My work is planning on using a UIMA cluster to run documents through to extract named entities and what not. As I understand it, UIMA have very few NLP components packaged with it. I've been testing GATE for awhile now and am fairly comfortable with it. It does ok on normal text, but when we run it through some representative test data, the accuracy drops way down. The text data we have internally is sometimes all caps, sometimes all lowercase, or a mix of the two in the same document. Even using ANNIE's all caps rules, the accuracy still leaves much to be desired. I've recently heard of Stanford NLP and OpenNLP but haven't had time to extensively train and test them. How do those two compare in terms of accuracy with ANNIE? Do they work with UIMA like GATE does?
Thanks in advance.
It's not possible/reasonable to give a general estimate on performance of these systems. As you said, on your test data the accuracy declines. That's for several reasons, one is the language characteristics of your documents, another is characteristics of the annotations you are expecting to see. Afaik for every NER task there are similar but still different annotation guidelines.
Having that said, on your questions:
ANNIE is the only free open source rule-based NER system in Java I could find. It's written for news articles and I guess tuned for the MUC 6 task. It's good for proof of concepts, but getting a bit outdated. Main advantage is that you can start improving it without any knowledge in machine learning, nlp, well maybe a little java. Just study JAPE and give it a shot.
OpenNLP, Stanford NLP, etc. come by default with models for news articles and perform (just looking at results, never tested them on a big corpus) better than ANNIE. I liked the Stanford parser better than OpenNLP, again just looking at documents, mostly news articles.
Without knowing what your documents look like I really can't say much more. You should decide if your data is suitable for rules or you go the machine learning way and use OpenNLP or Stanford parser or Illinois tagger or anything. The Stanford parser seems more appropriate for just pouring your data, training and producing results, while OpenNLP seems more appropriate for trying different algorithms, playing with parameters, etc.
For your GATE over UIMA dispute, I tried both and found more viral community and better documentation for GATE. Sorry for giving personal opinions :)
Just for the record answering the UIMA angle: For both Stanford NLP and OpenNLP, there is excellent packaging as UIMA analysis engines available via the DKPro Core project.
I would like to add one more note. UIMA and GATE are two frameworks for the creation of Natural Language Processing(NLP) applications. However, Name Entity Recognition (NER) is a basic NLP component and you can find an implementation of NER, independent of UIMA and GATE. The good news is you can usually find a wrapper for a decent NER in the UIMA and GATE. To make it clear let see this example:
OpenNLP NER
A wrapper for OpenNLP NER in GATE
A wrapper for OpenNLP NER in UIMA
It is the same for the Stanford NER component.
Coming back to your question, this website lists the state of the art NERs:
http://www.aclweb.org/aclwiki/index.php?title=Named_Entity_Recognition_(State_of_the_art)
For example, in the MUC-7 competition, best participant named LTG got the result with the accuracy of 93.39%.
http://www.aclweb.org/aclwiki/index.php?title=MUC-7_(State_of_the_art)
Note that if you want to use such a state of are implementation, you may have some issue with their license.
Related
I have a straightforward task of determining the sentence structure, specifically to identify if a sentence written in plain English is in the "present simple" tense. I am aware of a couple of libraries that could help with this task:
OpenNLP
CoreNLP
However, it seems that both of these libraries use machine learning in the background and require pre-trained language models. I am looking for a more lightweight solution, possibly using a rule-based approach. Is it possible to use OpenNLP or CoreNLP without machine learning for my task?
I have looked at Stanford NER and Polyglot. Both does not support Tamil Language.
I would like to use ML along with some rule based NLP processing to do the entity recognition
Neither Stanford NER nor Polyglot are rule-based. If you are only considering rule-based systems, you should probably look for existing frameworks that process Tamil correctly, or head to generic ones (e.g. GATE).
Have a look at this paper that report existing NER systems for Tamil, you may contact authors.
