Walking across Wikipedia: a scale-free network model of semantic memory retrieval.

Semantic knowledge has been investigated using both online and offline methods. One common online method is category recall, in which members of a semantic category like "animals" are retrieved in a given period of time. The order, timing, and number of retrievals are used as assays of semantic memory processes. One common offline method is corpus analysis, in which the structure of semantic knowledge is extracted from texts using co-occurrence or encyclopedic methods. Online measures of semantic processing, as well as offline measures of semantic structure, have yielded data resembling inverse power law distributions. The aim of the present study is to investigate whether these patterns in data might be related. A semantic network model of animal knowledge is formulated on the basis of Wikipedia pages and their overlap in word probability distributions. The network is scale-free, in that node degree is related to node frequency as an inverse power law. A random walk over this network is shown to simulate a number of results from a category recall experiment, including power law-like distributions of inter-response intervals. Results are discussed in terms of theories of semantic structure and processing

Multi-argument classification for semantic role labeling

This paper describes a Multi-Argument Classification (MAC) approach to Semantic Role Labeling. The goal is to exploit dependencies between semantic roles by simultaneously classifying all arguments as a pattern. Argument identification, as a pre-processing stage, is carried at using the improved Predicate-Argument Recognition Algorithm (PARA) developed by Lin and Smith (2006). Results using standard evaluation metrics show that multi-argument classification, archieving 76.60 in F₁ measurement on WSJ 23, outperforms existing systems that use a single parse tree for the CoNLL 2005 shared task data. This paper also describes ways to significantly increase the speed of multi-argument classification, making it suitable for real-time language processing tasks that require semantic role labelling

Dynamic Discovery of Type Classes and Relations in Semantic Web Data

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method

Communicating hands: ERPs elicited by meaningful symbolic hand postures.

Meaningful and meaningless hand postures were presented to subjects who had to carry out a semantic discrimination task while electrical brain responses were recorded. Both meaningful and control sets of hand postures were matched as closely as possible. The ERPs elicited by meaningless hand postures showed an anteriorly distributed N300 and a centro-posteriorly distributed N400 component. The N300 probably reflects picture-specific processes, whereas the N400-effect probably reflects processing in an amodal semantic network. The scalp-distribution of the N400-effect, which is more posterior than usually reported in picture processing, suggests that the semantic representations of the concepts expressed by meaningful hand postures have similar properties to those of abstract words

Using textual clues to improve metaphor processing

In this paper, we propose a textual clue approach to help metaphor detection, in order to improve the semantic processing of this figure. The previous works in the domain studied the semantic regularities only, overlooking an obvious set of regularities. A corpus-based analysis shows the existence of surface regularities related to metaphors. These clues can be characterized by syntactic structures and lexical markers. We present an object oriented model for representing the textual clues that were found. This representation is designed to help the choice of a semantic processing, in terms of possible non-literal meanings. A prototype implementing this model is currently under development, within an incremental approach allowing step-by-step evaluations. \footnote{This work takes part in a research project sponsored by the AUPELF-UREF (Francophone Agency For Education and Research)}Comment: 3 pages, single LaTeX file, uses aclap.st

Remembering 'zeal' but not 'thing':reverse frequency effects as a consequence of deregulated semantic processing

More efficient processing of high frequency (HF) words is a ubiquitous finding in healthy individuals, yet frequency effects are often small or absent in stroke aphasia. We propose that some patients fail to show the expected frequency effect because processing of HF words places strong demands on semantic control and regulation processes, counteracting the usual effect. This may occur because HF words appear in a wide range of linguistic contexts, each associated with distinct semantic information. This theory predicts that in extreme circumstances, patients with impaired semantic control should show an outright reversal of the normal frequency effect. To test this prediction, we tested two patients with impaired semantic control with a delayed repetition task that emphasised activation of semantic representations. By alternating HF and low frequency (LF) trials, we demonstrated a significant repetition advantage for LF words, principally because of perseverative errors in which patients produced the previous LF response in place of the HF target. These errors indicated that HF words were more weakly activated than LF words. We suggest that when presented with no contextual information, patients generate a weak and unstable pattern of semantic activation for HF words because information relating to many possible contexts and interpretations is activated. In contrast, LF words tend are associated with more stable patterns of activation because similar semantic information is activated whenever they are encountered

The influence of semantic context on initial eye landing sites in words

To determine the role of ongoing processing on eye guidance in reading, two studies examined the effects of semantic context on the eyes' initial landing position in words of different levels of processing diffculty. Results from both studies clearly indicate a shift of the initial fixation location towards the end of the words for words that can be predicted from a prior semantic context. However, shifts occur only in high-frequency words and with prior fixations close to the beginning of the target word. These results suggest that ongoing perceptual and linguistic processes can affect the decision of where to send the eyes next in reading. They are explained in terms of the easiness of processing associated with the target words when located in parafoveal vision. It is concluded that two critical factors might help observing effects of linguistic variables on initial landing sites, namely, the frequency of the target word and the position where the eyes are launched from as regards to the beginning of the target word. Results also provide evidence for an early locus of semantic context effects in reading