stanford-nlp

I have this parsed text in this format, I got it by using Standford nlp. (ROOT (S (NP (DT A) (NN passenger) (NN plane)) (VP (VBZ has) (VP (VBD crashed) (ADVP (RB shortly)) (PP (IN after) (NP (NP (NN take-off)) (PP (IN from) (NP (NNP Kyrgyzstan) (`` `) (NNP scapital) (, ,) (NNP Bishkek))))) (, ,) (VP (VBG killing) (NP (NP (DT a) (JJ large) (NN number)) (PP (IN of) (NP (NP (DT those)) (PP (IN on) (NP (NN board))))))))) (. .))) det(plane-3, A-1) nn(plane-3, passenger-2) nsubj(crashed-5, plane-3) aux(crashed-5, has-4) root(ROOT-0, crashed-5) advmod(crashed-5, shortly-6) prep_after(crashed-5, take

I got a problem that CoreNLP can only recognize named entity such as Kobe Bryant that is beginning with a uppercase char, but can't recognize kobe bryant as a person!!! So how to recognize a named entity that is beginning with a lowercase char by CoreNLP ???? Appreciate it !!!! First off, you do have to accept that it is harder to get named entities right in lowercase or inconsistently cased English text than in formal text, where capital letters are a great clue. (This is also one reason why Chinese NER is harder than English NER.) Nevertheless, there are things that you must do to get

I am training customized Named Entity Recognition(NER) model using stanford NLP but the thing is i want to re-train the model . Example : Suppose i trained xyz model , then i will test it on some text if model detected somethings wrong then i (end user) will correct it and wanna re-train(append mode) the model on the corrected text. Stanford Doesn't provide re-training facility so thats why i shifted towards spacy library of python , where i can retrain the model means , i can append new entities into the existing model.But after re-training the model using spacy , it overriding the existing

I cannot find the Stanford parsing models for German and French: there is no "germanPCFG.ser.gz" or "frenchFactored.ser.gz" in the jar (stanford-corenlp-3.2.0-models.jar) - only english. Have searched through posttagger jar too. Same issue encountered at : How to use Stanford CoreNLP with a Non-English parse model? You can find them in the download for the Stanford Parser . Look in the models.jar file. With Maven you can use <dependency> <groupId>edu.stanford.nlp</groupId> <artifactId>stanford-corenlp</artifactId> <version>3.5.2</version> </dependency> <dependency> <groupId>edu.stanford.nlp<

I am trying to make a tree (nested dictionary) from the output of dependency parser. The sentence is "I shot an elephant in my sleep". I am able to get the output as described on the link: How do I do dependency parsing in NLTK? nsubj(shot-2, I-1) det(elephant-4, an-3) dobj(shot-2, elephant-4) prep(shot-2, in-5) poss(sleep-7, my-6) pobj(in-5, sleep-7) To convert this list of tuples into nested dictionary, I used the following link: How to convert python list of tuples into tree? def build_tree(list_of_tuples): all_nodes = {n[2]:((n[0], n[1]),{}) for n in list_of_tuples} root = {} print all

I am trying to train a model with Stanford neural network dependency parser for English. It does not accept a standard word2vector file with 100 dimensions. It generates an error message. I am using the embedded words as defined in this Web page: [ https://drive.google.com/file/d/0B8nESzOdPhLsdWF2S1Ayb1RkTXc/view?usp=sharing][1] I have dowloaded the data as a text file in myPC. I am using the parameter -embeddingSize 100 but the parser generates an error message: Embedding File /../.../sskip.100.vectors: #Words=243004, dim=1 The dimension of embedding file does not match config.embeddingSize I

I am using the RegexNER annotator in CoreNLP and some of my named entities consist of multiple words. Excerpt from my mapping file: RAF inhibitor DRUG_CLASS Gilbert's syndrome DISEASE The first one gets detected but each word gets the annotation DRUG_CLASS and there seems to be no way to link the words, like an NER id which both words would have. The second case does not get detected at all and that's probably because the tokenizer treats the apostrophe after Gilbert as a separate token. Since RegexNER has the tokenization as a dependency, I can't really get around it. Any suggestions to