关系抽取页面展示修改

nlp-web/src/main/java/org/diagbot/nlp/controller/RelationExtractionController.java

@@ -1,8 +1,8 @@
 package org.diagbot.nlp.controller;
 
 import org.diagbot.nlp.feature.FeatureType;
-import org.diagbot.nlp.relation.RelationAnalyze;
-import org.diagbot.nlp.relation.extract.output.OutputInfo;
+import org.diagbot.nlp.relation.analyze.RelationAnalyze;
+import org.diagbot.nlp.relation.util.OutputInfo;
 import org.diagbot.pub.api.Response;
 import org.springframework.stereotype.Controller;
 import org.springframework.web.bind.annotation.RequestMapping;

nlp/src/main/java/org/diagbot/nlp/participle/ParticipleToken.java

@@ -135,12 +135,20 @@ public class ParticipleToken {
 
     private void matchARABIC() throws IOException {
         int position;
+        int cur_offset = this.offset;
         while (cursor < available) {
             position = Arrays.binarySearch(join_symbols, buffer[cursor]);
             if (CharacterUtil.identifyCharType(buffer[cursor]) == CharacterUtil.CHAR_ARABIC        //数字后跟'-'、'/'、'.'作为数字处理
 //                    || CharacterUtil.identifyCharType(buffer[cursor]) == CharacterUtil.CHAR_ENGLISH
                     || position > -1) {
-                cursor++;
+//                cursor++;
+                cur_offset++;
+                //先依据词库是否成词判断
+                this.matchCHN(segment, cursor, 0, false);
+                if (cursor - cur_offset > 1) {
+                    this.cursor = cur_offset;
+                    break;
+                }
             } else if (CharacterUtil.identifyCharType(buffer[cursor]) == CharacterUtil.CHAR_CHINESE) {    //数字后跟中文单位
                 break;
             } else {

nlp/src/main/java/org/diagbot/nlp/participle/ParticipleUtil.java

@@ -8,6 +8,7 @@ import org.diagbot.nlp.participle.word.Segment;
 import org.diagbot.nlp.util.Constants;
 import org.diagbot.nlp.util.NlpUtil;
 import org.diagbot.nlp.util.NlpCache;
+import org.springframework.util.StringUtils;
 
 import java.io.IOException;
 import java.io.StringReader;
@@ -142,7 +143,7 @@ public class ParticipleUtil {
         if (index > 0) {
             index--;
             last_l = lexemes.get(index);
-            if ("×".equals(last_l.getText()) && index > 0) {
+            if (("×".equals(last_l.getText()) || StringUtils.isEmpty(last_l.getText())) && index > 0) {
                 lexeme.setOffset(last_l.getOffset());
                 lexeme.setLength(last_l.getLength() + lexeme.getLength());
                 lexeme.setText(last_l.getText() + lexeme.getText());

nlp/src/main/java/org/diagbot/nlp/relation/analyze/RelationAnalyze.java

@@ -0,0 +1,74 @@
+package org.diagbot.nlp.relation.analyze;
+
+import org.algorithm.core.cnn.AlgorithmCNNExecutor;
+import org.algorithm.core.cnn.entity.Lemma;
+import org.algorithm.core.cnn.entity.Triad;
+import org.algorithm.core.cnn.model.impl.RelationExtractionModelImpl;
+import org.diagbot.nlp.feature.FeatureType;
+import org.diagbot.nlp.participle.ParticipleUtil;
+import org.diagbot.nlp.participle.word.Lexeme;
+import org.diagbot.nlp.participle.word.LexemePath;
+import org.diagbot.nlp.relation.extract.PresentExtract;
+import org.diagbot.nlp.relation.extract.VitalExtract;
+import org.diagbot.nlp.relation.util.OutputInfo;
+import org.diagbot.nlp.relation.util.LemmaUtil;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * @ClassName org.diagbot.nlp.relation.RelationAnalyze
+ * @Description 关系抽取入口
+ * @Author fyeman
+ * @Date 2019/1/18/018 14:36
+ * @Version 1.0
+ **/
+public class RelationAnalyze {
+    public List<OutputInfo> analyze(String content, FeatureType featureType) throws Exception {
+        String[] part_contents = content.split("\\。|\\；\\\r|\\\n|\\;");
+
+        List<OutputInfo> outputInfos = new ArrayList<>();
+
+        LemmaUtil lemmaUtil = new LemmaUtil();
+        for (String part_content : part_contents) {
+            LexemePath<Lexeme> lexemes = ParticipleUtil.participle(part_content, true);
+            //分词结果转词元结构，只提取有词性信息，因为部分特征信息未在三元组中，所以需要把分词结果也一并传入
+            List<Lemma> lemmaParticiple = lemmaUtil.lexemeToTriadLemma(lexemes);
+            //调用CNN模型
+            AlgorithmCNNExecutor executor = new RelationExtractionModelImpl();
+            List<Triad> triads = executor.execute(part_content, lemmaParticiple);
+            //模型返回的三元组转树形结构
+            List<Lemma> lemmaTree = lemmaUtil.traidToTree(triads, featureType);
+
+            OutputInfo outputInfo = new OutputInfo();
+            switch (featureType) {
+                case SYMPTOM:
+                    this.lemmaPresentExtract(outputInfo, lemmaTree, lemmaParticiple);
+            }
+            if (outputInfo != null) {
+                outputInfos.add(outputInfo);
+            }
+        }
+        return outputInfos;
+    }
+
+    public OutputInfo lemmaPresentExtract(OutputInfo outputInfo, List<Lemma> lemmaTree, List<Lemma> lemmaParticiple) {
+        //现病史症状信息提取
+        PresentExtract presentExtract = new PresentExtract();
+        outputInfo = presentExtract.extract(outputInfo, lemmaTree, lemmaParticiple);
+        return outputInfo;
+    }
+
+    /**
+     * 体征特征提取
+     * @param outputInfo
+     * @param lemmaTree
+     * @param lemmaParticiple
+     * @return
+     */
+    public OutputInfo lemmaVitalExtract(OutputInfo outputInfo, List<Lemma> lemmaTree, List<Lemma> lemmaParticiple) {
+        VitalExtract vitalExtract = new VitalExtract();
+        outputInfo = vitalExtract.extract(outputInfo, lemmaTree, lemmaParticiple);
+        return outputInfo;
+    }
+}

