cso-classifier | Python library that classifies content | Natural Language Processing library

 by   angelosalatino Python Version: 3.1 License: Apache-2.0

kandi X-RAY | cso-classifier Summary

kandi X-RAY | cso-classifier Summary

cso-classifier is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Natural Language Processing, Deep Learning applications. cso-classifier has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install cso-classifier' or download it from GitHub, PyPI.

The CSO Classifier is a novel application that takes as input the text from the abstract, title, and keywords of a research paper and outputs a list of relevant concepts from CSO. It consists of three main components: (i) the syntactic module, (ii) the semantic module and (iii) the post-processing module. Figure 1 depicts its architecture. The syntactic module parses the input documents and identifies CSO concepts that are explicitly referred to in the document. The semantic module uses part-of-speech tagging to identify promising terms and then exploits word embeddings to infer semantically related topics. Finally, the post-processing module combines the results of these two modules, removes outliers, and enhances them by including relevant super-areas. Figure 1: Framework of CSO Classifier.
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            kandi-support Support

              cso-classifier has a low active ecosystem.
              It has 71 star(s) with 14 fork(s). There are 7 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 5 open issues and 2 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of cso-classifier is 3.1

            kandi-Quality Quality

              cso-classifier has 0 bugs and 0 code smells.

            kandi-Security Security

              cso-classifier has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              cso-classifier code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              cso-classifier is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              cso-classifier releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 751 lines of code, 35 functions and 7 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed cso-classifier and discovered the below as its top functions. This is intended to give you an instant insight into cso-classifier implemented functionality, and help decide if they suit your requirements.
            • Run a single worker
            • Parses a paper
            • Returns a list of chunked text for the given pos_tag
            • Performs pre - processing
            • Runs the model
            • Check the version of the CSO classifier
            • Print header
            • Setup the ontology
            • Downloads the specified ontology from the server
            • Set the version of the ontology
            • Write the configuration to the config file
            • Update the ontology file
            • Print the version of the CSO Ontology
            • Setup the language model
            • Setup the model
            • Download and set up spaCy language model
            • Load the ontology from pickle file
            • Check if the ontology file is available
            • Set the ontology from the given cso
            • Read the ontology version
            • Set the paper
            • Update ontology
            • Update the cached model
            • Get the ontology graph
            • Loads the models
            • Set the classifier version
            Get all kandi verified functions for this library.

            cso-classifier Key Features

            No Key Features are available at this moment for cso-classifier.

            cso-classifier Examples and Code Snippets

            CSO-Classifier,Usage examples,Classifying a single paper (SP)
            Pythondot img1Lines of Code : 114dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            paper = {
                    "title": "De-anonymizing Social Networks",
                    "abstract": "Operators of online social networks are increasingly sharing potentially "
                        "sensitive information about users and their relationships with advertisers, appl  
            CSO-Classifier,Usage examples,Classifying in batch mode (BM)
            Pythondot img2Lines of Code : 58dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            papers = {
                "id1": {
                    "title": "De-anonymizing Social Networks",
                    "abstract": "Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, appli  
            Update
            Pythondot img3Lines of Code : 5dot img3License : Permissive (Apache-2.0)
            copy iconCopy
            import cso_classifier as cc
            cc.update()
            
            #or
            cc.update(force = True)
              

            Community Discussions

            QUESTION

            number of matches for keywords in specified categories
            Asked 2022-Apr-14 at 13:32

            For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.

            As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.

            ...

            ANSWER

            Answered 2022-Apr-14 at 13:32

            Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.

            Source https://stackoverflow.com/questions/71871613

            QUESTION

            Apple's Natural Language API returns unexpected results
            Asked 2022-Apr-01 at 20:30

            I'm trying to figure out why Apple's Natural Language API returns unexpected results.

            What am I doing wrong? Is it a grammar issue?

            I have the following four strings, and I want to extract each word's "stem form."

            ...

            ANSWER

            Answered 2022-Apr-01 at 20:30

            As for why the tagger doesn't find "accredit" from "accreditation", this is because the scheme .lemma finds the lemma of words, not actually the stems. See the difference between stem and lemma on Wikipedia.

            The stem is the part of the word that never changes even when morphologically inflected; a lemma is the base form of the word. For example, from "produced", the lemma is "produce", but the stem is "produc-". This is because there are words such as production and producing In linguistic analysis, the stem is defined more generally as the analyzed base form from which all inflected forms can be formed.

            The documentation uses the word "stem", but I do think that the lemma is what is intended here, and getting "accreditation" is the expected behaviour. See the Usage section of the Wikipedia article for "Word stem" for more info. The lemma is the dictionary form of a word, and "accreditation" has a dictionary entry, whereas something like "accredited" doesn't. Whatever you call these things, the point is that there are two distinct concepts, and the tagger gets you one of them, but you are expecting the other one.

            As for why the order of the words matters, this is because the tagger tries to analyse your words as "natural language", rather than each one individually. Naturally, word order matters. If you use .lexicalClass, you'll see that it thinks the third word in text2 is an adjective, which explains why it doesn't think its dictionary form is "accredit", because adjectives don't conjugate like that. Note that accredited is an adjective in the dictionary. So "is it a grammar issue?" Exactly.

            Source https://stackoverflow.com/questions/71711847

            QUESTION

            Tokenize text but keep compund hyphenated words together
            Asked 2022-Mar-29 at 09:16

            I am trying to clean up text using a pre-processing function. I want to remove all non-alpha characters such as punctuation and digits, but I would like to retain compound words that use a dash without splitting them (e.g. pre-tender, pre-construction).

            ...

