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.
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.
Are there any latest pre-trained multilingual word embeddings (multiple languages are jointly mapped to a same vector space)?
I have looked at the following but they don't fit my needs:
FastText / MUSE (https://fasttext.cc/docs/en/aligned-vectors.html): this one seems too old, and the word vectors are not using subwords / wordpiece information.
LASER (https://github.com/yannvgn/laserembeddings): I'm now using this one, it's using subword information (via BPE), however, it's suggested that not to use this for word embedding because it's designed to embed sentences (https://github.com/facebookresearch/LASER/issues/69).
BERT multilingual (bert-base-multilingual-uncased in https://huggingface.co/transformers/pretrained_models.html): it's contextualised embeddings that can be used to embed sentences, and seems not good at embedding words without contexts.
Here is the problem I'm trying to solve:
I have a list of company names, which can be in any language (mainly English), and I have a list of keywords in English to measure how close a given company name is with regards to the keywords. Now I have a simple keyword matching solution, but I want to improve it using pretrained embeddings. As you can see in the following examples, there are several challenges:
keyword and brand name is not separated by space (now I'm using package "wordsegment" to split words into subwords), so embedding with subword info should help a lot
keyword list is not extensive and company name could be in different languages (that's why I want to use embedding, because "soccer" is close to "football")
Examples of company names: "cheapfootball ltd.", "wholesalefootball ltd.", "footballer ltd.", "soccershop ltd."
Examples of keywords: "football"
Check if this would do:
Multilingual BPE-based embeddings
Aligned multilingual sub-word vectors
If you're okay with whole word embeddings:
(Both of these are somewhat old, but putting it here in-case it helps someone)
Multilingual FastText
ConceptNet NumberBatch
If you're okay with contextual embeddings:
Multilingual ELMo
XLM-RoBERTa
You can even try using the (sentence-piece tokenized) non-contextual input word embeddings instead of the output contextual embeddings, of the multilingual transformer implementations like XLM-R or mBERT. (Not sure how it will perform)
I think it might be a little misleading to build a model using embedding into this application(learned by experience). Because if there are two companies, football ltd, and soccer ltd, the model might say both are a match, which might not be right.
One approach is to remove redundant words, i.e., corporation from the Facebook corporation, ltd from Facebook ltd and try matching.
Another approach is to use deepmatcher, which uses deep learning fuzzy matching based on words context.
Link
If the sentence similarity is the primary approach you want to follow STSBenchmark algorithms might be worth exploring :Link
Sent2vec link and InferSent Link uses Fasttext but seems to have good results on STSBenchmark
If anyone is still looking for some sort of multilingual word embedding.
This might help:
https://github.com/babylonhealth/fastText_multilingual
As far as I know, it doesn't support sentences, and my trials to do it weren't really successful.
I'm kinda newbie and not native english so have some trouble understanding Gensim's word2vec and doc2vec.
I think both give me some words most similar with query word I request, by most_similar()(after training).
How can tell which case I have to use word2vec or doc2vec?
Someone could explain difference in short word, please?
Thanks.
In word2vec, you train to find word vectors and then run similarity queries between words. In doc2vec, you tag your text and you also get tag vectors. For instance, you have different documents from different authors and use authors as tags on documents. Then, after doc2vec training you can use the same vector aritmetics to run similarity queries on author tags: i.e who are the most similar authors to AUTHOR_X? If two authors generally use the same words then their vector will be closer. AUTHOR_X is not a real word which is part of your corpus just something you determine. So you don't need to have it or manually insert it into your text. Gensim allows you to train doc2vec with or without word vectors (i.e. if you only care about tag similarities between each other).
Here is a good presentation on word2vec basics and how they use doc2vec in an innovative way for product recommendations (related blog post).
If you tell me about what problem you are trying to solve, may be I can suggest which method will be more appropriate.
I am using Stanford NER classification as part of a PHI De-identification process running on laboratory text notes. I am noticing that in some cases, the classification tags e.g <PERSON></PERSON> tags can find a person name, but then continue to tag much more text either side of the found name. This loss of precision means that we could potentially lose a lot of non-PHI and valuable info. Is there a way to prepare text in such a way that entities are more precisely discovered?
I have been trying to use NER feature of NLTK. I want to extract such entities from the articles. I know that it can not be perfect in doing so but I wonder if there is human intervention in between to manually tag NEs, will it improve?
If yes, is it possible with present model in NLTK to continually train the model. (Semi-Supervised Training)
The plain vanilla NER chunker provided in nltk internally uses maximum entropy chunker trained on the ACE corpus. Hence it is not possible to identify dates or time, unless you train it with your own classifier and data(which is quite a meticulous job).
You could refer this link for performing he same.
Also, there is a module called timex in nltk_contrib which might help you with your needs.
If you are interested to perform the same in Java better look into Stanford SUTime, it is a part of Stanford CoreNLP.