nlp/src/main/java/org/diagbot/nlp/relation/extract/PresentExtract.java

@@ -1,12 +1,13 @@
 package org.diagbot.nlp.relation.extract;
 
 import org.algorithm.core.cnn.entity.Lemma;
-import org.diagbot.nlp.relation.extract.cell.*;
-import org.diagbot.nlp.relation.extract.module.Symptom;
-import org.diagbot.nlp.relation.extract.output.OutputInfo;
+import org.diagbot.nlp.relation.module.Symptom;
+import org.diagbot.nlp.relation.util.OutputInfo;
+import org.diagbot.nlp.relation.module.cell.*;
 import org.diagbot.nlp.util.Constants;
 import org.diagbot.nlp.util.NlpUtil;
 
+import java.util.ArrayList;
 import java.util.List;
 
 /**
@@ -18,12 +19,18 @@ import java.util.List;
  **/
 public class PresentExtract extends BaseExtract {
     public OutputInfo extract(OutputInfo outputInfo, List<Lemma> lemmaTree, List<Lemma> lemmaParticiple) {
+        List<String> repeatSymptomsList = new ArrayList<>();
         for (int index = 0; index < lemmaParticiple.size(); index++) {
             Lemma participle_lemma = lemmaParticiple.get(index);
             String property = participle_lemma.getProperty();
             if (NlpUtil.isFeature(property, Constants.symptom_type)) {          //特征词 症状
+                if (repeatSymptomsList.contains(participle_lemma.getText())) {
+                    continue;
+                }
                 Symptom symptom = lookSymptomRelations(participle_lemma, lemmaTree);
                 outputInfo.getSymptoms().add(symptom);
+
+                repeatSymptomsList.add(participle_lemma.getText());
             }
         }
         lemmaTree.removeAll(containsLemmaTree);

nlp/src/main/java/org/diagbot/nlp/relation/extract/VitalExtract.java

@@ -1,9 +1,9 @@
 package org.diagbot.nlp.relation.extract;
 
 import org.algorithm.core.cnn.entity.Lemma;
-import org.diagbot.nlp.relation.extract.cell.*;
-import org.diagbot.nlp.relation.extract.module.Vital;
-import org.diagbot.nlp.relation.extract.output.OutputInfo;
+import org.diagbot.nlp.relation.module.Vital;
+import org.diagbot.nlp.relation.util.OutputInfo;
+import org.diagbot.nlp.relation.module.cell.*;
 import org.diagbot.nlp.util.Constants;
 import org.diagbot.nlp.util.NlpUtil;
 

nlp/src/main/java/org/diagbot/nlp/relation/extract/module/Symptom.java

@@ -1,6 +1,6 @@
-package org.diagbot.nlp.relation.extract.module;
+package org.diagbot.nlp.relation.module;
 
-import org.diagbot.nlp.relation.extract.cell.*;
+import org.diagbot.nlp.relation.module.cell.*;
 