            ANSWER

            Answered 2022-Mar-29 at 09:14

            To remove all non-alpha characters but - between letters, you can use

            Source https://stackoverflow.com/questions/71659125

            QUESTION

            Create new boolean fields based on specific bigrams appearing in a tokenized pandas dataframe
            Asked 2022-Feb-16 at 20:47

            Looping over a list of bigrams to search for, I need to create a boolean field for each bigram according to whether or not it is present in a tokenized pandas series. And I'd appreciate an upvote if you think this is a good question!

            List of bigrams:

            ...

            ANSWER

            Answered 2022-Feb-16 at 20:28

            You could use a regex and extractall:

            Source https://stackoverflow.com/questions/71147799

            QUESTION

            ModuleNotFoundError: No module named 'milvus'
            Asked 2022-Feb-15 at 19:23

            Goal: to run this Auto Labelling Notebook on AWS SageMaker Jupyter Labs.

            Kernels tried: conda_pytorch_p36, conda_python3, conda_amazonei_mxnet_p27.

            ...

            ANSWER

            Answered 2022-Feb-03 at 09:29

            I would recommend to downgrade your milvus version to a version before the 2.0 release just a week ago. Here is a discussion on that topic: https://github.com/deepset-ai/haystack/issues/2081

            Source https://stackoverflow.com/questions/70954157

            QUESTION

            Which model/technique to use for specific sentence extraction?
            Asked 2022-Feb-08 at 18:35

            I have a dataset of tens of thousands of dialogues / conversations between a customer and customer support. These dialogues, which could be forum posts, or long-winded email conversations, have been hand-annotated to highlight the sentence containing the customers problem. For example:

            Dear agent, I am writing to you because I have a very annoying problem with my washing machine. I bought it three weeks ago and was very happy with it. However, this morning the door does not lock properly. Please help

            Dear customer.... etc

            The highlighted sentence would be:

            However, this morning the door does not lock properly.

            1. What approaches can I take to model this, so that in future I can automatically extract the customers problem? The domain of the datasets are broad, but within the hardware space, so it could be appliances, gadgets, machinery etc.
            2. What is this type of problem called? I thought this might be called "intent recognition", but most guides seem to refer to multiclass classification. The sentence either is or isn't the customers problem. I considered analysing each sentence and performing binary classification, but I'd like to explore options that take into account the context of the rest of the conversation if possible.
            3. What resources are available to research how to implement this in Python (using tensorflow or pytorch)

            I found a model on HuggingFace which has been pre-trained with customer dialogues, and have read the research paper, so I was considering fine-tuning this as a starting point, but I only have experience with text (multiclass/multilabel) classification when it comes to transformers.

            ...

            ANSWER

            Answered 2022-Feb-07 at 10:21

            This type of problem where you want to extract the customer problem from the original text is called Extractive Summarization and this type of task is solved by Sequence2Sequence models.

            The main reason for this type of model being called Sequence2Sequence is because the input and the output of this model would both be text.

            I recommend you to use a transformers model called Pegasus which has been pre-trained to predict a masked text, but its main application is to be fine-tuned for text summarization (extractive or abstractive).

            This Pegasus model is listed on Transformers library, which provides you with a simple but powerful way of fine-tuning transformers with custom datasets. I think this notebook will be extremely useful as guidance and for understanding how to fine-tune this Pegasus model.

            Source https://stackoverflow.com/questions/70990722

            QUESTION

            Assigning True/False if a token is present in a data-frame
            Asked 2022-Jan-06 at 12:38

            My current data-frame is:

            ...

            ANSWER

            Answered 2022-Jan-06 at 12:13

            QUESTION

            How to calculate perplexity of a sentence using huggingface masked language models?
            Asked 2021-Dec-25 at 21:51

            I have several masked language models (mainly Bert, Roberta, Albert, Electra). I also have a dataset of sentences. How can I get the perplexity of each sentence?

            From the huggingface documentation here they mentioned that perplexity "is not well defined for masked language models like BERT", though I still see people somehow calculate it.

            For example in this SO question they calculated it using the function

            ...

            ANSWER

            Answered 2021-Dec-25 at 21:51

            There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.

            As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:

            Source https://stackoverflow.com/questions/70464428

            QUESTION

            Mapping values from a dictionary's list to a string in Python
            Asked 2021-Dec-21 at 16:45

            I am working on some sentence formation like this:

            ...

            ANSWER

            Answered 2021-Dec-12 at 17:53

            You can first replace the dictionary keys in sentence to {} so that you can easily format a string in loop. Then you can use itertools.product to create the Cartesian product of dictionary.values(), so you can simply loop over it to create your desired sentences.

            Source https://stackoverflow.com/questions/70325758

            QUESTION

            What are differences between AutoModelForSequenceClassification vs AutoModel
            Asked 2021-Dec-05 at 09:07

            We can create a model from AutoModel(TFAutoModel) function:

            ...

            ANSWER

            Answered 2021-Dec-05 at 09:07

            The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model

            Source https://stackoverflow.com/questions/69907682

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install cso-classifier

            Ensure you have Python 3.6 or above installed. Download latest version.
            Use pip to install the classifier: pip install cso-classifier
            Setting up the classifier. Go to Setup for finalising the installation.
            Ensure you have Python 3.6 or above installed. Download latest version.
            Download this repository using: git clone https://github.com/angelosalatino/cso-classifier.git
            Install the package by running the following command: pip install ./cso-classifier
            Setting up the classifier. Go to Setup for finalising the installation.
            After installing the CSO Classifier, it is important to set it up with the right dependencies. To set up the classifier, please run the following code:. This function downloads the English package of spaCy, which is equivalent to run python -m spacy download en_core_web_sm. Then, it downloads the latest version of Computer Science Ontology and the latest version of the word2vec model, which will be used across all modules.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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            pip install cso-classifier

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            gh repo clone angelosalatino/cso-classifier

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