Word2Vec has its own coherence score based on cosine similarity applied both between words that are inside the same topic (it measure the intra-topic similarity and how coherent a topic is) and across two different topics (it measure the inter-topic similarity and how separate two topics are).
May 19, 2022
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Mar 18, 2024 · We can use the coherence score in topic modeling to measure how interpretable the topics are to humans. In this case, topics are represented as the top N words.
To do this, we will implement a simple version of the TC-W2V coherence measure. We use the Word2vec model to calculate coherence scores for each of these models ...
Aug 10, 2024 · Typically, CoherenceModel used for evaluation of topic models. The four stage pipeline is basically: Segmentation. Probability Estimation.
Sep 26, 2020 · The "c_v" coherence method drags a sliding window across all documents in the corpus to accumulate co-occurrence statistics. Similarity is calculated using ...
Aug 6, 2021 · I am trying to compare different models using their coherence score. I have tried using Gensim's coherence score but failed. I got an error message indicating ...
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Oct 6, 2015 · It reports significant improvements on topic coherence, document clustering and document classification tasks, especially on small corpora or ...
A fully-automatic rich statistical model of local and global coherence that uses word2vec approach to assess the coherence a document and achieves ...
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Jan 12, 2023 · In cᵥ coherence, each topic word is compared with the set of all topics. A boolean sliding window of size 110 is used to assess whether two ...
Aug 10, 2024 · This module implements the word2vec family of algorithms, using highly optimized C routines, data streaming and Pythonic interfaces.