88,328 research outputs found
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Contrasting Static and Contextualised Embeddings in the use of Semantic Feature Vectors in Neurophysiological Prediction
Primary progressive aphasia (PPA) is a neurodegenerative syndrome leading to the progressive loss of speech/language. There are three PPA subtypes with unique deficits and underlying brain atrophy. We used temporal response function (TRF) modelling to examine semantic processing across PPA subtypes and age-matched controls (n = 10 per group). TRF modelling is a sophisticated regression approach that maps acoustic and/or linguistic features of a continuous stimulus to continuously collected neurophysiological data (e.g., EEG). This approach allows for examination of acoustic/linguistic processing without the need for overt responses and has shown promise for use in PPA 1. EEG responses were collected while participants listened to an audiobook. For each word in the audiobook, feature vectors were derived using word2vec and GPT2; word2vec uses static embeddings whereas GPT2 uses contextualized embeddings, accounting for polysemy and potentially leading to a better approximation of a wordâs semantic features2. Currently, it is not clear whether GPT2 is a better approximation of humansâ semantic processing. Thus, we sought to contrast the TRFs produced by word2vec and GPT2. For each model, we derived semantic dissimilarity values for the current word given its context by calculating one minus the Pearson correlation coefficient between the current wordâs feature vector and the mean of the previous wordsâ feature vectors 3. To estimate the extent to which EEG responses could be modelled as a function of semantic dissimilarity, we used the MATLAB Multivariate Temporal Response Function (mTRF) Toolbox 4.Subsequently, TACC was utilized to estimate the null distribution for the TRFs between EEG signals and semantic dissimilarity values. Despite their contrasting embeddings, no significant differences were observed in the TRFâs predictive accuracy between word2vec and GPT2. Ongoing work seeks to disambiguate these modelsâ similar TRFs. Future research will investigate the utility of TRF modelling in differential diagnosis of PPA subtype
TAPON: a two-phase machine learning approach for semantic labelling
Through semantic labelling we enrich structured information from sources such as HTML pages, tables, or JSON files, with labels to integrate it into a local ontology. This process involves measuring some features of the information and then nding the classes that best describe it. The problem with current techniques is that they do not model relationships between classes. Their features fall short when some classes have very similar structures or textual formats. In order to deal with this problem, we have devised TAPON: a new semantic labelling technique that computes novel features that take into account the relationships. TAPON computes these features by means of a two-phase approach. In the first phase, we compute simple features and obtain a preliminary set of labels (hints). In the second phase, we inject our novel features and obtain a refined set of labels. Our experimental results show that our technique, thanks to our rich feature catalogue and novel modelling, achieves higher accuracy than other state-of-the-art techniques.Ministerio de EconomĂa y Competitividad TIN2016-75394-
Image and interpretation using artificial intelligence to read ancient Roman texts
The ink and stylus tablets discovered at the Roman Fort of Vindolanda are a unique resource for scholars of ancient history. However, the stylus tablets have proved particularly difficult to read. This paper describes a system that assists expert papyrologists in the interpretation of the Vindolanda writing tablets. A model-based approach is taken that relies on models of the written form of characters, and statistical modelling of language, to produce plausible interpretations of the documents. Fusion of the contributions from the language, character, and image feature models is achieved by utilizing the GRAVA agent architecture that uses Minimum Description Length as the basis for information fusion across semantic levels. A system is developed that reads in image data and outputs plausible interpretations of the Vindolanda tablets
Video Classification Using Spatial-Temporal Features and PCA
We investigate the problem of automated video classification by analysing the low-level audio-visual signal patterns along the time course in a holistic manner. Five popular TV broadcast genre are studied including sports, cartoon, news, commercial and music. A novel statistically based approach is proposed comprising two important ingredients designed for implicit semantic content characterisation and class identities modelling. First, a spatial-temporal audio-visual "super" feature vector is computed, capturing crucial clip-level video structure information inherent in a video genre. Second, the feature vector is further processed using Principal Component Analysis to reduce the spatial-temporal redundancy while exploiting the correlations between feature elements, which give rise to a compact representation for effective probabilistic modelling of each video genre. Extensive experiments are conducted assessing various aspects of the approach and their influence on the overall system performance
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MAC-REALM: A video content feature extraction and modelling framework
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A consequence of the âdata delugeâ is the exponential increase in digital video footage, while the ability to find relevant video clips diminishes. Traditional text based search engines are no longer optimal for searching, as they cannot provide a granular search of the content inside video footage. To be able to search the video in a content based manner, the content features of the video need to be extracted and modelled into a content model, which can then act as a searchable proxy for the video content. This thesis focuses on the extraction of syntactic and semantic content features and content modelling, using machine driven processes, with either little or no user interaction. Our abstract framework design extracts syntactic and semantic content features and compiles them into an integrated content model. The framework integrates a four plane strategy that consists of a pre-processing plane that removes redundant data and filters the media to improve the feature extraction properties of the media; a syntactic feature extraction plane that extracts low level syntactic feature and mid-level syntactic features that have semantic attributes; a semantic relationship analysis and linkage plane, where the spatial and temporal relationships of all the content features are defined, and finally a content modelling stage where the syntactic and semantic content features are integrated into a content model. Each of the four planes can be split into three layers namely, the content layer, where the content to be processed is stored; the application layer, where the content is converted into content descriptions, and the MPEG-7 layer, where content descriptions are serialised. Using MPEG-7 standards to produce the content model will provide wide-ranging interoperability, while facilitating granular multi-content type searches. The framework is aiming to âbridgeâ the semantic gap, by integrating the syntactic and semantic content features from extraction through to modelling. The design of the framework has been implemented into a prototype called MAC-REALM, which has been tested and evaluated for its effectiveness to extract and model content features. Conclusions are drawn about the research output as a whole and whether they have met the objectives. Finally, future work is presented on how concept detection and crowd sourcing can be used with MAC-REALM
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