I have a bunch of documents such as bank statements, utilities bills, personal expenditure invoices, etc. The document types range is very broad. Some of these files are saved as pictures, others as pdfs. So far, my tactic has been to ocr all the documents, and then use some regexes to extract information (I would like to extract dates, quantities/amounts and entities). However, this hasn't worked out great so far... Thus, I was wondering what other possibilities there were in …

I want to extract data from documents (native pdf's with English language) using GPT-J but without using it's API. I have searched all documentation regarding GPT-J but haven't came across anything related to this. This article mentions that searching data is possible using GPT-J but that's all it mentions. Basically I want to extract text from documents using GPT-J without using the API. Any help/links/articles/videos would be helpful! Thanks for your time and help!

Text mining on Amazon product review using R Program. I wasn't able to extract the particular product's review(i.e.If iphone 11 has 6k review, I need to extract all of it.) I'm getting only one column labelled x. Please let me know where I need to make necessary changes. I need those for performing sentiment analysis. install.packages("rvest") library(rvest) install.packages("xml2") library(xml2) install.packages("magrittr") library(magrittr) url <-"https://www.amazon.in/Apple-iPhone-11-128GB-Black/product-reviews/B07XVLW7YK/ref=cm_cr_dp_d_show_all_btm?ie=UTF8&reviewerType=all_reviews" apple <- NULL for(i in 1:100) { murl <- read_html(as.character(paste(url,i,sep = "="))) rev <- murl%>% html_nodes(".review-text")%>% html_text() …

I'm relatively new in the field of Information Extraction and was wondering if there are any methods to summarize multiple headlines on the same topic, like some kind of "average" of headlines. Imagine like 20 headlines from news articles on the topic that the Los Angeles Rams won the Super Bowl such as "Rams win Super Bowl", "Los Angeles rallies past Bengals to win Super Bowl", ... The goal would be to find one "average" sentence that summarizes these headlines. …

I am trying to extract information from resumes. I tried the pdfminer for the text extraction. But I need to extract the contents from a resume with respect to its title. For example: I will be giving my educational details under a title EDUCATIONAL BACKGROUND, so I have to extract the content topic wise. Is it possible to extract like that? What will be the process behind that? Is it possible to approach the problem in a segmentation manner.

I have a problem to solve and was hoping you could advise/point me in the right direction. The problem is: people returning products from my niche store talk to employees via a built-in chat. They provide product details, such as brand, product ID, color, etc. Eventually employees type that data into an internal return form and push it out to processing. I was wondering if it would be possible to automate this - the manual copy-pasting/typing is pretty error prone. …

I am working on CharGrid and BERTGrid papers and have questions about bounding box regression decoder part. In the CharGrid paper, it states that there are two outputs from this branch: one with 2Na outputs and one with 4Na outputs. First one is for whether there is an object in bbox or not and the second one is for four bbox coordinates. Na is number of anchor boxes per pixel. I’ve got until this part. However, let’s say Na is …

My question is about the difference between the architectures of semantic segmentation and instance segmentation models. So, as far as I understand, a semantic segmentation model is making pixel-wise classification and, therefore, it has a dense layer at the end where the output dimension is number of labels (classes). The part that makes me confused is how instance segmentation models distinguish between the instances from same classes? How is the architecture of them? Actually, I am studying on NLP and …

I have a rather simple data scraping task, but my knowledge of web scraping is limited. I have a excel file containing the names of 500 cities in a column, and I'd like to find their distance from a fixed city, say Montreal. I have found this website which gives the desired distance (in both km and miles). For each of these 500 cities, I'd like to read the name in the excel file, enter it in the "to" box, …

How can we calculate/formulate the effectiveness of named entity linking (based on P/R/F1 or any other evaluation metrics) on a relation extraction system which accepts the output of ER as its input? Suppose we have a serial architecture in which entity recognition (NER or EL) performs before relation extraction, and RE uses the entities which are extracted by ER module. Note that, relation extraction module just detects the relationship between each pair of entities in the sentence. I found this …

I had a general doubt. If I want to extract some data, say the information regarding a particular chemical using API of material projects(a site that has open-source info on, say elements) available on the internet, and compile the information on an excel sheet, what would be the simplest way to do so? Can someone guide..it would be great, thanks!

I have seen the different options to extract words from sentences in English but when I wanted to know if its possible to do the same thing in Hindi or Marathi eg: टोमॅटो बेचना है where the word at position 1 is TOMATO and it needs to be extracted i.e. extraction of product name from a sentence. Any help will be appreciated.

I am scraping websites of organisations (mostly retailers) and I want to use NLP to extract information from the websites’ unstructured text. The first thing I want to do is to identify covid-related events in the text, for example “The shop will be closed from the 3rd of March” or “Unfortunately we have to close permanently.” The lexicon is rather limited, involving perhaps a few dozen (or hundreds at most) phrases/expressions. I am very familiar with regular expressions, and I …

I'm looking for ways to extract sentences from paragraphs of text containing different types of punctuations and all. I used SpaCy's Sentencizer to begin with. Sample input python list abstracts: ["A total of 2337 articles were found, and, according to the inclusion and exclusion criteria used, 22 articles were included in the study. Inhibitory activity against 96% (200/208) and 95% (312/328) of the pathogenic fungi tested was described for Eb and [(PhSe)2], respectively. Including in these 536 fungal isolates tested, …

I'm doing a project where I wish to create a graph visualization of free-form citations (not academic style citations) across all my e-books. E.g. David Foster Wallace's essays cite a lot of other books by different authors. For that I should be able to detect and extract book and authors names from my own e-books. I've selected some examples from my e-books that I wish my NER model would tag as "books" (in bold font): (...) or even the parodistic …

I know. The title sounds like I haven't googled my problem, but trust me, I did. Maybe my problem has a name and I haven't found it yet. Hoping you can help me wrap my head around it. What I want to do is, given a text, extract all terms from a specific domain. For simplicity let's say, given a list of hard-coded animals, I want my model to extract from an input text all of the animals that are …

Using very basic techniques (zone segmentation + OPTICS) I was able to organize a set of around 10^4 business documents (invoices, receipts) into hierarchy of clusters of documents of similar layout. Now, for each cluster, I would like to extract a template. A template consists of: labels: text boxes whose position and content (text) remains fixed inputs fields: text boxes whose position is fixed but content varies. I have already OCRed my documents. As a result for each document I …