About 5 years ago, I re-trained Stanford NER and it works somewhat, but new products often get missed. At that time, I retrained the entire NER model. What I would really like to do is to fine tune the Stanford NER model. Can that be done now? Someone asked that before but the answer is not clear to me.
How to create incremental NER training model(Appending in existing model)?
Also related:
How to extract brand from product name
The most recent paper I can find is this: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.904.3818&rep=rep1&type=pdf
I tried the NER command on https://stanfordnlp.github.io/CoreNLP/caseless.html and it gave an error indicating "english-caseless-left3words-distsim.tagger" etc. could not be found. Is there a place for download trained models?
(OK. The models are included in stanford-english-corenlp-2018-02-27-models.jar)
Related
I am working on a Named Entity Recognition (NER) project in which I got a large amount of text in the sense that it is too much to read or skim read. Therefore, I want to create an overview of what is mentioned by extracting named entities (places, names, times, maybe topics) and create an index of kind (entity, list of pages/lines where it is mentioned). I have worked through Standford's NLP lecture, (parts of) Eisenstein's Introduction to NLP book found some literature and systems for English texts. As my corpus is in German, I would like to ask how I can approach this problem. Also, this is my first NLP project, so I would not know if I could solve this challenge even if texts were in English.
As a first step
are there German NER systems out there which I could use?
The further roadmap of my project is:
How can I avoid mapping misspellings or rare names to a NUL/UNK token? This is relevant because there are also some historic passages that use words no longer in use or that follow old orthography. I think the relevant terms are tokenisation or stemming.
I thought about fine-tuning or transfer learning the base NER model to a corpus of historic texts to improve NER.
A major challenge is that there is no annotated dataset for my corpus available and I could only manually annotate a tiny fraction of it. So I would be happy for hints on German annotated datasets which I could incorporate into my project.
Thank you in advance for your inputs and fruitful discussions.
Most good NLP toolkits can perform NER in German:
Stanford NLP
Spacy
probably NLTK and OpenNLP as well
What is crucial to understand is that using NER software like the above means using a pretrained model, i.e. a model which has been previously trained on some standard corpus with standard annotated entities.
Btw you can usually find the original annotated dataset by looking at the documentation. There's one NER corpus here.
This is convenient and might suit your goal, but sometimes it doesn't collect exactly every that you would like it to collect, especially if your corpus is from a very specific domain. If you need more specific NER, you must train your own model and this requires obtaining some annotated data (i.e. manually annotating or paying somebody to do it).
Even in this case, a NER model is statistical and it will unavoidably make some mistakes, don't expect perfect results.
About misspellings or rare names: a NER model doesn't care (or not too much) about the actual entity, because it's not primarily based on the words in the entity. It's based on indications in the surrounding text, for example in the sentence "It was announced by Mr XYZ that the event would take place in July", the NER model should find 'Mr XYZ' as a person due to "announced by" and 'July' as a date because of "take place in". However if the language used in the corpus is very different from the training data used for the model, the performance could be very bad.
I have a corpus with bad sentences and good ones. I need to train a RNN language model to give quality score to every sentence. I tried tensorflow ptb model. It works during training. But the project doesn't give much info about how to use the model. I ran into all sorts of problem when trying to modify it to score sentence.
Is there an example showing how to use.the model?
Also, another project IMDB comment classification caught my eyes. Is it a good idea to use that model to classify good sentences and bad ones?
I want to use NER(CRF classifier) to identify Author names in a query. I trained NER following the method given in nlp.stanford.edu site using the training file:training-data.col. And tested using the file:testing-data.tsv.
The NER is tagging every input as Author, even the data that is tagged as non-Author in the training data. Can anyone tell me why NER is tagging the non-Authors in training data as Authors and how to train NER to identify Authors(I have the list of Author names to train).
Any suggestions for reference material on NER other than nlp.stanford.edu site will be helpful.
That's a very small piece of training data, so I'm not surprised that it made the wrong inferences. Since the only example it has seen of "Atal" is as Author, it's tagging "Atal" as such.
But more so, if you want to discriminate between people listed at the beginning as Author and people listed in the text as 0, Stanford NER is not going to do that. Stanford NER is intended to make long distance inferences about the named-entity tags of tokens in natural language text. In other words, it's doing the opposite of what you're trying to do.