 /**
  * @ClassName org.diagbot.nlp.relation.extract.cell.Symptom
@@ -13,7 +13,7 @@ public class Symptom {
     private String symptomName;
     private Negative negative;
 
-    private BodyPart  bodyPart;
+    private BodyPart bodyPart;
     private Degree degree;
     private Cause cause;
     private Property property;

nlp/src/main/java/org/diagbot/nlp/relation/extract/module/Vital.java

@@ -1,9 +1,9 @@
-package org.diagbot.nlp.relation.extract.module;
+package org.diagbot.nlp.relation.module;
 
-import org.diagbot.nlp.relation.extract.cell.BodyPart;
-import org.diagbot.nlp.relation.extract.cell.Degree;
-import org.diagbot.nlp.relation.extract.cell.Item;
-import org.diagbot.nlp.relation.extract.cell.Negative;
+import org.diagbot.nlp.relation.module.cell.BodyPart;
+import org.diagbot.nlp.relation.module.cell.Degree;
+import org.diagbot.nlp.relation.module.cell.Item;
+import org.diagbot.nlp.relation.module.cell.Negative;
 
 /**
  * @ClassName org.diagbot.nlp.relation.extract.module.Vital

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/BodyPart.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 public class BodyPart {
     private String partBodyName;

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Cause.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 public class Cause {
     private String causeName;

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Degree.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 public class Degree {
     private String degreeName;

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Desc.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 /**
  * @ClassName org.diagbot.nlp.relation.extract.cell.Desc

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Factor.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 /**
  * @ClassName org.diagbot.nlp.relation.extract.cell.Factor

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Frequency.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 public class Frequency {
     private String freName;

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Item.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 /**
  * @ClassName org.diagbot.nlp.relation.extract.cell.Item

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Negative.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 /**
  * @ClassName org.diagbot.nlp.relation.extract.cell.Negative

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/PD.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 public class PD {
     private String value;

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Position.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 /**
  * @ClassName org.diagbot.nlp.relation.extract.cell.Position

nlp/src/main/java/org/diagbot/nlp/relation/extract/cell/Property.java

@@ -1,4 +1,4 @@
-package org.diagbot.nlp.relation.extract.cell;
+package org.diagbot.nlp.relation.module.cell;
 
 public class Property {
     private String propertyName;

nlp/src/main/java/org/diagbot/nlp/relation/RelationAnalyze.java

@@ -1,16 +1,10 @@
-package org.diagbot.nlp.relation;
+package org.diagbot.nlp.relation.util;
 
-import org.algorithm.core.cnn.AlgorithmCNNExecutor;
 import org.algorithm.core.cnn.entity.Lemma;
 import org.algorithm.core.cnn.entity.Triad;
-import org.algorithm.core.cnn.model.impl.RelationExtractionModelImpl;
 import org.diagbot.nlp.feature.FeatureType;
-import org.diagbot.nlp.participle.ParticipleUtil;
 import org.diagbot.nlp.participle.word.Lexeme;
 import org.diagbot.nlp.participle.word.LexemePath;
-import org.diagbot.nlp.relation.extract.PresentExtract;
-import org.diagbot.nlp.relation.extract.VitalExtract;
-import org.diagbot.nlp.relation.extract.output.OutputInfo;
 import org.diagbot.nlp.util.Constants;
 import org.diagbot.nlp.util.NegativeEnum;
 import org.diagbot.nlp.util.NlpUtil;
@@ -19,48 +13,19 @@ import java.util.ArrayList;
 import java.util.List;
 
