We provide two enrich functions of a similar purpose. Both run FEL and enrich the input records with the recognized named entities. However, we found that this process in some cases takes very long (see Issues below), which can be problematic when running it on very large collections. For that reason, besides the regular
FEL we have included a second one, called
FELwithTimeOut, which runs the annotation process in a separate thread and interrupts it if it is running too long (after 10 seconds by default).
First, you will need to disseminate the model file (by default
english-nov15.hash) to the nodes of your cluster. This can be done through the
sc), e.g. from an HDFS location:
Next, create an instance of the enrich function, e.g., to run it on the text of a webpage:
val fel = FEL.on(HtmlText)
or with paramters:
val fel = FEL(scoreThreshold = -5, modelFile = "english-nov15.hash").on(HtmlText)
FELwithTimeOut can be parameterized as follows:
FEL(scoreThreshold: Double, stopwords: Set[String], modelFile: String) FELwithTimeOut(scoreThreshold: Double, stopwords: Set[String], modelFile: String, timeout: Duration)
The default values are defined in FELConstants.
Finally, you can enrich your dataset (ArchiveSpark RDD) with FEL:
val enriched = rdd.enrich(fel)
The enriched annotations are of type FELAnnotation.
Build and install
git clone https://github.com/yahoo/FEL.git cd FEL mvn install
Now, the required JAR can be created using SBT:
git clone https://github.com/helgeho/FEL4ArchiveSpark.git cd FEL4ArchiveSpark sbt assembly
The compiled JAR file under target/scala-2.11/fel4archivespark-assembly-1.0.0.jar includes all required dependencies (except for ArchiveSpark itself). To use it (as shown under Usage, see above), you will need to add it to your classpath together with ArchiveSpark. Please make sure to add this JAR before all other JARs in order to avoid conflicts.
We found that in some cases FEL takes extremely long for no obvious reason and sometimes even appears to have got stuck. One example record where we encountered this issue can be found under long-running-example.txt (in ArchiveSpark's JSON output format).
One workaround that solved this issue in most cases in our experiments was to split up texts into smaller chunks, e.g., sentences. Therefore, this enrich function splits texts on periods ('.') before applying FEL. This should not cause any problems in most cases as entity names rarely contain periods. The position of an entity is added up accordingly, so that the reported positions still relate to the full text.
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Copyright (c) 2017 Helge Holzmann
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