I am working on an NLP Chatbot project. The Chatbot will need to process requests like the following:
"I want to go to Penn Station from Back Bay Station" and "I want to go from Back Bay Station to Penn Station"
In each case, I want to extract the source train station as "Back Bay Station" and the destination as "Penn Station." However, because of the sentence re-ordering, I am not sure how to do this.
Any advice, including examples, would be much appreciated.
Two ways.
Heuristics: Look for words like 'to' and 'from' and similar before the entities. You might have to spend some time creating a library of these prepositions or subordinating conjunctions but that will do the job.
Use more sophisticated deep parsers that can do this job for you. You might have to still fall back to heuristics here as well, but you can get much more information this way. I am suggesting this option because I don't know how wide your problem statement is. If it is just about 'to' and 'from' then stick to option 1
Related
I hope you can help me :).
I am working for a translation company.
As you know, every translation consists in splitting the original text into small segments and then re-joining them into the final product.
In other words, the segments are considered as "translation units".
Often, especially for large documents, the translators make some linguistic consistency errors, I try to explain it with an example.
In Spanish, you can use "tu" or "usted", depending on the context, and this determines the formality-informality tone of the sentence.
So, if you consider these two sentences of a document:
Lara, te has lavado las manos? (TU)
Lara usted se lavò las manos? (USTED)
They are BOTH correct, but if you consider the whole document, there is a linguistic inconsistency.
I am studying NLP basic in my spare time, and I am figuring out how to create a tool to perform a linguistic consistency analysis on a set of sentences.
I am looking in particular at Standford CoreNLP (I prefer Java to Python).
I guess that I need some linguistic tools to perform verb analysis first of all. And naturally, the tool would be able to work with different languages (EN, IT, ES, FR, PT).
Anyone can help me to figure out how to start this?
Any help would be appreciated,
thanks in advance!
Im not sure about Stanford CoreNLP, but if you're considering this an option, you could make your own tagger and use modifiers at pos tagging. Then, use this as a translation feature.
In other words, instead of just tagging a word to be a verb, you could tag it "a verb in the infinitive second person".
There are already good pre-tagged corpora out there for spanish that can help you do exactly that. For example, if you look at Universal Dependencies Ankora Corpus, you can find that there are annotations referring to the Person of a verb.
With a little tweaking, you could make a compose PoS that takes in "Verb-1st-Person" or something like that and train a Tagger.
I've made an article about how to do it in Python, but I bet that you can do it in Java using Weka. You can read the article here.
After this, I guess that the next step is that you ensure to match the person of one "translation unit" to the other, or make something in a pipeline fashion.
There are some event description texts.
I want to extract the entrance fee of the events.
Sometimes the entrance fee is conditional.
What I want to achieve is to extract the entrance fee and it's conditions(if available). It's fine to retrieve the whole phrase or sentence which tells the entrance fee + it's conditions.
Note I: The texts are in German language.
Note II: Often the sentences are not complete, as they are mainly event flyers or advertisements.
What would be the category of this problem in NLP? Is it Named Entity Recognition and could be solved by training an own model with Apache openNLP?
Or I thought maybe easier would be to detect the pattern via the usual keywords in the use-case(entrance, $, but, only till, [number]am/pm, ...).
Please shed some light on me.
Input Examples:
- "If you enter the club before 10pm, the entrance is for free. Afterwards it is 6$."
- "Join our party tonight at 11pm till 5am. The entrance fee is 8$. But for girls and students it's half price."
This is broadly a structure learning problem. You might have to combine Named-Entity-Recognition/Tagging with Coreference Resolution. Read some papers on these as well as related github code and take it from there. Here is good discussion of state of the art tools for these at the moment https://www.reddit.com/r/MachineLearning/comments/3dz3fl/dl_architectures_for_entity_recognition_and_other/
Hope that helps.
You might try Stanford's CoreNLP for the named entity extraction part. It should be able to help you pick out the money values, and there is also a link to models trained for German language as well (https://nlp.stanford.edu/software/CRF-NER.shtml).
Given that it's fine to extract the entire sentence that contains the information, I'd suggest taking a binary sentence classification approach. You could probably get quite far just by using ngrams and some named entity information as features. That would mean that you'd need you'd want to build a pipeline that would automatically segment your documents into sentence-like chunks. You could try a sentence segmentation tool (also provided by Stanford CoreNLP) as a first go https://stanfordnlp.github.io/CoreNLP/. Since this would form the basis for all further work, you'd want to ensure that the results are at least decent. Perhaps the structure of the document itself gives you enough information to segment it without even using a sentence segmentation tool.