 /**
- * @ClassName org.diagbot.nlp.relation.RelationAnalyze
- * @Description 关系抽取入口
+ * @ClassName org.diagbot.nlp.relation.util.LemmaUtil
+ * @Description TODO
  * @Author fyeman
- * @Date 2019/1/18/018 14:36
+ * @Date 2019/3/21/021 14:25
  * @Version 1.0
  **/
-public class RelationAnalyze {
-    public List<OutputInfo> analyze(String content, FeatureType featureType) throws Exception {
-        String[] part_contents = content.split("\\。|\\；\\\r|\\\n|\\;");
-
-        List<OutputInfo> outputInfos = new ArrayList<>();
-        for (String part_content : part_contents) {
-            LexemePath<Lexeme> lexemes = ParticipleUtil.participle(part_content, true);
-            //分词结果转词元结构，只提取有词性信息，因为部分特征信息未在三元组中，所以需要把分词结果也一并传入
-            List<Lemma> lemmaParticiple = lexemeToTriadLemma(lexemes);
-            //调用CNN模型
-            AlgorithmCNNExecutor executor = new RelationExtractionModelImpl();
-            List<Triad> triads = executor.execute(part_content, lemmaParticiple);
-            //模型返回的三元组转树形结构
-            List<Lemma> lemmaTree = traidToTree(triads, featureType);
-            OutputInfo outputInfo = new OutputInfo();
-            switch (featureType) {
-                case SYMPTOM:
-                    PresentExtract presentExtract = new PresentExtract();
-                    outputInfo = presentExtract.extract(outputInfo, lemmaTree, lemmaParticiple);
-                    //如果还有体征信息 也需要抽取
-                    VitalExtract vitalExtract = new VitalExtract();
-                    outputInfo = vitalExtract.extract(outputInfo, lemmaTree, lemmaParticiple);
-            }
-            if (outputInfo != null) {
-                outputInfos.add(outputInfo);
-            }
-        }
-        return outputInfos;
-    }
-
+public class LemmaUtil {
     /**
      * 分词结果转词元结构
      * @param lexemes
      * @return
      */
-    private List<Lemma> lexemeToTriadLemma(LexemePath<Lexeme> lexemes) {
+    public List<Lemma> lexemeToTriadLemma(LexemePath<Lexeme> lexemes) {
         List<Lemma> lemmas = new ArrayList<>();
         for (Lexeme lexeme : lexemes) {
             if (!lexeme.getProperty().equals("99")) {
@@ -81,7 +46,7 @@ public class RelationAnalyze {
      * @param featureType
      * @return
      */
-    private List<Lemma> traidToTree(List<Triad> triads, FeatureType featureType) {
+    public List<Lemma> traidToTree(List<Triad> triads, FeatureType featureType) {
         List<Lemma> lemmaTree = new ArrayList<>();
         switch (featureType) {
             case SYMPTOM:
@@ -91,32 +56,32 @@ public class RelationAnalyze {
         return lemmaTree;
     }
 
-    private void select(List<Lemma> lemmaTree, List<Triad> triads, NegativeEnum[] type) {
+    public void select(List<Lemma> lemmaTree, List<Triad> triads, NegativeEnum[] type) {
         List<Triad> has_add_triads = new ArrayList<>();
         for (Triad triad : triads) {
-            Lemma origin_l = null;
-            Lemma relation_l = null;
+            Lemma left_l = null;
+            Lemma right_l = null;
             if (NlpUtil.isFeature(triad.getL_1().getProperty(), type)) {
-                origin_l = this.copyProperties(triad.getL_1());
-                relation_l = this.copyProperties(triad.getL_2());
+                left_l = this.copyProperties(triad.getL_1());
+                right_l = this.copyProperties(triad.getL_2());
             }
             if (NlpUtil.isFeature(triad.getL_2().getProperty(), type)) {
-                origin_l = this.copyProperties(triad.getL_2());
-                relation_l = this.copyProperties(triad.getL_1());
+                left_l = this.copyProperties(triad.getL_2());
+                right_l = this.copyProperties(triad.getL_1());
             }
-            if (origin_l != null && relation_l != null) {
+            if (left_l != null && right_l != null) {
                 boolean isFindFromTree = false;
                 for (Lemma l : lemmaTree) {
-                    if (l.getText().equals(origin_l.getText()) && l.getPosition().equals(origin_l.getPosition())) {
-                        origin_l = l;
+                    if (l.getText().equals(left_l.getText()) && l.getPosition().equals(left_l.getPosition())) {
+                        left_l = l;
                         isFindFromTree = true;
                         break;
                     }
                 }
-                this.findRelationTriad(triads, triad, has_add_triads, relation_l, type);
-                origin_l.add(relation_l);
+                this.findRelationTriad(triads, triad, has_add_triads, right_l, type);
+                left_l.add(right_l);
                 if (!isFindFromTree) {      //树上已有节点 不需要添加
-                    lemmaTree.add(origin_l);
+                    lemmaTree.add(left_l);
                 }
                 //已添加到树中的三元组
                 has_add_triads.add(triad);
@@ -125,7 +90,7 @@ public class RelationAnalyze {
         triads.removeAll(has_add_triads);
     }
 
-    private boolean findRelationTriad(List<Triad> triads, Triad current_triad, List<Triad> has_add_triads, Lemma lemma, NegativeEnum[] type) {
+    public boolean findRelationTriad(List<Triad> triads, Triad current_triad, List<Triad> has_add_triads, Lemma lemma, NegativeEnum[] type) {
         boolean isFindRelation = false;
         for (Triad triad : triads) {
             if (current_triad == triad) {
@@ -151,7 +116,7 @@ public class RelationAnalyze {
         return isFindRelation;
     }
 