You could probably do this with some simple pattern recognition---if your documents are formatted in a similar way, with the authors together, I would start with exploiting that. You could use the NER to tag the authors as PERSON, and then use that tag as a feature in your own tagging.
Could I ask about Stanford NER?? Actually, I'm trying to train my own model, to use it later for learning. According to the documentation, I have to add my own features in SeqClassifierFlags and add code for each Feature in NERFeatureFactory.
My questions is that, I have my tokens with all features extracted and Last column represents the label. So, is there any way in Stanford NER to give it my Tab-Delimeted file which contains 30 columns (1 is word , 28 are featurs, and 1 is label) to train my own model without spending time for extracting features???
Of course, in Testing phase, I will give it a file like the the aforementioned file without label to predict the label.
Is this possible or Not??
Many thanks in Advance
As explained in the FAQ page, the only way to customize the NER model is by inserting the data and specifying the features that you want to extract.
But, wait ... you have the data, and you have managed to extract the features, so I think you don't need the NER model, you need a classifier. I know this answer is pretty pretty late, but maybe this classifier will be a good place to start.
So, this question might be a little naive, but I thought asking the friendly people of Stackoverflow wouldn't hurt.
My current company has been using a third party API for NLP for a while now. We basically URL encode a string and send it over, and they extract certain entities for us (we have a list of entities that we're looking for) and return a json mapping of entity : sentiment. We've recently decided to bring this project in house instead.
I've been studying NLTK, Stanford NLP and lingpipe for the past 2 days now, and can't figure out if I'm basically reinventing the wheel doing this project.
We already have massive tables containing the original unstructured text and another table containing the extracted entities from that text and their sentiment. The entities are single words. For example:
Unstructured text : Now for the bed. It wasn't the best.
Entity : Bed
Sentiment : Negative
I believe that implies we have training data (unstructured text) as well as entity and sentiments. Now how I can go about using this training data on one of the NLP frameworks and getting what we want? No clue. I've sort of got the steps, but not sure:
Tokenize sentences
Tokenize words
Find the noun in the sentence (POS tagging)
Find the sentiment of that sentence.
But that should fail for the case I mentioned above since it talks about the bed in 2 different sentences?
So the question - Does any one know what the best framework would be for accomplishing the above tasks, and any tutorials on the same (Note: I'm not asking for a solution). If you've done this stuff before, is this task too large to take on? I've looked up some commercial APIs but they're absurdly expensive to use (we're a tiny startup).
Thanks stackoverflow!
OpenNLP may also library to look at. At least they have a small tutuorial to train the name finder and to use the document categorizer to do sentiment analysis. To trtain the name finder you have to prepare training data by taging the entities in your text with SGML tags.
http://opennlp.apache.org/documentation/1.5.3/manual/opennlp.html#tools.namefind.training
NLTK provides a naive NER tagger along with resources. But It doesnt fit into all cases (including finding dates.) But NLTK allows you to modify and customize the NER Tagger according to the requirement. This link might give you some ideas with basic examples on how to customize. Also if you are comfortable with scala and functional programming this is one tool you cannot afford to miss.
Cheers...!
I have discovered spaCy lately and it's just great ! In the link you can find comparative for performance in term of speed and accuracy compared to NLTK, CoreNLP and it does really well !
Though to solve your problem task is not a matter of a framework. You can have two different system, one for NER and one for Sentiment and they can be completely independent. The hype these days is to use neural network and if you are willing too, you can train a recurrent neural network (which has showed best performance for NLP tasks) with attention mechanism to find the entity and the sentiment too.
There are great demo everywhere on the internet, the last two I have read and found interesting are [1] and [2].
Similar to Spacy, TextBlob is another fast and easy package that can accomplish many of these tasks.
I use NLTK, Spacy, and Textblob frequently. If the corpus is simple, generic, and straightforward, Spacy and Textblob work well OOTB. If the corpus is highly customized, domain-specific, messy (incorrect spelling or grammar), etc. I'll use NLTK and spend more time customizing my NLP text processing pipeline with scrubbing, lemmatizing, etc.
NLTK Tutorial: http://www.nltk.org/book/
Spacy Quickstart: https://spacy.io/usage/
Textblob Quickstart: http://textblob.readthedocs.io/en/dev/quickstart.html