After you have this pipeline in place, you'd want to annotate the sentences extracted from a large set of documents as relevant or non-relevant to make it a binary classification task. Then train a model based on that dataset. Finally, when you apply it to unseen data, first use the sentence segmentation approach, and then classify each sentence.
I have a collection of "articles", each 1 to 10 sentences long, written in a noisy, informal english (i.e. social media style).
I need to extract some information from each article, where available, like date and time. I also need to understand what the article is talking about and who is the main "actor".
Example, given the sentence: "Everybody's presence is required tomorrow morning starting from 10.30 to discuss the company's financial forecast.", I need to extract:
the date/time => "10.30 tomorrow morning".
the topic => "company's financial forecast".
the actor => "Everybody".
As far as I know, the date and time could be extracted without using NLP techniques but I haven't found anything as good as Natty (http://natty.joestelmach.com/) in Python.
My understanding on how to proceed after reading some chapters of the NLTK book and watching some videos of the NLP courses on Coursera is the following:
Use part of the data to create an annotated corpus. I can't use off-the-shelf corpus because of the informal nature of the text (e.g. spelling errors, uninformative capitalization, word abbreviations, etc...).
Manually (sigh...) annotate each article with tags from the Penn TreeBank tagset. Is there any way to automate this step and just check/fix the results ?
Train a POS tagger on the annotated article. I've found the NLTK-trainer project that seems promising (http://nltk-trainer.readthedocs.org/en/latest/train_tagger.html).
Chunking/Chinking, which means I'll have to manually annotate the corpus again (...) using the IOB notation. Unfortunately according to this bug report n-gram chunkers are broken: https://github.com/nltk/nltk/issues/367. This seems like a major issue, and makes me wonder whether I should keep using NLTK given that it's more than a year old.
At this point, if I have done everything correctly, I assume I'll find actor, topic and datetime in the chunks. Correct ?
Could I (temporarily) skip 1,2 and 3 and produce a working, but possibly with a high error rate, implementation ? Which corpus should I use ?
I was also thinking of a pre-process step to correct common spelling mistakes or shortcuts like "yess", "c u" and other abominations. Anything already existing I can take advantage of ?
THE question, in a nutshell, is: is my approach at solving this problem correct ? If not, what am I doing wrong ?
Could I (temporarily) skip 1,2 and 3 and produce a working, but
possibly with a high error rate, implementation ? Which corpus should
I use ?
I was also thinking of a pre-process step to correct common spelling
mistakes or shortcuts like "yess", "c u" and other abominations.
Anything already existing I can take advantage of ?
I would suggest you first have a go at processing standard language text. The pre-processing you refer to is an NLP task in its own right, known as normalization. Here is a resource for Twitter normalization: http://www.ark.cs.cmu.edu/TweetNLP/ , additionally, you can use spell checking, sentence boundary detection, ...
THE question, in a nutshell, is: is my approach at solving this
problem correct ? If not, what am I doing wrong ?
If you make abstraction of normalization, I think your approach is valid. With regard to automating the annotation process: you can bootstrap the process by using off-the-shelf components first, after which you correct, retrain, and so on, ... during different iterations. To get acceptable results, you will need to do your steps 2, 3, and 4 a couple of times.
If you are interested in understanding the problem and being able to optimize existing solutions, I would suggest you focus on tools that allow you to develop your own models. If you prioritize getting results over being able to develop your own models, I would recommend looking into existing open source text engineering frameworks such as Gate (https://gate.ac.uk/) UIMA (http://uima.apache.org/) and DKPro (which extends UIMA) (https://code.google.com/p/dkpro-core-asl/). All three frameworks wrap existing components, so you have a wide range of possible solutions.
I'd suggesting giving a try to NER and Temporal Normalizer.
Here is what I see for your example sentence:
You can try the demo here:
http://deagol.cs.illinois.edu:8080/
My current understanding is that it's possible to extract entities from a text document using toolkits such as OpenNLP, Stanford NLP.
However, is there a way to find relationships between these entities?
