What you need is perhaps Named Entity Recognition with custom entity dictionary. See this example:
Many packages like NLTK or Spacy have a large dictionary of such entities that enable models to identify them without using regular expressions. However, those pre-defined entities often is not applicable to one's application, thus won't recognize what you are ...
There is at least one way:
Create/Acquire a grammar model for the language spoken (there are several such models for various languages used in NLP)
Test the transcripts for beign grammaticaly/syntacticaly correct.
This assesment will at least rule out gibberish and most of transcripts that do not correspond to valid sentences of the language spoken
If you want to be up to date with the new advancements, a good way is skimming through the accepted papers of the major NLP conferences, namely ACL, EMNLP, and the regional EACL, NAACL, AACL.
If you want even more information, you can skim through the papers uploaded to the arxiv. One way to do that is via Twitter, by following bots that tweet papers in ...
Certainly. I demonstrated clustering techniques using 52 records (playing cards and their features). Unlike classification algorithms, clustering will work with the data available.
The question to ask yourself is whether your data has a sufficient number of features that are enable records to be both clusterable and separable.
For any kind of Machine Learning task or a NLP task (which is what you are doing), you need to convert string/text values to numeric values. The machine cannot uderstand or work with string values. It only understands numeric values.
So for example if you are doing a machine learning task, you would use libraries like OneHotEncoder, LabelEncoder etc to ...
I agree that the first part is text classification. The last part looks like a Named Entity Recognition problem: detect specific types of words or sequences of words in a document among several possible categories. It would also require training a model using some annotated data.
I can only answer your first part, if you want to automatically label document if they are talking about funding opportunities, you can train a classification model to classify which document belong to your defined class and which is not. But to train such a model, you need data, and in your data you have to define and label manually documents that belong to ...
https://nlpprogress.com/ aims to provide pointers to the state of the art papers and datasets for the main NLP tasks.
It seems to be updated regularly so far. However it depends on the efforts of volunteers so there's no guarantee about completeness or future updates.
TF-IDF and Topic Modelling wouldn't be suitable as they do not take classes into account. One approach would be to train a basic classifier and extract important features per class.
Create a TF-IDF matrix for the text corpus.
Train a basic classifier using the TF-IDF Matrix as feature matrix and the classes as target. (A decent accuracy is enough....
Dense layer | Feature in-dependency
There are a couple of things that may be going on in your experiment. First of all, it may as well be that the particular keyword you are seeing to play the most important role is intrinsically correlated with a correct prediction.
That aside, by feeding the network words in the form of a dense layer, these words are ...
You can either use a sentence embedding model to associate a vector to each of your inputs, and use a clustering algorithm like KMeans, or build a similarity matrix between your strings using a string distance metric, and use a similarity-based algorithm like Spectral Clustering or Agglomerative Clustering.
The first one using KMeans might not work the best ...
Once I assume you are using text data as your input matrix X. The first point is that you have to include your preprocessing step as you would do when not using a calibrated classifier, so as you already know you can use a Pipeline like so:
calibrated_svc = CalibratedClassifierCV(linear_svc,
As far as I understand from your question, you are trying to compare sentences on word level, but it seems like you are interested in finding the number of words in sentence A that are contained in sentence B (not te intersection itself)
So you could use something very simple (as a first approach)
l1 = s1.split()
l2 = s2....
You need at least a few labelled vaccine tweets (positive, neutral, negative) to train a BERT model so that it starts to understand the domain.
For VADER you don't need any labelled data.
However, when we compare the accuracies, the BERT model always performs better.
First I think it's worth mentioning that in the context of an exploratory study with a small dataset, manual analysis is certainly as useful as applying NLP methods (if not more) since:
Small size is an advantage for manual study and a disadvantage for automatic methods.
There's no particular goal other than uncovering general patterns or insights, so it's ...
Translation as a pre-processing step is usually sufficient for many tasks (e.g. sentiment classification), but naturally undesirable for other tasks e.g. grading someone in written Dutch fluency.
Hence, for these tasks, the objective is:
Be able to train a language model for your specific language
However, you want to be able to do this with minimal ...
The NER model performance on a particular text depends on which data it was trained with originally, and naturally the standard models (like en_core_web_sm) are trained with English data which doesn't contain a lot of names from non-US/UK origin (same for other kinds of entities like organizations or locations).
Better performance can be achieved by training ...
I think the closest standard NLP task would be relationship extraction. In general it's a quite complex task which involves NER, syntactic analysis and semantic role labeling.
Note that there are various works using the term "event extraction" (for example this), but as far as I know there is no clear definition of the task. It's often related to ...
You could use clustering with a more basic similarity measure, for example cosine or even simply the proportion of words in common (e.g. Jaccard, overlap coefficient). This should gives you groups of sentences which are "quite similar" against each other, whereas sentences in different clusters are supposed to be very different. This way you would ...
This is essentially information retrieval: usually there is a collection of documents and the goal is to find the document which is the most similar to a given query (what you call the "semantic concept").
The traditional way to do that is to convert the collection of documents as vectors, typically with TFIDF weight but there are many options (I ...
This is usually done by carefully choosing two things:
The sentence representation. Word count is the most simple option but there can be many others: TFIDF weights, with/without removing stop words, with/without lemmatization, etc. In a DL approach the sentence would be represented as a sentence embedding.
The similarity measure between two sentences. ...
Rule of thumb: if a human with a lot of time wouldn't be able to do it, there's little chance a machine could. In this case if you ask somebody to classify whether a place is a park or not, they just can't do it correctly if the place name doesn't contain any indication that it is a park.
In a case like this you would need to use external resources in order ...
As mentioned in the comment you can use regex but you would need to define a set of rules for it. You can try with LexNLP which is trained on legal documents and use it to extract data type like address, companies and persons.
My first thought was that it's difficult to formally define this concept:
"b" is likely to contain "a" with some "error"
On the one hand, there is the idea that a is a substring of b. This question should have a boolean answer: either it does contain it, or it doesn't.
On the other hand there is the idea of approximate ...
I can think of two approaches.
You could use term-frequency/inverse-document-frequency (tf-idf) to cluster the vectors. Personally, I would start by first clustering the full text of the original job vacancies and then use this to assign clusters to the vectors. I have the feeling that it will outperform clustering directly the vectors.
There are ...
Yes, the parameter is available in the vanilla K-Means too.
The algorithm supports sample weights, which can be given by a parameter sample_weight. This allows assigning more weight to some samples when computing cluster centers and values of inertia. For example, assigning a weight of 2 to a sample is equivalent to adding a duplicate of that sample to the ...
If the image will always be the same and your ROI will always be in the same location, then an RNN with CV2 is not necessary, you can mark the areas that you need to extract the texts. I even recommend you more like JaidedAI
Here is the google colab of the papers you sent: Information extraction