lib/nlp: extract NLPProcessor from the Java wrapper

Signed-off-by: Alexandre Terrasa <alexandre@moz-code.org>

diff --git a/lib/nlp/nitnlp.nit b/lib/nlp/nitnlp.nit
index 72946bc..bbf7d53 100644 (file)
--- a/lib/nlp/nitnlp.nit
+++ b/lib/nlp/nitnlp.nit
@@ -33,7 +33,7 @@ var arguments = options.rest
 # Processor initialization
 var java_cp = opt_java_cp.value
 if java_cp == null then java_cp = "*"
-var proc = new NLPProcessor(java_cp)
+var proc = new NLPJavaProcessor(java_cp)
 
 if arguments.length != 2 then
        print "Usage: nitnlp text1 text2\n"

diff --git a/lib/nlp/stanford.nit b/lib/nlp/stanford.nit
index 734a228..29058b3 100644 (file)
--- a/lib/nlp/stanford.nit
+++ b/lib/nlp/stanford.nit
@@ -20,19 +20,47 @@ module stanford
 import opts
 import dom
 
-# Wrapper around StanfordNLP jar.
+# Natural Language Processor
 #
-# NLPProcessor provides natural language processing of input text files and
-# an API to handle analysis results.
+# NLPProcessor provides natural language processing for input text and files.
+# Analyzed documents can be manipulated through the resulting NLPDocument.
+interface NLPProcessor
+
+       # Creates a new NLPDocument from a string
+       fun process(string: String): NLPDocument is abstract
+
+       # Creates a new NLPDocument from a file content
+       fun process_file(path: String): NLPDocument do
+               var content = path.to_path.read_all
+               return process(content)
+       end
+
+       # Creates a new NLPDocument from a list of files (batch mode)
+       #
+       # Returns a map of file path associated with their NLPDocument.
+       fun process_files(paths: Array[String]): Map[String, NLPDocument] do
+               var res = new HashMap[String, NLPDocument]
+               for file in paths do
+                       res[file] = process_file(file)
+               end
+               return res
+       end
+end
+
+# Wrapper around StanfordNLP jar.
 #
 # FIXME this should use the Java FFI.
-class NLPProcessor
+class NLPJavaProcessor
+       super NLPProcessor
 
        # Classpath to give to Java when loading the StanfordNLP jars.
        var java_cp: String
 
+       # Temp dir used to store batch results
+       var tmp_dir = ".nlp"
+
        # Process a string and return a new NLPDocument from this.
-       fun process(string: String): NLPDocument do
+       redef fun process(string) do
                var tmp_file = ".nlp.in"
                var file = new FileWriter.open(tmp_file)
                file.write string
@@ -43,7 +71,7 @@ class NLPProcessor
        end
 
        # Process the `input` file and return a new NLPDocument from this.
-       fun process_file(input: String): NLPDocument do
+       redef fun process_file(input) do
                # TODO opt annotators
                var tmp_file = "{input.basename}.xml"
                sys.system "java -cp \"{java_cp}\" -Xmx2g edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma -outputFormat xml -file {input}"
@@ -55,7 +83,7 @@ class NLPProcessor
        # Batch mode.
        #
        # Returns a map of file path associated with their NLPDocument.
-       fun process_files(inputs: Collection[String], output_dir: String): Map[String, NLPDocument] do
+       redef fun process_files(inputs) do
                # Prepare the input file list
                var input_file = "inputs.list"
                var fw = new FileWriter.open(input_file)
@@ -63,14 +91,15 @@ class NLPProcessor
                fw.close
 
                # Run Stanford NLP jar
-               sys.system "java -cp \"{java_cp}\" -Xmx2g edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma -outputFormat xml -filelist {input_file} -outputDirectory {output_dir}"
+               sys.system "java -cp \"{java_cp}\" -Xmx2g edu.stanford.nlp.pipeline.StanfordCoreNLP -annotators tokenize,ssplit,pos,lemma -outputFormat xml -filelist {input_file} -outputDirectory {tmp_dir}"
                # Parse output
                var map = new HashMap[String, NLPDocument]
                for input in inputs do
-                       var out_file = output_dir / "{input.basename}.xml"
+                       var out_file = tmp_dir / "{input.basename}.xml"
                        map[input] = new NLPDocument.from_xml_file(out_file)
                end
                input_file.file_delete
+               tmp_dir.rmdir
                return map
        end
 end