday information search is only now starting to be recognised and there is no genre classification schema that has been consolidated to have applicable value in this direction. By presenting here our experiences in constructing the KRYS I corpus, we hope to shed light on the information gathering and seeking behaviour and the role of genre in these activities, as well as a way forward for creating a better corpus for testing automated genre classification tasks and the application of these tasks to other domains.

Operator Counting for N=2 Chern-Simons Gauge Theories with Chiral-like Matter Fields

The localization formula of Chern-Simons quiver gauge theory on $S^3$ nicely reproduces the geometric data such as volume of Sasaki-Einstein manifolds in the large-$N$ limit, at least for vector-like models. The validity of chiral-like models is not established yet, due to technical problems in both analytic and numerical approaches. Recently Gulotta, Herzog and Pufu suggested that the counting of chiral operators can be used to find the eigenvalue distribution of quiver matrix models. In this paper we apply this method to some vector-like or chiral-like quiver theories, including the triangular quivers with generic Chern-Simons levels which are dual to in-homogeneous Sasaki-Einstein manifolds $Y^{p,k}(\mathbb{CP}^2)$. The result is consistent with AdS/CFT and the volume formula. We discuss the implication of our analysis.Comment: 23 pages; v2. revised version; v3. corrected typos and clarified argument