I have been doing some capability testing with Apache OpenNLP, Which has the capability to Sentence detection, Tokenization, Name entity recognition. Now when i started looking at UIMA documents it is mentioned on the UIMA home page - "language identification" => "language specific segmentation" => "sentence boundary detection" => "entity detection (person/place names etc.)".
Which says that i can use UIMA to do the same task as done by OpenNLP. What added feature both have ? I am new to this area, Please help me to understand the uses and capability perspective of both.
As I understand the question, you are asking for the differences between the feature sets of Apache UIMA and Apache OpenNLP. Their feature sets barely have anything in common as these two projects have very different aims.
Apache UIMA is an open source implementation of the UIMA specification. The latter defines a conceptual framework for augmenting unstructured information (such as natural language produced by humans) with structured metadata so that computers can work with it.
As an example for an application working with unstructured information, let us take a an application that takes natural language text as input and marks all named entities in the given text, e.g.
Input text = "Bob's cat Charlie is chasing a mouse."
Result = "<NE>Bob</NE>'s cat <NE>Charlie</NE> is chasig a mouse."
To identify the named entities in this example (i.e. Bob and Charlie), several steps of natural language processing have to be performed. Without going into detail about what each of the steps does, a hypothetical system for named entity recognition might involve the following steps:
Data preparation
Sentence splitting
Tokenization
Token lemmatization
Part-of-speech tagging
Phrase detection
Classifying phrases as named entities or not
As you can see, such applications can be very intuitively modelled as sequences of components, and this is exactly what UIMA does. It models applications dealing with unstructed information as pipelines of components (called analytics in UIMA parlance). As you can imagine, many of the pipeline components listed above can be used for other tasks and so the architecture design of UIMA emphasizes reusability of components.
To avoid confusion, the UIMA standard itself doesn't provide any specific components, but defines an infrastructure for UIM (Unstructured Information Management) applications, e.g. workflows, data types, inter-component communication, and so on.
Apache OpenNLP on the other hand does exactly that, namely provide concrete implementations of NLP algorithms dealing with very specific tasks (sentence splitting, POS-tagging, etc.). The source of your confusion might be that it is possible to write Apache UIMA components that wrap OpenNLP tools. The OpenNLP project actually provides such components.
Whether you want to use the UIMA framework for your UIM applications depends on the size of the project. If it is small, I would go without UIMA and just use OpenNLP directly, as UIMA is rather heavy-weight and thus only adds complex yet (for small applications) unnecessary overhead. Also, due to its complexity, it takes a good amount of time to learn how to use it.
Summing up, Apache UIMA and Apache OpenNLP solve different problems, but since both deal with unstructured information, they can be combined profitably.
Related
I have a corpus of a few 100-thousand legal documents (mostly from the European Union) – laws, commentary, court documents etc. I am trying to algorithmically make some sense of them.
I have modeled the known relationships (temporal, this-changes-that, etc). But on the single-document level, I wish I had better tools to allow fast comprehension. I am open for ideas, but here's a more specific question:
For example: are there NLP methods to determine the relevant/controversial parts of documents as opposed to boilerplate? The recently leaked TTIP papers are thousands of pages with data tables, but one sentence somewhere in there may destroy an industry.
I played around with google's new Parsey McParface, and other NLP solutions in the past, but while they work impressively well, I am not sure how good they are at isolating meaning.
In order to make sense out of documents you need to perform some sort of semantic analysis. You have two main possibilities with their exemples:
Use Frame Semantics:
http://www.cs.cmu.edu/~ark/SEMAFOR/
Use Semantic Role Labeling (SRL):
http://cogcomp.org/page/demo_view/srl
Once you are able to extract information from the documents then you may apply some post-processing to determine which information is relevant. Finding which information is relevant is task related and I don't think you can find a generic tool that extracts "the relevant" information.
I see you have an interesting usecase. You've also mentioned the presence of a corpus(which a really good plus). Let me relate a solution that I had sketched for extracting the crux from research papers.
To make sense out of documents, you need triggers to tell(or train) the computer to look for these "triggers". You can approach this using a supervised learning algorithm with a simple implementation of a text classification problem at the most basic level. But this would need prior work, help from domain experts initially for discerning "triggers" from the textual data. There are tools to extract gists of sentences - for example, take noun phrases in a sentence, assign weights based on co-occurences and represent them as vectors. This is your training data.
