Named-entity recognition also known as Named-entity extraction is a task that seeks to classify elements in the text into categories. There are various tools that cope with that task, and a quick search for entity extraction tools returns quite a few, as well this discussion. Such extraction techniques and tools are beyond the scope of this post though. Let's assume that we have a simple Annotator interface, which can either accept or reject tokens, with two implementations: How could we use this annotator to tag our text, such that the index captured the tagged data, and allowed us to find the document by searching for color: As mentioned above, when we add text fields to documents, they are processed by the Analyzer that was configured for the indexer.
Lucene's Analyzer produces a TokenStream which processes the text and produces index tokens. That token stream comprises a Tokenizer and TokenFilter.
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The former is responsible for breaking the input text into tokens and the latter is responsible for processing them. Lucene offers a great variety of filters out of the box. Some drop tokens e.
Since we are not after breaking the input text into words, let's write an AnnotatorTokenFilter which emits only words that are accepted by an annotator. Fortunately, Lucene provides a base class for filters that keep only some of the input tokens, called FilteringTokenFilter. By extending it, and using our Annotator interface, we come up with the following implementation:. It really can't get any simpler than that. Now we just need to build an Analyzer which uses our filter and we can move on to indexing and searching colors:.
To index the color annotations, we are going to index two fields: The "text" field will index the original text's tokens while the "color" field will index only the ones that are also colors. Lucene provides PerFieldAnalyzerWrapper which can return a different analyzer per field. It's quite handy and easy to construct:. As you can see, the "text" field contains all words from the text that we indexed, while the "color" field contains only the words that were identified as colors by our ColorAnnotator.
We can now also search for color: If we also print the full indexed information about the "color" terms, we will notice that they retain their original text position:. This is important since it provides us a direct back-reference to the input text. That way we can search for "foxes that are brown-colored", while if we omit the exact position information of 'brown', we may not be able to associate the token 'fox' with the color 'brown'.
Let's demonstrate that capability using Lucene's a SpanQuery:. In this post I've demonstrated a very basic approach to index and search tagged data with Lucene. As I noted above, the input data is added to a separate field for every annotator that you will apply to it. This may be OK in case you index short texts and only apply few annotators.
However, if you index large data, and especially since most likely your analysis chain comprises something more complex than just whitespace tokenization, there will be performance implications to this approach. In a follow-up post I will demonstrate how to process the input data only once, by using another of Lucene's cool TokenStream implementations, TeeSinkTokenFilter. Terms for field [color], with positional info: Searching for [spanNear [mask color: Before it does so though, it captures the state of the token stream, so that whatever attributes it changes can later be restored.
Posted by Shai Erera at 3: Let's just do that: Posted by Shai Erera at Indexing Tagged Data with Lucene part 2. You can chain additional TokenFilter s for additional analysis lowercasing, stopwrods removal , and wrap the full chain with TeeSinkTokenFilter. Notice that we create a separate sink for "colors" and "animals".
Posted by Shai Erera at 5: The following code snippet demonstrates how easy it is: This will use the default Analyzer that was configured for IndexWriter to extract and process the field's terms. Note, you rarely want to commit after every indexed document, and in many cases it is preferred to use Lucene's near-realtime search.
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Usually you will use a QueryParser to parse a query such as text: TopDocs holds the list of matching documents; in the example above only one document. You can learn more about Lucene's analysis chain here. Note that we don't need to explicitly populate it, as it's being populated by the downstream tokenizer and additional filters. If our annotator accepts the current token, we retain it on the stream. Otherwise, it will not be indexed.
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Lucene also provides a KeepWordFilter which takes a set of words to keep. We could use it by passing a list of words for each category, however in reality a simple list of words may not be enough to extract entities from the text e. Notice that there is no point storing it for this field too. Terms for field [text]: Terms for field [color], with additional info: Sunday, April 27, Expressions with Lucene.
Lucene's expressions module allows computing values for documents using arbitrary mathematical expressions. Among other things, expressions allow a very easy and powerful way to e. The module parses String expressions in JavaScript notation, and returns an object which computes a value for each requested document. Expressions are built of literals , variables and functions.
The JavaScript parser recognizes a handful of useful functions , such as max , min , sqrt etc. The javadocs contain the full list of functions as well as operators that the parser recognizes. The variables are resolved by binding their name to a ValueSource. For example, in order to parse and execute the above expression, you need to write code similar to this: SimpleBindings let you bind a variable to a SortField , however internally it is bound to a ValueSource. Expression itself returns a ValueSource , and when your application asks for the value of a document, it computes it based on the formula and the bounded variables' ValueSource s.
Customizing expressions JavascriptCompiler lets you pass a mapping of custom functions, where each function is implemented by a public and static Method , which takes up to double parameters and returns a double.
You can see a good example here. That, together with variables , provides great customization capabilities of expressions.
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Sometimes customizing expressions is not so straightforward. For example, someone recently asked on the Lucene user-list how to use a multi-valued field in an expression , for the purpose of computing different functions on it max , sum etc. At first, it looks like a custom function, e.
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However, since data is a variable, and variables are bound to ValueSource s which return a single value for a document , we cannot pass all values of data to maxAll. We can implement a ValueSource though, which returns the maximum value of the field data , and bind a variable max. Since this isn't a true function , we cannot use a more natural notation like max data.
Perhaps one day Lucene will have built-in support for multi-valued numeric fields, and the expressions module will auto-detect such fields and pass all values to the function you're welcome to contribute patches! Until then though, you need to implement a ValueSource for each such function, but fortunately it is quite trivial. I wrote some prototype code below which demonstrates how to do that.
The following code indexes five documents with a binary doc-values field which encodes some integers using variable-length coding: Its getValues method looks like this: In my previous post I described how updatable DocValues are implemented, and concluded with a brief discussion about alternative approaches we could take to implement them.
I created a benchmark, leveraging our benchmarking framework , to measure the indexing and searching effects of updatable DocValues. I also set Linux's swappiness to 0. I indexed two Wikipedia datasets: StandardAnalyzer no stopwords ID postings: Lucene41 the current default DocValues: Additionally, each document is indexed several fields such as body , title etc. The benchmark all sources available here performs the following operations: Threads record the time to execute the update: The thread records the time it took for each reopen as well as whether the index was in fact reopened, and whether the thread fell behind i.
Searches While the index is being updated and reopened, search threads execute search tasks against it, and sort the results by the last-modification-time numeric doc-values field to exercise the updated doc-values. Parameters There are different costs to re-indexing a document and updating a doc-values field.