If you find no system available, it should be rather easy to train one using existing datasets such as NER-FIRE2013 and NER-FIRE2014: ask organizers how it would be possible to obtain access to those datasets.
Hope this helps!
I'm having a hard time figuring out what library and datasets go together.
Toolkits / Libraries I've found:
CoreNLP - Java
NLTK - Python
OpenNLP - Java
ClearNLP - Java
Out of all of these, some are missing features. For example OpenNLP didn't have a dependency parsing.
I need to find a library that's quick that will also do dependency parsing and part of speech tagging.
The next hurdle is where do we get data sets. I've found a lot of things out there, but nothing full and comprehensive.
Data I've found:
NLTK Corpora
English Web Treebank (looks to be the best but is paid)
OpenNLP
Penn Treebank
I'm confused as to what data sets I need for what features and what's actually available publicly. From my research is seems ClearNLP will work best for but has very little data.
Thank you
Stanford CoreNLP provides both POS tagging and dependency parsing out of the box (plus many other features!), it already has trained models so you don't need any data sets for it work!
Please let me know if you have any more questions about the toolkit!
http://nlp.stanford.edu/software/corenlp.shtml
Are there any tools that analyze the meaning of given sentences? Recommendations are greatly appreciated.
Thanks in advance!
I am also looking for similar tools. One thing I found recently was this sentiment analysis tool built by researchers at Stanford.
It provides a model of analyzing the sentiment of a given sentence. It's interesting and even this seemingly simple idea is quite involved to model in an accurate way. It utilizes machine learning to develop higher accuracy as well. There is a live demo where you can input sentences to analyze.
http://nlp.stanford.edu/sentiment/
I also saw this RelEx semantic dependency relationship extractor.
http://wiki.opencog.org/w/Sentence_algorithms
Some natural language understanding tools can analyze the meaning of sentences, including NLTK and Attempto Controlled English. There are several implementations of discourse representation structures and semantic parsers with a similar purpose.
There are also several parsers that can be used to generate a meaning representation from the text that is being parsed.
I need your help in determining the best approach for analyzing industry-specific sentences (i.e. movie reviews) for "positive" vs "negative". I've seen libraries such as OpenNLP before, but it's too low-level - it just gives me the basic sentence composition; what I need is a higher-level structure:
- hopefully with wordlists
- hopefully trainable on my set of data
Thanks!
What you are looking for is commonly dubbed Sentiment Analysis. Typically, sentiment analysis is not able to handle delicate subtleties, like sarcasm or irony, but it fares pretty well if you throw a large set of data at it.
Sentiment analysis usually needs quite a bit of pre-processing. At least tokenization, sentence boundary detection and part-of-speech tagging. Sometimes, syntactic parsing can be important. Doing it properly is an entire branch of research in computational linguistics, and I wouldn't advise you with coming up with your own solution unless you take your time to study the field first.
OpenNLP has some tools to aid sentiment analysis, but if you want something more serious, you should look into the LingPipe toolkit. It has some built-in SA-functionality and a nice tutorial. And you can train it on your own set of data, but don't think that it is entirely trivial :-).
Googling for the term will probably also give you some resources to work with. If you have any more specific question, just ask, I'm watching the nlp-tag closely ;-)
Some approaches to sentiment analysis use strategies popular on other text classification tasks. The most common being transforming your film review into a word vector, and feeding it into a classifier algorithm as training data. Most popular data mining packages can help you here. You could have a look at this tutorial on sentiment classification illustrating how to do an experiment using the open source RapidMiner toolkit.
Incidentally, there is a good data set made available for research purposes related to detecting opinion on film reviews. It is based on IMDB user reviews, and you can check many related research work on the area and how they use the data set.
Its worth bearing in mind that the effectiveness of these methods can only be judged from a statistical viewpoint, so you can pretty much assume there will be misclassifications and cases where opinion is hard to detect. As already noticed in this thread, detecting things like irony and sarcasm can be very difficult indeed.