-    private Lemma copyProperties(Lemma lemma) {
+    public Lemma copyProperties(Lemma lemma) {
         Lemma l = new Lemma();
         l.setLen(lemma.getLen());
         l.setPosition(lemma.getPosition());

nlp/src/main/java/org/diagbot/nlp/relation/extract/output/OutputInfo.java

@@ -1,7 +1,7 @@
-package org.diagbot.nlp.relation.extract.output;
+package org.diagbot.nlp.relation.util;
 
-import org.diagbot.nlp.relation.extract.module.Symptom;
-import org.diagbot.nlp.relation.extract.module.Vital;
+import org.diagbot.nlp.relation.module.Symptom;
+import org.diagbot.nlp.relation.module.Vital;
 
 import java.util.ArrayList;
 import java.util.List;

nlp/src/main/java/org/diagbot/nlp/util/Constants.java

@@ -17,7 +17,7 @@ public class Constants {
     public final static String feature_type_history = "7";       //历史
     public final static String feature_type_feature = "9"; //症状描述中的特征信息 如部位、性质等
 
-    public static NegativeEnum[] symptom_type = new NegativeEnum[]{NegativeEnum.SYMPTOM};
+    public static NegativeEnum[] symptom_type = new NegativeEnum[]{NegativeEnum.SYMPTOM, NegativeEnum.SYMPTOM_INDEX, NegativeEnum.SYMPTOM_PERFORMANCE};
     public static NegativeEnum[] unit_time_type = new NegativeEnum[]{NegativeEnum.EVENT_TIME, NegativeEnum.UNIT};
     public static NegativeEnum[] vital_type = new NegativeEnum[]{NegativeEnum.VITAL_INDEX_VALUE, NegativeEnum.VITAL_RESULT};
     public static NegativeEnum[] vital_index_type = new NegativeEnum[]{NegativeEnum.VITAL_INDEX};

nlp/src/main/java/org/diagbot/nlp/util/NegativeEnum.java

@@ -4,12 +4,12 @@ package org.diagbot.nlp.util;
  * Created by Administrator on 2017/11/8.
  */
 public enum NegativeEnum {
-    NUMBER("0"), SYMPTOM("1"), EVENT_TIME("2"), BODY_PART("3"), PROPERTY("4"), CAUSE("5"), DEEP("6"), FEMININE("7"), POSITIVE("8"),
+    NUMBER("0"), SYMPTOM("1"), SYMPTOM_INDEX("75"), EVENT_TIME("2"), EVENT_TIME_DESC("74"), BODY_PART("3"), PROPERTY("4"), CAUSE("5"), DEEP("6"), DEEP_QUANTIFIER("80"), FEMININE("7"), POSITIVE("8"),
     UNIT("9"), MEDICINE("10"), TREATMENT("11"),
     LIS_TYPE("13"), LIS_NAME("14"), LIS_RESULT("15"), WAY("16"),
     PACS_NAME("17"), PACS_RESULT("18"),
     DISEASE("19"), JOIN("20"), POSITION("21"), FAMILY("24"), FOOD("27"), PAST("28"), OPERATION("29"),
-    HABIT("25"), SYMPTOM_PERFORMANCE("32"), EVENT_TIME_VALUE("33"), FREQUENCY("34"),
+    HABIT("25"), SYMPTOM_PERFORMANCE("32"), NUMBER_QUANTIFIER("33"), FREQUENCY("34"),
     OTHER("99"),
     VITAL_INDEX("60"), VITAL_INDEX_VALUE("61"), VITAL_RESULT("62"), VITAL_BODY_PART("63"),
     ADDRESS("70"), PERSON("72"), PERSON_FEATURE_DESC("73");
@@ -109,7 +109,7 @@ public enum NegativeEnum {
                 negativeEnum = NegativeEnum.SYMPTOM_PERFORMANCE;
                 break;
             case "33":
-                negativeEnum = NegativeEnum.EVENT_TIME_VALUE;
+                negativeEnum = NegativeEnum.NUMBER_QUANTIFIER;
                 break;
             case "34":
                 negativeEnum = NegativeEnum.FREQUENCY;
@@ -135,6 +135,15 @@ public enum NegativeEnum {
             case "73":
                 negativeEnum = NegativeEnum.PERSON_FEATURE_DESC;
                 break;
+            case "74":
+                negativeEnum = NegativeEnum.EVENT_TIME_DESC;
+                break;
+            case "75":
+                negativeEnum = NegativeEnum.SYMPTOM_INDEX;
+                break;
+            case "80":
+                negativeEnum = NegativeEnum.DEEP_QUANTIFIER;
+                break;
             case "99":
                 negativeEnum = org.diagbot.nlp.util.NegativeEnum.OTHER;
                 break;

