Vector space model ≈ Vector space model
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Combining Fact Extraction and Verification with Neural Semantic Matching Networks Open
The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recentlyreleased FEVER dataset introduced a benchmark factverification task in which a system is asked to verify a claim using evide…
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Applying Genetic Algorithms to Information Retrieval Using Vector Space Model Open
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users' needs and help them fi…
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Precise Zero-Shot Dense Retrieval without Relevance Labels Open
While dense retrieval has been shown to be effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance labels are available. In this paper, we recog…
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Dense Passage Retrieval for Open-Domain Question Answering Open
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be p…
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Toward an enhanced Arabic text classification using cosine similarity and Latent Semantic Indexing Open
The Faculty of Systems and Information Engineering of the National University of San Martin, is constantly developing projects looking for new forms of innovation in order to meet needs and improve the quality of life of the university com…
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The Implementation of Cosine Similarity to Calculate Text Relevance between Two Documents Open
Rapidly increasing number of web pages or documents leads to topic specific filtering in order to find web pages or documents efficiently. This is a preliminary research that uses cosine similarity to implement text relevance in order to f…
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College Library Personalized Recommendation System Based on Hybrid Recommendation Algorithm Open
When the number of books provided by library is relatively large, it becomes difficult for user to select appropriate book from a lot of candidate books. In this case, this paper designs a personalized recommendation system for college lib…
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Sentiment Classification Using Document Embeddings Trained with Cosine Similarity Open
In document-level sentiment classification, each document must be mapped to a fixed length vector. Document embedding models map each document to a dense, low-dimensional vector in continuous vector space. This paper proposes training docu…
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A probabilistic model of information and retrieval:
development and status Open
The paper combines a comprehensive account of the probabilistic model of retrieval with new systematic experiments on TREC Programme material. It presents the model from its foundations through its logical development to cover more aspects…
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Revisiting K-Means and Topic Modeling, a Comparison Study to Cluster Arabic Documents Open
Clustering Arabic text documents is of high importance for many natural language technologies. This paper uses a combined method to cluster Arabic text documents. Mainly, we use generative models and clustering techniques. The study uses l…
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Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2vec and WordNet Open
Words have different meanings (i.e., senses) depending on the context. Disambiguating the correct sense is important and a challenging task for natural language processing. An intuitive way is to select the highest similarity between the c…
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Neural Vector Spaces for Unsupervised Information Retrieval Open
We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we learn low-dimensional representations of words and documents …
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Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content Open
We consider the problem of learning distributed representations for documents in data streams. The documents are represented as low-dimensional vectors and are jointly learned with distributed vector representations of word tokens using a …
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Using Concept Lattice for Personalized Recommendation System Design Open
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Efficient Estimation of Nepali Word Representations in Vector Space Open
Word representation is a means of representing a word as mathematical entities that can be read, reasoned and manipulated by computational models. The representation is required for input to any new modern data models and in many cases, th…
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Automated Text Summarization for Indonesian Article Using Vector Space Model Open
In a scientific work, an abstract always contains main information of an article including at least a researched problem, aim(s), methodology, and result of the study. Writing an abstract requires a conscientious analysis since the content…
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Counter-fitting Word Vectors to Linguistic Constraints Open
In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors' capability for judging semantic similarity. Applying this method to …
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Expedient Information Retrieval System for Web Pages Using the Natural Language Modeling Open
Retrieving of information from the huge set of data flowing due to the day to day development in the technologies has become more popular as it assists in searching for the valuable information in a structured, unstructured or a semi struc…
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Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy Open
Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised score-based method which combines the vector space model, …
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The Faiss library Open
Vector databases typically manage large collections of embedding vectors. Currently, AI applications are growing rapidly, and so is the number of embeddings that need to be stored and indexed. The Faiss library is dedicated to vector simil…
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Using Vector Space Model in Question Answering System Open
Question answering system is an information retrieval system in which the expected response givesdirectly the answer as requested rather than set of references which have possibilities as the answer. The objective of this research is to re…
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Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations Open
Publication in the conference proceedings of SampTA, Bremen, Germany, 2013
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A Vector Space Approach for Aspect Based Sentiment Analysis Open
Vector representations for language has been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Aspect Based Sentime…
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Patent Analytics Based on Feature Vector Space Model: A Case of IoT Open
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. The vector space model (VSM) represents documents as high-dim…
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Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs Open
We describe a Deep Learning approach to modeling the relevance of a document’s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length differenc…
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Improved Accuracy of Sentiment Analysis Movie Review Using Support Vector Machine Based Information Gain Open
The quality of a movie can be known from the opinions or reviews of previous audiences. This classification of reviews is grouped into positive opinions and negative opinions. One of the data mining algorithms that are most frequently used…
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New Generation Model of Word Vector Representation Based on CBOW or Skip-Gram Open
Word vector representation is widely used in natural language processing tasks. Most word vectors are generated based on probability model, its bag-of-words features have two major weaknesses: they lose the ordering of the words and they a…
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On Effective Personalized Music Retrieval by Exploring Online User Behaviors Open
In this paper, we study the problem of personalized text based music retrieval which takes users' music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel Dual-Layer…
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SynTF Open
Text mining and information retrieval techniques have been developed to assist us with analyzing, organizing and retrieving documents with the help of computers. In many cases, it is desirable that the authors of such documents remain anon…
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Information Retrieval using Cosine and Jaccard Similarity Measures in Vector Space Model Open
With the exponential growth of documents available to us on the web, the requirement for an effective technique to retrieve the most relevant document matching a given search query has become critical.The field of Information Retrieval dea…