Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 21 for “"Vector Space Model"”.
-
Context-based motion retrieval using vector space model
… and its semantics. Our approach uses vector space model to measure the similarities among motions, which are made discrete using the vocabulary technique and transformation invariant using the relational feature model. In our approach, relational features are first extracted from …
-
Sentiment Analysis Using Deep Learning: A Comparison Between Chinese And English
… reviews. Despite many sentiment classification models having good performance on English corpora, they are not good at Chinese or other languages. Traditional sentiment approaches impose many restrictions on the raw data, and they don't have enough capacity to deal with long-distance sequential …
-
Effect of OCR errors on short documents
… system that was used is SMART, based on the vector space model. On evaluating these measures, it has been concluded that average precision and recall are not affected significantly when the OCR collection is compared to its corrected version. However, it was also concluded that with more …
-
Artificial empathy : using vector space modeling and mixed scope alignment to infer emotional states of characters in stories
… programs, I created a multi-corpus informed vector space model to determine the emotions evoked by individual terms. I then combined that information with the semantic parse trees produced by the Genesis Story Understanding System to ascertain the emotions evoked by a single sentence. …
-
Analysis of Search on Clinical Narrative within the EHR
… used to evaluate and compare different search models on clinical narrative. The second study conducted was an error analysis of the traditional, vector-space model search approach. The study examined the false positives and false negatives of this approach and categorized the errors in order to …
-
A client side tool for contextual Web search
… of the contextual information is based on a Vector Space Model and is obtained from a set of documents that have been identified as relevant to the context of the search. Two algorithms have been developed for using this contextual representation to re-rank the search results obtained using …
-
Evolutionary learning multi-agent based information retrieval systems
… approach that relies on evolutionary user-modelling. The proposed information retrieval system learns user needs from user-provided relevance feedback. The method combines a qualitative feedback measure obtained using fuzzy inference, and quantitative feedback based on evolutionary …
-
New Weighting Schemes for Document Ranking and Ranked Query Suggestion
… feature for document representation under the vector space model (VSM) framework, i.e., class specific document frequency (CSDF), which leads to a new term weighting scheme based on term frequency (TF) and the newly proposed feature. The experimental results show that the proposed methods, CSDF …
-
A Novel Hybrid Focused Crawling Algorithm to Build Domain-Specific Collections
… focused crawlers normally adopting the simple Vector Space Model and local Web search algorithms typically only find relevant Web pages with low precision. Recall also often is low, since they explore a limited sub-graph of the Web that surrounds the starting URL set, and will ignore relevant …
-
Improving Searchability of Automatically Transcribed Lectures Through Dynamic Language Modelling
… within recordings, the lexicon and language model used by the ASR engine may be dynamically adapted for the topic of each lecture. A prototype is presented which uses the English Wikipedia as a semantically dense, large language corpus to generate a custom lexicon and language model for each …
-
Automatic Identification of Topic Tags from Texts Based on Expansion-Extraction Approach
… from Cognitive Informatics with a practical model from Information Retrieval. One of the design constraints, however, is that the Web was used as a universal knowledge source, which was essential in accessing the required information for inferring topics from texts. Retrieving specific …
-
Providing personalised information based on individual interests and preferences.
… address this problem, keyword-based search of Vector Space Model is employed as an IR technique to model the Web users and build their interest profiles. Semantic-based search through Ontology is further employed to represent documents matching the users' needs without being directly contained …
-
Usage-Driven Unified Model for User Profile and Data Source Profile Extraction
… as support of analysis to extract a profile model. The objective is to characterize the user and the data source that interact in a system to allow different types of comparison (user-to-user, sourceto- source, user-to-source). According to the study we conducted on the work done on profile …
-
Location-Based Social Media for Activity Space Modeling
Human activity research is rooted in the study of modeling the patterns of human activities in space and time. Previous studies have made prevalent progress in the theories, methods, and applications of human activity analysis. Among these studies, human activity space modeling has been a crucial …
-
KNN Optimization for Multi-Dimensional Data
… a KNN optimization algorithm which leverages vector space models to enhance the nearest neighbors search for a new sample. It accomplishes this enhancement by restricting the search area, and therefore reducing the number of comparisons necessary to find the nearest neighbors. The experimental …
-
Semantic Frameworks for Document and Ontology Clustering
… we propose a new approach to extract and build a model for citation semantics. Both subjective and objective evaluations prove the effectiveness of this model in extracting citation semantics. For the clustering stage, the Citonomy framework offers two approaches: (1) CS-VS: Combining Citation …
-
Role of semantic indexing for text classification.
The Vector Space Model (VSM) of text representation suffers a number of limitations for text classification. Firstly, the VSM is based on the Bag-Of-Words (BOW) assumption where terms from the indexing vocabulary are treated independently of one another. However, the expressiveness of natural …
-
A study of the translation of sentiment in user-generated text
… On the word-level, we propose a Transformer NMT model trained on a sentiment-oriented vector space model (VSM) of UGT data that is capable of translating the correct sentiment polarity of challenging contronyms. On the sentence-level, we propose a semi-supervised approach to overcome the problem …
-
Usefulness of social tagging in organizing and providing access to the web: An analysis of indexing consistency and quality
… and (2) employing the Information Retrieval (IR) Vector Space Model (VSM) - based indexing consistency method since it is suitable for dealing with a large number of indexers. As a second phase, an analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to …
-
Low-rank estimation and embedding learning: theory and applications
… the datasets are in high-dimensional feature space. For example, in the vector space model of text data, the feature dimension is the vocabulary size. If representing a social network using an adjacency matrix, the feature dimension corresponds to the number of objects in the network. Many …
Page 1 of 2