nlp/src/main/java/org/diagbot/nlp/util/NlpUtil.java

@@ -22,7 +22,7 @@ public class NlpUtil {
 
     public static boolean isNumberString(Lexeme l) {
         if (l == null) return false;
-        if (NlpUtil.isFeature(l.getProperty(), new NegativeEnum[]{NegativeEnum.EVENT_TIME_VALUE})) {
+        if (NlpUtil.isFeature(l.getProperty(), new NegativeEnum[]{NegativeEnum.EVENT_TIME_DESC})) {
             return true;
         }
         for (char c : l.getText().toCharArray()) {

nlp/src/main/resources/classify.dict

@@ -166,7 +166,6 @@ ZS17Dgm0g5sr9CAO6fuC/753qq2yF2cWRRuFBtx13KM=
 ZS17Dgm0g5uWR6gm1zbExfAZVFfu7NX1kG57ceWMY4U=
 ZS17Dgm0g5tIBVGJQ5x2a7I5g1QSIqIgK4DYmKUtYzA=
 ZS17Dgm0g5tIBVGJQ5x2a7LMlg0j5ZRh9csoaYhecJCqne2lkXR8xlSdFN30dJby
-ZS17Dgm0g5vdPmsNsdD0Um4OXV6Tw9PG7V4GyMOt7zhW9jHJ+r/xLg==
 ZS17Dgm0g5vdPmsNsdD0UiKD3zwhl3gh7V4GyMOt7zhW9jHJ+r/xLg==
 ZS17Dgm0g5vdPmsNsdD0Umh6VPbMFwmv7V4GyMOt7zhW9jHJ+r/xLg==
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nlp/src/main/resources/push-tc.dict

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nlp/src/main/resources/synonym.dict

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nlp/src/test/java/org/diagbot/nlp/test/EntityExtractTest.java