This can be a really good start to incorporating NLP into your domain.
Don't use triggers. What you need is a word sense disambiguation and domain adaptation. You want to make sense of is in the documents i.e understand the semantics to figure out the meaning. You can build a legal ontology of terms in skos or json-ld format represent it ontologically in a knowledge graph and use it with dependency parsing like tensorflow/parseymcparseface. Or, you can stream your documents in using a kappa based architecture - something like kafka-flink-elasticsearch with added intermediate NLP layers using CoreNLP/Tensorflow/UIMA, cache your indexing setup between flink and elasticsearch using redis to speed up the process. To understand relevancy you can apply specific cases from boosting in your search. Furthermore, apply sentiment analysis to work out intents and truthness. Your use case is one of an information extraction, summarization, and semantic web/linked data. As EU has a different legal system you would need to generalize first on what is really a legal document then narrow it down to specific legal concepts as they relate to a topic or region. You could also use here topic modelling techniques from LDA or Word2Vec/Sense2Vec. Also, Lemon might also help from converting lexical to semantics and semantics to lexical i.e NLP->ontology ->ontology->NLP. Essentially, feed the clustering into your classification of a named entity recognition. You can also use the clustering to assist you in building out the ontology or seeing what word vectors are in a document or set of documents using cosine similarity. But, in order to do all that it be best to visualize the word sparsity of your documents. Something like commonsense reasoning + deep learning might help in your case as well.
In UIMA, the CAS (common annotating structure) plays a major role in structuring an NLP application. It allows to pass the metadata that one components adds into the next compoment. For example, sentence boundaries from a sentence tokenizer can be added to the CAS and used by the subsequent word tokenizer.
What is the equivalent data structure in NLTK?
In short, there is no equivalent concept to the CAS (Common Analysis System) in NLTK. The latter uses much simpler means of representing texts than does UIMA. In NLTK, texts are simply lists of words, whereas in UIMA you have very complex (and heavy-weight) data structures defined as part of the CAS for the purpose of describing the input data and its flow through a UIMA system.
That being said, I view the two of them to serve quite different purposes anyway. If I was to name a Java equivalent for NLTK, I would choose the OpenNLP toolkit rather than UIMA. The former offers a number of algorithms for NLP based on machine learning (as does NLTK, among other things), while the latter is a component-based framework not only for NLP, but unstructured data in general. That is, it defines a general model for building applications working with unstructured data.
I am interested in the filed on Computational Linguistics and NLP. I read a lot about Grammatical Framework (GF), which is divided into abstract syntax and concrete syntax. And I know a little bit about OWL, RDF and WordNet. I am confused about the differences between the 2 technologies.
Can we use GF rather than OWL as syntax builders?
Can we eliminate Parser by using GF?
Does GF contains all terms so we don't need to use WordNet?
One of the formal definitions of Grammatical Framework is:
Grammatical Framework (GF), grammaticalframework.org, is a multilingual grammar formalism based on the idea of a shared abstract syntax and mappings between the abstract syntax and concrete languages. GF has hundreds of users all over the world.
The way GF is connected to the Semantic Web is through lemon:
Lemon is a proposed model for modeling lexicon and machine-readable dictionaries and linked to the Semantic Web and the Linked Data cloud.It was designed to meet the following challenges:
RDF-native form to enable leverage of existing Semantic Web technologies (SPARQL, OWL, RIF etc.).
Linguistically sound structure based on LMF to enable conversion to existing offline formats.
Separation of the lexicon and ontology layers, to ensure compatability with existing OWL models.
Linking to data categories, in order to allow for arbitrarily complex linguistic description.
So to answer your first question, GF and OWL complement each other. GF is essentially a set of grammatical rules that can be mapped between languages, but depending on the task at hand, you can use GF to develop powerful Semantic Web tools. For example, GF can be used to verbalise ontologies, as it has been demonstrated in lemon papers.
