I am still very new to Retrieve and Rank, and Document Conversion services, so I have been playing around with that lately.
I encountered a problem where when I upload a large document (100+ pages) - Retrieve and Rank would help me automatically break it up into answer units, which is great and helpful.
However, some questions only require ONE small line in the big chunks of answer units, is there a way that I can manually break further down the answer units that Retrieve and Rank service has provided me?
I heard that you can do it through JavaScript, but is there a way to do it through the UI?
I am contemplating to manually break up the huge doc into multiple smaller documents, but that could potentially lead to 100s of them - which is probably the last option that I'd resort to.
Any help or suggestions is greatly appreciated!
Thank you all!
First off, one clarification:
Retrieve and Rank does not break up your documents into answer units. That is something that the Document Conversion Service does when your conversion target is ANSWER_UNITS.
Regarding your question:
I don't fully understand exactly what you're trying to do, but if the answer units that are produced by default don't meet your requirements, you can customize different steps of the conversion process to adjust the produced answer units. Take a look at the documentation here.
Specifically, you want to make sure that the heading levels (for Word, PDF or HTML, depending on your document type) are defined in a way that
they detect the start of each answer unit. Then, make sure that the heading levels that you defined (h1, h2, h3, etc.) are included in the selector_tags list within the answer_units section.
Once your custom Document Conversion Service configuration produces the answer units you are looking for, you will be ready to send them to Retrieve and Rank to be indexed.
I need to store a URL in a PostgreSQL table. What is the best datatype for a field that will hold a URL with an undetermined length?
Thanks in advance.
The answer depends on what you intend to do with the data.
If you just need to store some uris in order to print them when requested, the text datatype seems indicated. There seems to be no standard about the maximum length of an url (note that browsers have their own limits, for example at least some years ago IE was limited to 2083 characters, but this is unrelated to our problem).
If you need some advanced operations on uris (for example, computing the base uri or extract some other parts), then you may want to use some libraries designed for this purpose. One example of such library (actually I know of no alternative) is pguri.
I use Facebook and Google maps to get a full Geo Entities data values (country, city, street, zip...).
I store these values on my mongoDB,
I noticed that some locations are deffer in the way they were written on Face and on Google, for (an unreal) example Face wrote the name of 'Hawaii' with an 'e' - Haweii.
I use match_all fields (country + city + street...) to search for entities at the same location but since some are written a bit different i will not find them.
Is there a way make elasticsearch search for 'Hawaii' and any other option that sounds like Hawaii but written a bit different?
Thanks for any help!
Using Google API one can get a location's
full details
To match words that sound similar you can use the phonetic analyzer. You can also give fuzzy query a try to match words with spelling mistakes. None of them are fool proof though and may result in false positives. Guess you'll have to experiment a little to come up with a solution that best fits your need.
If you have a known set of differences between Facebook and Google maps, you could look at using Synonyms at either index time or query time to accommodate differences in the APIs; There are merits to taking either approach.
I'm trying to figure out what's the best solution to find all nodes of certain types around a given GPS-Location.
Let's say I want to get all cafes, pubs, restaurant and parks around a given point X.xx,Y.yy.
[out:json];(node[amenity][leisure](around:500,52.2740711,10.5222147););out;
This returns nothing because I think it searches for nodes that are both, amenity and leisure which is not possible.
[out:json];(node[amenity or leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity,leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity;leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity|leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity]|[leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity],[leisure](around:500,52.2740711,10.5222147););out;
[out:json];(node[amenity];[leisure](around:500,52.2740711,10.5222147););out;
These solutions result in an error (400: Bad Request)
The only working solution I found is the following one which results in really long queries
[out:json];(node[amenity=cafe](around:500,52.2740711,10.5222147);node[leisure=park](around:500,52.2740711,10.5222147);node[amenity=pub](around:500,52.2740711,10.5222147);node[amenity=restaurant](around:500,52.2740711,10.5222147););out;
Isn't there an easier solution without multiple "around" statements?
EDIT:
Found This on which is a little bit shorter. But still multiple "around" statements.
[out:json];(node["leisure"~"park"](around:400,52.2784715,10.5249662);node["amenity"~"cafe|pub|restaurant"](around:400,52.2784715,10.5249662););out;
What you're probably looking for is regular expression support for keys (not only values).
Here's an example based on your query above:
[out:json];
node[~"^(amenity|leisure)$"~"."](around:500,52.2740711,10.5222147);
out;
NB: Since version 0.7.54 (released in Q1/2017) Overpass API also supports filter criteria with 'or' conditions. See this example on how to use this new (if: ) filter.
I am working with some really large databases of newspaper articles, I have them in a MySQL database, and I can query them all.
I am now searching for ways to help me tag these articles with somewhat descriptive tags.
All these articles is accessible from a URL that looks like this:
http://web.site/CATEGORY/this-is-the-title-slug
So at least I can use the category to figure what type of content that we are working with. However, I also want to tag based on the article-text.
My initial approach was doing this:
Get all articles
Get all words, remove all punctuation, split by space, and count them by occurrence
Analyze them, and filter common non-descriptive words out like "them", "I", "this", "these", "their" etc.
When all the common words was filtered out, the only thing left is words that is tag-worthy.