@@ -0,0 +1,269 @@
+package org.diagbot.nlp.test;
+
+import org.diagbot.nlp.participle.ParticipleUtil;
+import org.diagbot.nlp.participle.cfg.Configuration;
+import org.diagbot.nlp.participle.cfg.DefaultConfig;
+import org.diagbot.nlp.participle.word.Lexeme;
+import org.diagbot.nlp.participle.word.LexemePath;
+import org.diagbot.nlp.participle.word.Segment;
+import org.diagbot.nlp.util.Constants;
+import org.diagbot.nlp.util.NegativeEnum;
+import org.diagbot.pub.jdbc.MysqlJdbc;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+public class EntityExtractTest {
+    public static void main(String[] args) {
+
+        Configuration configuration = new DefaultConfig();
+        Segment segment = configuration.loadMainDict("tc.dict");
+
+        EntityExtractTest entityExtractTest = new EntityExtractTest();
+        List<Map<String, String>> data = entityExtractTest.searchData();
+
+        Map<String, List<String>> pairList =  entityExtractTest.searchPropertyPair();
+
+        Map<String, String> lexiconTypeMap = entityExtractTest.searchLexiconType();
+
+        String present = "";
+        String[] partPresents;
+        String sentenceId = "";
+        LexemePath<Lexeme> lexemes = null;
+        List<Map<String, Object>> results_pair = new ArrayList<>();
+        List<Map<String, Object>> results_none_pair = new ArrayList<>();
+        try {
+            for (int i = 0; i < data.size(); i++) {
+                present = data.get(i).get("xbs");
+                present = present.replaceAll("\r\n", "");
+                sentenceId = data.get(i).get("zyxh");
+                partPresents = present.split(";|；|。");
+                for (int k = 0; k < partPresents.length; k++) {
+                    if (partPresents[k].length() == 0) {
+                        continue;
+                    }
+                    lexemes = ParticipleUtil.participle(partPresents[k], segment);
+
+                    lexemes = entityExtractTest.combineValidate(lexemes);
+                    for (int l_1 = 0; l_1 < lexemes.size(); l_1++) {
+                        Lexeme lexeme_1 = lexemes.get(l_1);
+                        if ("99".equals(lexeme_1.getProperty())) {
+                            continue;
+                        } else {
+                            for (int l_2 = l_1 + 1; l_2 < lexemes.size(); l_2++) {
+                                Lexeme lexeme_2 = lexemes.get(l_2);
+                                if (!"99".equals(lexeme_2.getProperty())) {
+                                    boolean isPair = entityExtractTest.isPair(pairList, lexeme_1.getProperty(), lexeme_2.getProperty());
+                                    Map<String, Object> result = new HashMap<>();
+                                    result.put("sentence_id", sentenceId);
+                                    result.put("sentence_uuid", sentenceId + "_" + k);
+                                    result.put("sentence", partPresents[k]);
+                                    result.put("entity_1_name", lexeme_1.getText());
+                                    result.put("entity_1_position", lexeme_1.getOffset() + "," + (lexeme_1.getOffset() + lexeme_1.getLength() - 1));
+                                    result.put("entity_1_prop", lexeme_1.getProperty());
+                                    result.put("entity_1_prop_name", entityExtractTest.propId2Name(lexiconTypeMap, lexeme_1.getProperty()));
+                                    result.put("entity_2_name", lexeme_2.getText());
+                                    result.put("entity_2_position", lexeme_2.getOffset() + "," + (lexeme_2.getOffset() + lexeme_2.getLength() - 1));
+                                    result.put("entity_2_prop", lexeme_2.getProperty());
+                                    result.put("entity_2_prop_name", entityExtractTest.propId2Name(lexiconTypeMap, lexeme_2.getProperty()));
+                                    result.put("relation", "0");
+                                    if (isPair) {
+                                        results_pair.add(result);
+                                    } else {
+                                        results_none_pair.add(result);
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+
+            if (results_pair.size() > 0) {
+                MysqlJdbc nlpJdbc = new MysqlJdbc("root", "diagbot@20180822", "jdbc:mysql://192.168.2.235:3306/nlp-web?useUnicode=true&characterEncoding=UTF-8");
+                nlpJdbc.insert(results_pair, "re_tagging_result_part", new String[]{"sentence_id", "sentence_uuid", "sentence", "entity_1_position", "entity_2_position",
+                        "entity_1_name", "entity_2_name", "entity_1_prop", "entity_2_prop", "entity_1_prop_name", "entity_2_prop_name", "relation"});
+            }
+
+            if (results_none_pair.size() > 0) {
+                MysqlJdbc nlpJdbc = new MysqlJdbc("root", "diagbot@20180822", "jdbc:mysql://192.168.2.235:3306/nlp-web?useUnicode=true&characterEncoding=UTF-8");
+                nlpJdbc.insert(results_none_pair, "re_tagging_result_none_part", new String[]{"sentence_id", "sentence_uuid", "sentence", "entity_1_position", "entity_2_position",
+                        "entity_1_name", "entity_2_name", "entity_1_prop", "entity_2_prop", "entity_1_prop_name", "entity_2_prop_name", "relation"});
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    public List<Map<String, String>> searchData() {
+        MysqlJdbc nlpJdbc = new MysqlJdbc("root", "diagbot@20180822", "jdbc:mysql://192.168.2.235:3306/nlp-web?useUnicode=true&characterEncoding=UTF-8");
+        List<Map<String, String>> data = nlpJdbc.query("tb_ryjl_extract", new String[]{"zyxh", "xbs"}, " limit 0, 3");
+        return data;
+    }
+
+    public Map<String, String> searchLexiconType() {
+        MysqlJdbc nlpJdbc = new MysqlJdbc("root", "diagbot@20180822", "jdbc:mysql://192.168.2.235:3306/nlp-web?useUnicode=true&characterEncoding=UTF-8");
+        List<Map<String, String>> data = nlpJdbc.query("re_lexicon_type", new String[]{"id", "name"}, " limit 0, 1000");
+
+        Map<String, String> result = new HashMap<>();
+        for (Map<String, String> map : data) {
+            result.put(map.get("id"), map.get("name"));
+        }
+        return result;