For the second question, yes. Since the intermediate level of GF is a set of logical rules, you don't need a parser anymore. The morphology and basic syntax mapping can be enough (again, what is your goal? As the definition says, GF covers basic syntax.)
As for WordNet:
WordNet® is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations.
WordNet can be perceived as an ontology, but it is not. It cannot even be called a linguistic ontology. Having hypernym and hyponym relations does not make a dataset into an ontology.
What lemon or ontolex are trying to achieve is to create an ontology that can be used for linguistic purposes. This purpose could be annotation, corpus study, modelling dictionaries, and etc. However, the power of WordNet lies within its synsets (Words from the same lexical category that are roughly synonymous are grouped into synsets.); but the power of RDF/OWL lies within inference.
In the 4 years since this question was first asked, there have been some updates in GF. Most importantly, we now have a WordNet ported into GF, currently for 13 languages, with full inflection tables. You can find the repository in https://github.com/GrammaticalFramework/gf-wordnet#readme and a multilingual web interface in http://www.grammaticalframework.org/~krasimir/gf-wordnet.html. Some examples how to use the interface:
English inflection table:
Finnish inflection table:
I was checking out Stanford CoreNLP in order to understand NER and POS tagging. But what if I want to create custom tags for entities like<title>Nights</title>, <genre>Jazz</genre>, <year>1992</year> How can I do it? is CoreNLP useful in this case?
CoreNLP out-of-the-box will be restricted to types they mention : PERSON, LOCATION, ORGANIZATION, MISC, DATE, TIME, MONEY, NUMBER. No, you won't be able to recognize other entities just by assuming it could "intuitively" do it :)
In practice, you'll have to choose, either:
Find another NER systems that tags those types
Address this tagging task using knowledge-based / unsupervised approaches.
Search for extra resources (corpora) that contain types you want recognize, and re-train a supervised NER system (CoreNLP or other)
Build (and possibly annotate) your own resources - then you'll have to define an annotation scheme, rules, etc. - quite an interesting part of the work!
Indeed, unless you find an existing system that fulfills your needs, some effort will be required! Unsupervised approaches may help you bootstrapping a system, so as to see if you need to find / annotate a dedicated corpus. In the latter case, it would be better to separate data as train/dev/test parts, so as to be able to assess how much the resulting system performs on unseen data.
Look into this FAQ (http://nlp.stanford.edu/software/crf-faq.shtml) to use CRF classifier to train your model for new classes. You may find it useful.
I'm building a small prototype of a Movies semantic search engine based on the data of LinkedIMDB
I've defined some Query Types as an example of use cases
search by entity name search by
entity type
search common features between two entities ...etc
So far I've developed a SPARQL engine that takes any type of those Queries and send the Query to the endpoint and preview the result.
The problem here is that I want to make a natural language or semi natural language interface for it in order for users to invoke those sentences using Natural language search Queries. But I don't know from where to start.
I've found some papers that are trying to extract triplets from the text but I don't feel that's the key to the solution.
Also I've found some LSA techniques to interpret Natural language search Queries but I feel it's not applicable to semantic search domain.
Any idea or resources to start reading from?
Is there a best practice than the natural language interface?
A lot of work has been done in the field of natural languge -> SQL conversion. Maybe you should take that as a starting point and see how you can modify the available examples for SPARQL. (Also, designing a controlled natural language could make your task easier.)
Another path to explore can be this article: Supporting Domain Experts to Construct Conceptual Ontologies: A Holistic Approach published at the Journal of Web Semantics, http://www.websemanticsjournal.org/index.php/ps/article/view/189 Even though it is about using natural language for ontology construction, the approach explained there (along with open source code) can turn into a fruitful exploration.
Have you seen FREya # https://github.com/nmvijay/freya it is an NLP to SPARQL convertor.
FREyA is an interactive Natural Language Interface for querying ontologies which combines usability enhancement methods such as feedback and clarification dialogs in order to:
1) improve recall by generating the dialog and enriching the domain lexicon from the user's vocabulary, whenever an "unknown" term appears in a question
2) improve precision by resolving ambiguities more effectively through the dialog. The suggestions shown to the user are found through ontology reasoning and are initially ranked using the combination of string similarity and synonym detection. The system then learns from the user's selections, and improves its performance over time.