But this turned out to be a rather manual task, and not a very pretty or helpful approach.
This also suffered from the problem of words or names that are split by space, for example if 1.000 articles contains the name "John Doe", and 1.000 articles contains the name of "John Hanson", I would only get the word "John" out of it, not his first name, and last name.
Automatically tagging articles is really a research problem and you can spend a lot of time re-inventing the wheel when others have already done much of the work. I'd advise using one of the existing natural language processing toolkits like NLTK.
To get started, I would suggest looking at implementing a proper Tokeniser (much better than splitting by whitespace), and then take a look at Chunking and Stemming algorithms.
You might also want to count frequencies for n-grams, i.e. a sequences of words, instead of individual words. This would take care of "words split by a space". Toolkits like NLTK have functions in-built for this.
Finally, as you iteratively improve your algorithm, you might want to train on a random subset of the database and then try how the algorithm tags the remaining set of articles to see how well it works.
You should use a metric such as tf-idf to get the tags out:
Count the frequency of each term per document. This is the term frequency, tf(t, D). The more often a term occurs in the document D, the more important it is for D.
Count, per term, the number of documents the term appears in. This is the document frequency, df(t). The higher df, the less the term discriminates among your documents and the less interesting it is.
Divide tf by the log of df: tfidf(t, D) = tf(t, D) / log(df(D) + 1).
For each document, declare the top k terms by their tf-idf score to be the tags for that document.
Various implementations of tf-idf are available; for Java and .NET, there's Lucene, for Python there's scikits.learn.
If you want to do better than this, use language models. That requires some knowledge of probability theory.
Take a look at Kea. It's an open source tool for extracting keyphrases from text documents.
Your problem has also been discussed many times at http://metaoptimize.com/qa:
http://metaoptimize.com/qa/questions/1527/what-are-some-good-toolkits-to-get-lda-like-tagging-of-my-documents
http://metaoptimize.com/qa/questions/1060/tag-analysis-for-document-recommendation
If I understand your question correctly, you'd like to group the articles into similarity classes. For example, you might assign article 1 to 'Sports', article 2 to 'Politics', and so on. Or if your classes are much finer-grained, the same articles might be assigned to 'Dallas Mavericks' and 'GOP Presidential Race'.
This falls under the general category of 'clustering' algorithms. There are many possible choices of such algorithms, but this is an active area of research (meaning it is not a solved problem, and thus none of the algorithms are likely to perform quite as well as you'd like).
I'd recommend you look at Latent Direchlet Allocation (http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) or 'LDA'. I don't have personal experience with any of the LDA implementations available, so I can't recommend a specific system (perhaps others more knowledgeable than I might be able to recommend a user-friendly implementation).
You might also consider the agglomerative clustering implementations available in LingPipe (see http://alias-i.com/lingpipe/demos/tutorial/cluster/read-me.html), although I suspect an LDA implementation might prove somewhat more reliable.
A couple questions to consider while you're looking at clustering systems:
Do you want to allow fractional class membership - e.g. consider an article discussing the economic outlook and its potential effect on the presidential race; can that document belong partly to the 'economy' cluster and partly to the 'election' cluster? Some clustering algorithms allow partial class assignment and some do not
Do you want to create a set of classes manually (i.e., list out 'economy', 'sports', ...), or do you prefer to learn the set of classes from the data? Manual class labels may require more supervision (manual intervention), but if you choose to learn from the data, the 'labels' will likely not be meaningful to a human (e.g., class 1, class 2, etc.), and even the contents of the classes may not be terribly informative. That is, the learning algorithm will find similarities and cluster documents it considers similar, but the resulting clusters may not match your idea of what a 'good' class should contain.
Your approach seems sensible and there are two ways you can improve the tagging.
Use a known list of keywords/phrases for your tagging and if the count of the instances of this word/phrase is greater than a threshold (probably based on the length of the article) then include the tag.
Use a part of speech tagging algorithm to help reduce the article into a sensible set of phrases and use a sensible method to extract tags out of this. Once you have the articles reduced using such an algorithm, you would be able to identify some good candidate words/phrases to use in your keyword/phrase list for method 1.
If the content is an image or video, please check out the following blog article:
http://scottge.net/2015/06/30/automatic-image-and-video-tagging/
There are basically two approaches to automatically extract keywords from images and videos.
Multiple Instance Learning (MIL)
Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), and the variants
In the above blog article, I list the latest research papers to illustrate the solutions. Some of them even include demo site and source code.
If the content is a large text document, please check out this blog article:
Best Key Phrase Extraction APIs in the Market
http://scottge.net/2015/06/13/best-key-phrase-extraction-apis-in-the-market/
Thanks, Scott
Assuming you have pre-defined set of tags, you can use the Elasticsearch Percolator API like this answer suggests:
Elasticsearch - use a "tags" index to discover all tags in a given string
Are you talking about the name-entity recognition ? if so, Anupam Jain is right. it;s research problem with using deep learning & CRF. In 2017, the name-entity recognition problem is force on semi-surprise learning technology.
The below link is related ner of paper:
http://ai2-website.s3.amazonaws.com/publications/semi-supervised-sequence.pdf
Also, The below link is key-phase extraction on twitter:
http://jkx.fudan.edu.cn/~qzhang/paper/keyphrase.emnlp2016.pdf