How are relevance scores determined for AlchemyAPI's extraction of Keywords, Concepts, Entities, etc.?
Are the scores based on frequency of occurrence, placement in the text (title, lead paragraph, etc.), or other factors?
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So I read a bit about NDCG and how it computes the retrieved relevance in information retrieval/recommender systems. All examples I read about it have specific value's for item relevance and what relevance the model predicts.
But how do recsys measure NDCG when the relevance is unknown? When we only know implicit interactions from the user, so we can only assume similar relevance to those items. Also, the model output is a ranked list of items without a relevance score to each item, do we infer the model predicted relevance as the inverse of the index in the recommendation list?
For example, if user listens to songs [1,2,3] and they got songs [5,4,3,1] recommended to them. How do you calculate the NDCG score of the recommendation list?
I'm new to elastic search. I'm having trouble understanding the calibration and scaling of boost values for fields in a document. As in how should we decide the boosting values for field so that it works as expected. I've gone through some of the online blogs and es doc as well, it's written that es does normalization and internal optimization of boosting values? How does that work?
E.g.: If we have tags, title, name and text fields in our doc, how should we decide the boosting values for these?
Elasticsearch uses a boolean model to match documents, and then a scoring model to determine relevance (i.e. ranking). The scoring model utilizes a TF/IDF score, coupled with some additional features. Those TF/IDF scores are calculated for each matching field within a query, and then aggregated to produce an overall score for a document. To dig into this process, I suggest running explain on your query to see how the score of each field is influencing the overall relevance of your document.
As the expert on your data, you're in the best position to determine which fields should most heavily influence the relevance of your document. Finding the right boost value for a field is about adjusting the levers until you find a formula that best suites your desired outcome (Also, if you have users, A/B testing can help here).
I have a set of almost 2000 texts.
My goal is to find the keywords across these texts to understand what is the subject of them, or simply the most common words and expressions.
I would like some ideias of algorithms to score the words and identify when they frequently come together.
I have read some other related questions here, but I'm trying to get more and more information about this subject. So any ideas are very welcome. Thank you so much!
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I have already extracted stopwords. After removing them I have more than 7000 words remaing; My question is how to score these words and from which point I can consider removing some them from my list of keywords. Also, how to get key expressions, find words that come together.
You may want to refer to a classical text on Information Retrieval. Most of the algorithms use a stop list to remove commonly occurring words such as "for" and "the", and then, extract the base or root word (change "seeing", "seen", "see", "sees" to the base word "see"). The remaining words form the keywords of the document and are weighted by things like term frequency (how many times the word occurs in the document) and inverse document frequency (how unique is the word in describing the content). You can use the weighted keywords as document representation and use them for retrieval.
You can use the Lucene MoreLikeThis implementation, which extracts a list of top most important keywords from a given text document. The term scoring function it uses is the tf-idf, i.e. it chooses those terms with topmost tf-idf scores, i.e. the terms which are relatively uncommon and occur frequently in the document.
If efficiency is an issue, it employs some common heuristics as follows.
Since you're trying to maximize a tf*idf score, you're probably most interested in terms with a high tf. Choosing a tf threshold even as low as two or three will radically reduce the number of terms under consideration. Another heuristic is that terms with a high idf (i.e., a low df) tend to be longer. So you could threshold the terms by the number of characters, not selecting anything less than, e.g., six or seven characters. With these sorts of heuristics you can usually find small set of, e.g., ten or fewer terms that do a pretty good job of characterizing a document.
More details can be found in this javadoc.
I have a list of keywords taken from 95 documents. I'd like to rank their importance, but I have only the number of documents in which the keywords appear and the maximum frequency of a keyword among all the documents. I'm looking for a ranking formula that could help. At the moment I'm using IDF, but I'd like to know if there is any better formula.
word frequency is already done by listing the most important words in English ( and many other langs ) by Wikitionary Frequency Lists which has many type of lists based on the most important and top words, besides the TV and Movies most frequent words and many others.
If you like to do some algorithm based on word ranking I would suggest you don't go far away from
TF-IDF
and here you can find the Latent semantic indexing algorithm which might me an asset for you.
Hope that is what you needed.
TF-IDF is definitely a good base and easy to implement.
It is also really common to add other bias such as the position of your terms inside your documents; a term occurring at the beginning of a document, or better, in its title tends to be more relevant than the ones occurring in the middle or at the end.
But you have to keep in mind that choosing an algorithm and its bias also depends on the nature of your documents. For instance, long documents (e.g. research papers or books) would need a position bias, but not necessarily news articles. Same thing for the "IDF" measure, it has to be computed on a large corpus of documents with similar type of content than your documents. You don't want to have a relevancy score computed on a "TV and Movies" corpus if for instance your documents are research papers about semi-conductors.
My two cents.
Lately I've been mucking about with text categorization and language classification based on Cavnar and Trenkle's article "N-Gram-Based Text Categorization" as well as other related sources.
For doing language classification I've found this method to be very reliable and useful. The size of the documents used to generate the N-gram frequency profiles is fairly unimportant as long as they are "long enough" since I'm just using the most common n N-grams from the documents.
On the other hand well-functioning text categorization eludes me. I've tried with both my own implementations of various variations of the algorithms at hand, with and without various tweaks such as idf weighting and other peoples' implementations. It works quite well as long as I can generate somewhat similarly-sized frequency profiles for the category reference documents but the moment they start to differ just a bit too much the whole thing falls apart and the category with the shortest profile ends up getting a disproportionate number of documents assigned to it.
Now, my question is. What is the preferred method of compensating for this effect? It's obviously happening because the algorithm assumes a maximum distance for any given N-gram that equals the length of the category frequency profile but for some reason I just can't wrap my head around how to fix it. One reason I'm interested in this fix is actually because I'm trying to automate the generation of category profiles based on documents with a known category which can vary in length (and even if they are the same length the profiles may end up being different lengths). Is there a "best practice" solution to this?
If you are still interested, and assuming I understand your question correctly, the answer to your problem would be to normalise your n-gram frequencies.
The simplest way to do this, on a per document basis, is to count the total frequency of all n-grams in your document and divide each individual n-gram frequency by that number. The result is that every n-gram frequency weighting now relates to a percentage of the total document content, regardless of the overall length.
Using these percentages in your distance metrics will discount the size of the documents and instead focus on the actual make up of their content.
It might also be worth noting that the n-gram representation only makes up a very small part of an entire categorisation solution. You might also consider using dimensional reduction, different index weighting metrics and obviously different classification algorithms.
See here for an example of n-gram use in text classification
As I know the task is to count probability of generation some text by language model M.
Recently i was working on measuring the readaiblity of texts using semantic, synctatic and lexical properties. It can be also measured by language model approach.
To answer properly you should consider these questions:
Are you using log-likelihood approach?
What levels of N-Grams are you using? unigrams digrams or higher level?
How big are language corpuses that you use?
Using only digrams and unigrams i managed to classify some documents with nice results. If your classification is weak consider creating bigger language corpuse or using n-grams of lower levels.
Also remember that classifying some text to invalid category may be an error depending on length of text (randomly there are few words appearing in another language models).
Just consider making your language corpuses bigger and know that analysing short texts have higher probability of missclasification