I would like to classify texts without using any ML model. My idea is to find a list of keywords that I would assign to each class. Then when I need to classify a new text, I can compare it with my list of keywords and count how many keywords for each class are in the text; the class with the most corresponding keywords would be my final prediction. Example of classification for this list of keywords: green : A …

I would like to export tables for the following result for a repeated measure anova: Here the function which ANOVA test has been implemented fAddANOVA = function(data) data %>% ezANOVA(dv = .(value), wid = .(ID), within = .(COND)) %>% as_tibble() And here the commands to explore ANOVA statistics aov_stats <- df_join %>% group_by(signals) %>% mutate(ANOVA = map(data, ~fAddANOVA(.x))) %>% dplyr::select(., -data) %>% unnest(ANOVA) > aov_stats # A tibble: 12 x 4 # Groups: signals [12] signals ANOVA$Effect $DFn $DFd $F …

I want to create a ML model which would give a score from 0 to 1 which would signify the relation between them. I know about Relationship Extraction(RE) but that's more related with sentences based relation. Instead i want to input two words and that should output the relation between them and a input dataset being a lot of sentences.

I am trying to find a dataset in which dataset targets words that are difficult. I understand there would be different levels of difficulty for each individual , but if we considered an average individual, I want to detect the difficult words that would be present in a sentence. Example: Yes, may be today's Britains are not responsible for some of these reparations but the same speakers have pointed with pride to their foreign aid - you are not responsible …

https://www.google.com/search?sxsrf=ALeKk01_SgA8G4UfNm4rOqku4yJBFvKhLw%3A1600154854621&source=hp&ei=5mxgX8ztI6KZ4-EPq-mL8Ak&q=homophones+example&oq=Homophones&gs_lcp=ChFtb2JpbGUtZ3dzLXdpei1ocBABGAEyBQgAELEDMgUIABCxAzICCAAyCAgAELEDEIMBMgUIABCxAzICCAAyAggAMgUIABCxAzoHCCMQ6gIQJzoECCMQJzoFCAAQkQI6CAguELEDEIMBOgUILhCxA1DkKliKSGDuUGgBcAB4AIAB6wGIAe8NkgEFMC44LjKYAQCgAQGwAQ8&sclient=mobile-gws-wiz-hp Are there Machine learning algorithms for forming Homophones from input dataset word? Homophones examples : accessary, accessory. ad, add. air, heir. all, awl. allowed, aloud. alms, arms. Input : ad Output : ad, add Are there Machine learning algorithms for forming Homophones from input dataset word taking Indian regional languages viz Hindi, Gujarati, Bengali etc and other languages viz French, German, Italian, Spanish, Dutch etc?

https://www.google.com/search?q=jumbled+words&oq=jumbled&aqs=chrome.1.69i57j0l4.3399j0j9&client=ms-android-lava&sourceid=chrome-mobile&ie=UTF-8 Can Machine learning algorithms solve the input dataset of jumbled words and form the correct words from them?

Is there a tool to computationally extract the editing history of a given Microsoft Word Document? I have been using Apache Tika, but can only extract the last version of the text, and meta-information about the initial and last edit – even if edits are still being tracked via Word.

I am looking for the way to get the similarity between two item names using integer encoding or one-hot encoding. For example, "lane connector" vs. "a truck crane". I have 100,000 item names consisting of 2~3 words as above. also, items have its size(36mm, 12M, 2400*1200...) and unit(ea, m2, m3, hr...) I wanna make (item name, size, unit) as a vector. To do this, I need to change texts to numbers using some way. All I found is only word2vec …

I want to replace the values in a data set (sample in the picture) using numbers instead of words, e.g., 1 instead of D, -1 instead of R, 0 for all other values. How can I do it with a loop? I know it can be done doing this instead: (suppose d is index name) d[d$Response == "R",]$Response = -1 d[d$Response == "D",]$Response = 1 ... (other values code it and assign value of) = 0

Currently i'm working on a sentiment analysis research project using LSTM networks. As the input I convert sentences into set of vectors using word2vec. And there are some well pretrained word vectors like Google word2vec. My problem is, is there are any advantages of using custom trained word2vecs(train using a dataset which related to our domain, such as user reviews of electronic items) over pretrained ones. Whats the best option use a pretrained word2vec Train our own word2vec using a …

I need to build semantic word embeddings representation of compound terms like "electronic engineer" or "microsoft excel". One approach would be to use a standard pretrained model an average the words but, since I have a corpus of my domain, is there a possible better approach? To be more precise: The data I have is a corpus of millions of documents. Each document is ~ half a page and contains these compound terms. However there may be compound terms not …