For example consider the following text :
"As some of you may know, I spent last week at CERN, the European high-energy physics laboratory where the famous Higgs boson was discovered last July. Every time I go to CERN I feel a deep sense of reverence. Apart from quick visits over the years, I was there for three months in the late 1990s as a visiting scientist, doing work on early Universe physics, trying to figure out how to connect the Universe we see today with what may have happened in its infancy."
Entities: I (author), CERN, Higgs boson
Relationships :
- I "visited" CERN
- CERN "discovered" Higgs boson
Thanks.
Yes absolutely. This is called Relation Extraction. Stanford has developed several useful tools for working on this problem.
Here is there website: http://deepdive.stanford.edu/relation_extraction
Here is the github repository: https://github.com/philipperemy/Stanford-OpenIE-Python
In general here is how the process works.
results = entract_entity_relations("Barack Obama was born in Hawaii.")
print(results)
# [['Barack Obama','was born in', 'Hawaii']]
Of some importance is that only triples are extracted of the form (subject,predicate,object).
You can extract verbs with their dependants using Stanford Parser, for example. E.g., you might get "dependency chains" like
"I :: spent :: at :: CERN".
It is a much tougher task to recognise that "I spent at CERN" and "I visited CERN" and "CERN hosted my visit" (etc) denote the same kind of event. Going into how this can be done is beyond the scope of an SO question, but you can read up literature of paraphrases recognition (here is one overview paper). There is also a related question on SO.
Once you can cluster similar chains, you'd need to find a way to label them. You could simply choose the verb of the most common chain in a cluster.
If, however, you have a pre-defined set of relation types you want to extract and lots of texts manually annotated for these relations, then the approach could be very different, e.g., using machine learning to learn how to recognize a relation type based on annotated data.
Don't know if you're still interested but CoreNLP added a new annotator called OpenIE (Open Information Extraction), which should accomplish what you're looking for. Check it out: OpenIE
Similar to the Stanford parser, you can also use the Google Language API, where you send a string and get a dependency tree response.
You can test this API first to see if it works well with your corpus: https://cloud.google.com/natural-language/
The outcome here is a subject predicate object (SPO) triplet, where your predicate describes the relationship. You'll need to traverse the dependency graph and write a script to parse out the triplet.
There are many ways to do relation extraction. As colleagues mentioned that you have to know about NER and coreference resolution. Different techniques require different approaches. Nowadays, Distant Supervision is most common, and for detecting the relation between entities, they used FREEBASE.
I'm trying to make an analysis of a set of phrases, and I don't know exactly how "natural language processing" can help me, or if someone can share his knowledge with me.
The objective is to extract streets and localizations. Often this kind of information is not presented to the reader in a structured way, and It's hard to find a way of parsing it. I have two main objectives.
First the extraction of the streets itself. As far as I know NLP libraries can help me to tokenize a phrase and perform an analysis which will get nouns (for example). But where a street begins and where does it ends?. I assume that I will need to compare that analysis with a streets database, but I don't know wich is the optimal method.
Also, I would like to deduct the level of severity , for example, in car accidents. I'm assuming that the only way is to stablish some heuristic by the present words in the phrase (for example, if deceased word appears + 100). Am I correct?
Thanks a lot as always! :)
The first part of what you want to do ("First the extraction of the streets itself. [...] But where a street begins and where does it end?") is a subfield of NLP called Named Entity Recognition. There are many libraries available which can do this. I like NLTK for Python myself. Depending on your choice I assume that a streetname database would be useful for training the recognizer, but you might be able to get reasonable results with the default corpus. Read the documentation for your NLP library for that.
The second part, recognizing accident severity, can be treated as an independent problem at first. You could take the raw words or their part of speech tags as features, and train a classifier on it (SVM, HMM, KNN, your choice). You would need a fairly large, correctly labelled training set for that; from your description I'm not certain you have that?
"I'm assuming that the only way is to stablish some heuristic by the present words in the phrase " is very vague, and could mean a lot of things. Based on the next sentence it kind of sounds like you think scanning for a predefined list of keywords is the only way to go. In that case, no, see the paragraph above.
Once you have both parts working, you can combine them and count the number of accidents and their severity per street. Using some geocoding library you could even generalize to neighborhoods or cities. Another challenge is the detection of synonyms ("Smith Str" vs "John Smith Street") and homonyms ("Smith Street" in London vs "Smith Street" in Leeds).