Using TF-IDF to Determine Word Relevance in Document Queries

By J. E. Ramos. 2003

2,614 citations recorded by the source corpus.

Subfields: Information Retrieval & Search

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Citations in the corpus

Generating Wikipedia by Summarizing Long Sequences (2018, methodology): “We compute tf-idf (Ramos et al., 2003) for the query, with respect to the documents, { p ij } .”
Entity-Based Knowledge Conflicts in Question Answering (2021, methodology): “In some experiments we also use a sparse retriever, TF-IDF (Ramos, 1999; Manning et al., 2008).”
BillSum: A Corpus for Automatic Summarization of US Legislation (2019, methodology): “Following a large percentage of previous works, we capture this property using TF-IDF (Seki, 2002; Ramos et al., 2003).”
The Summary Loop: Learning to Write Abstractive Summaries Without Examples (2020, methodology): “We use a tf-idf-based approach to decide on the set of masked keywords, as it is both simple and has been shown to represent word relevance to a document (Ramos, 2003).”
Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents (2018, result): “Due to this limitation, when TFIDF algorithm is applied, sometimes it gives some unexpected results [7].”
Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media (2020, methodology): “, TF-IDF [35], word-embedding [36]) is provided as training to the model in order to generalize the weight of”
Neuron clustering for mitigating catastrophic forgetting in feedforward neural networks (2014, methodology): “We chose to use the TF-IDF method to extract 2000 features [16].”
Voice of airline passenger: A text mining approach to understand customer satisfaction (2019, background): “In other words, this calculation demonstrates the relevancy of a given word in a specific document (Ramos, 2003).”
Crowd sensing of traffic anomalies based on human mobility and social media (2013, methodology): “Once we have both TH and TC , we analyze the relevance of each term among them using the strategy similar with tf-idf in [7].”
Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Systems (2015, methodology): “We currently make use of the term frequency-inverse document frequency (TF-IDF) criteria [13] for identifying which knowledge source to use.”
Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering (2017, methodology): “The hidden units weight matrix of the GRU are initialized using orthogonal weights (Saxe, McClelland, and Ganguli 2013), while input embedding weight matrix is initialized using a pretrained embedding vector, the Glove (Pennington, Socher, and Manning 2014), with 300 dimension.”
Machine Learning-Based Sentiment Analysis for Twitter Accounts (2018, methodology): “Term frequency–inverse document frequency (TF–IDF) [19] was applied for the purpose of ﬁnding strongly related words for relevant documents.”
Neuron Clustering for Mitigating Catastrophic Forgetting in Supervised and Reinforcement Learning (2015, methodology): “We chose to use the TF-IDF method to extract 2000 features [67].”
A Supervised Approach to Extractive Summarisation of Scientific Papers (2017, background): “TF-IDF Term Frequency, Inverse Document Frequency (TF-IDF) is a measure of how relevant a word is to a document (Ramos et al., 2003).”
An open source machine learning framework for efficient and transparent systematic reviews (2020, methodology): “Feature extraction techniques available are Doc2Vec, 33 embedding with IDF or TF-IDF 34 (the default is unigram, with the option to run n-grams, while other parameters are set to the defaults of Scikit-learn 35 ), and sBERT.”