quootstrap | Unsupervised method for extracting quotation | Natural Language Processing library

 by   epfl-dlab Java Version: v1.3 License: No License

kandi X-RAY | quootstrap Summary

kandi X-RAY | quootstrap Summary

quootstrap is a Java library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning applications. quootstrap has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

This is the reference implementation of Quootstrap, as described in the paper [PDF]:. Dario Pavllo, Tiziano Piccardi, Robert West. Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping. In Proceedings of the 12th International Conference on Web and Social Media (ICWSM), 2018. We propose Quootstrap, a method for extracting quotations, as well as the names of the speakers who uttered them, from large news corpora. Whereas prior work has addressed this problem primarily with supervised machine learning, our approach follows a bootstrapping paradigm and is therefore fully unsupervised. It leverages the redundancy present in large news corpora, more precisely, the fact that the same quotation often appears across multiple news articles in slightly different contexts. Starting from a few seed patterns, such as ["Q", said S.], our method extracts a set of quotation-speaker pairs (Q,S), which are then used for discovering new patterns expressing the same quotations; the process is then repeated with the larger pattern set. Our algorithm is highly scalable, which we demonstrate by running it on the large ICWSM 2011 Spinn3r corpus. Validating our results against a crowdsourced ground truth, we obtain 90% precision at 40% recall using a single seed pattern, with significantly higher recall values for more frequently reported (and thus likely more interesting) quotations. Finally, we showcase the usefulness of our algorithm's output for computational social science by analyzing the sentiment expressed in our extracted quotations.
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            kandi-support Support

              quootstrap has a low active ecosystem.
              It has 19 star(s) with 2 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 0 have been closed. On average issues are closed in 673 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of quootstrap is v1.3

            kandi-Quality Quality

              quootstrap has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              quootstrap does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              quootstrap releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed quootstrap and discovered the below as its top functions. This is intended to give you an instant insight into quootstrap implemented functionality, and help decide if they suit your requirements.
            • Entry point for testing purposes
            • Performs a test
            • Exports the results to a JavaPairRDD
            • Extract a pattern from a sentence
            • Match a sentence
            • Match tokens
            • Perform match
            • Matches a given sentence
            • Matches a list of tokens
            • Loads all the articles from a given file
            • Returns a hash code for this object
            • Reads the next tar archive entry
            • Finds longest superstrings of needle in haystack
            • Compares two sentences
            • Performs a multi match
            • Compares two patterns
            • Get the next token
            • Inserts a substring into the current tree
            • Exports the graph into a DOT format
            • Insert a pattern
            • Returns true if the sentence matches
            • Sanity check
            • Converts this token into a human readable string
            • Post - processes a sentence
            • Command - line entry point
            • Load a Parquet Parquet dataset
            Get all kandi verified functions for this library.

            quootstrap Key Features

            No Key Features are available at this moment for quootstrap.

            quootstrap Examples and Code Snippets

            No Code Snippets are available at this moment for quootstrap.

            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 quootstrap

            To run our code, you need:. The Spinn3r dataset must be converted to JSON format using the tool that we provide. You can find more details in the README in src\main\java\ch\epfl\dlab\spinn3r\converter. The reason for this requirement is that our format is more suitable for distributed processing, whereas the original dataset is stored in large non-splittable archives. For reference, this is the command that we have used. It creates an output file dataset.json, stored on HDFS and split into 500 gzip-compressed chunks. Only news articles are kept, and duplicates are removed.
            Java 8
            Spark >= 2 (tested on 2.3.1). For Spark 1.6, we have another branch.
            The ICWSM 2011 Spinn3r dataset
            Our dataset of people extracted from Freebase. You can download it from: https://drive.google.com/file/d/1fj4LxOE5T9WlNfW2tYycKi7ZYaJaOnXW/view
            Clone the repository and import it as an Eclipse project. All dependencies are downloaded through Maven. To build the application, generate a .jar file with all source files and run it as explained in the previous section. Alternatively, you can use Spark in local mode for experimenting. Additional instructions on how to extend the project with new functionalities (e.g. support for new datasets) are reported later.

            Support

            If you want to add support for other datasets/formats, you can provide a concrete implementation for the Java interface DatasetLoader and specify its full class name in the NEWS_DATASET_LOADER field of the configuration. For each article, you must supply a unique ID (int64/long), the website in which it can be found, and its content in tokenized format, i.e. as a list of strings. We provide an implementation for our JSON Spinn3r dataset in ch.epfl.dlab.quootstrap.Spinn3rDatasetLoader, and for parquet dataframes in ch.epfl.dlab.quootstrap.ParquetDatasetLoader.
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