+    }
+
+    public Map<String, List<String>> searchPropertyPair() {
+        MysqlJdbc nlpJdbc = new MysqlJdbc("root", "diagbot@20180822", "jdbc:mysql://192.168.2.235:3306/nlp-web?useUnicode=true&characterEncoding=UTF-8");
+        List<Map<String, String>> data = nlpJdbc.query("re_lexicon_property_pair", new String[]{"prop1_id", "prop2_id"}, " where has_relation = 1");
+
+        Map<String, List<String>> result = new HashMap<>();
+        List<String> list = null;
+        for (Map<String, String> map : data) {
+            String prop1_id = map.get("prop1_id");
+            String prop2_id = map.get("prop2_id");
+
+            list = result.get(prop1_id);
+            if (list == null) {
+                list = new ArrayList<>();
+                list.add(prop2_id);
+                result.put(prop1_id, list);
+            } else {
+                if (!list.contains(prop2_id)) {
+                    list.add(prop2_id);
+                    result.put(prop1_id, list);
+                }
+            }
+
+            list = result.get(prop2_id);
+            if (list == null) {
+                list = new ArrayList<>();
+                list.add(prop1_id);
+                result.put(prop2_id, list);
+            } else {
+                if (!list.contains(prop1_id)) {
+                    list.add(prop1_id);
+                    result.put(prop2_id, list);
+                }
+            }
+        }
+        return result;
+    }
+
+    private boolean isPair(Map<String, List<String>> result, String prop1_id, String prop2_id) {
+        String[] prop1_ids = prop1_id.split(",");
+        String[] prop2_ids = prop2_id.split(",");
+        List<String> list = null;
+        for (int i = 0; i < prop1_ids.length; i++) {
+            list = result.get(prop1_ids[i]);
+            if (list == null) continue;
+            for (int j = 0; j < prop2_ids.length; j++) {
+                if (list.contains(prop2_ids[j])) {
+                    return true;
+                }
+            }
+        }
+        return false;
+    }
+
+    public String propId2Name(Map<String, String> map, String prop_id) {
+        String[] prop_ids = prop_id.split(",");
+
+        String prop_name = "";
+        for (int i = 0; i < prop_ids.length; i++) {
+            if (i == 0) {
+                prop_name = map.get(prop_ids[i]);
+            } else {
+                prop_name = prop_name + "," + map.get(prop_ids[i]);
+            }
+        }
+        return prop_name;
+    }
+
+    public String lexeme2Text(List<Lexeme> lexemes) {
+        String text = "";
+        for (Lexeme l : lexemes) {
+            text = text + l.getText();
+        }
+        return text;
+    }
+
+    private LexemePath<Lexeme> combineValidate(LexemePath<Lexeme> lexemes) {
+        Lexeme l = null;
+        LexemePath<Lexeme> results = new LexemePath<>();
+        for (int i = 0, len = lexemes.size(); i < len; i++) {
+            l = lexemes.get(i);
+            if (l.getProperty() != null
+                    && (l.getProperty().equals(Constants.word_property_time) || l.getProperty().equals(Constants.word_property_unit))) {
+                findLast(lexemes, i, l, results);
+            } else {
+                results.add(l);
+            }
+        }
+        return results;
+    }
+
+    private void findLast(LexemePath<Lexeme> lexemes, int index, Lexeme lexeme, LexemePath<Lexeme> results) {
+        Lexeme last_l = null;
+        if (index > 0) {
+            index--;
+            last_l = lexemes.get(index);
+            if ("×".equals(last_l.getText()) && index > 0) {
+                lexeme.setOffset(last_l.getOffset());
+                lexeme.setLength(last_l.getLength() + lexeme.getLength());
+                lexeme.setText(last_l.getText() + lexeme.getText());
+                results.remove(results.size() - 1);
+                index--;
+                last_l = lexemes.get(index);
+            }
+            if (isNumberString(last_l)) {
+                lexeme.setOffset(last_l.getOffset());
+                lexeme.setLength(last_l.getLength() + lexeme.getLength());
+                lexeme.setText(last_l.getText() + lexeme.getText());
+                results.remove(results.size() - 1);
+            }
+            results.add(lexeme);
+        }
+    }
+
+    public static boolean isNumberString(Lexeme l) {
+        if (l == null) return false;
+        if (isFeature(l.getProperty(), new NegativeEnum[]{NegativeEnum.EVENT_TIME_DESC})) {
+            return true;
+        }
+        for (char c : l.getText().toCharArray()) {
+            if (c >= '0' && c <= '9') {
+                return true;
+            }
+        }
+        return false;
+    }
+
+    public static boolean isFeature(String property, NegativeEnum[] features) {
+        if (property == null) {
+            return false;
+        }
+        if (features == null || features.length == 0) {
+            return true;
+        }
+        if (property.indexOf(",") > 0) {
+            String[] properties = property.split(",");
+            for (int i = 0; i < properties.length; i++) {
+                for (NegativeEnum nenum : features) {
+                    if (NegativeEnum.parseOfValue(properties[i]) == nenum) {
+                        return true;
+                    }
+                }
+            }
+        } else {
+            for (NegativeEnum nenum : features) {
+                if (NegativeEnum.parseOfValue(property) == nenum) {
+                    return true;
+                }
+            }
+        }
+        return false;
+    }
+
+}

nlp/src/test/java/org/diagbot/nlp/test/LexemeDicTest.java

@@ -31,7 +31,7 @@ public class LexemeDicTest {
             String path = this.getClass().getClassLoader().getResource("").getPath();
             path = path.substring(0, path.indexOf("target"));
             //所有词典库
-            String sql = "select distinct name, category_id, status from kl_standard_info where category_id != 100 and category_id is not null && category is not null order by name";
+            String sql = "select distinct name, category_id, status from kl_standard_info_0318 where category_id != 100 and category_id is not null && category is not null order by name";
             st = conn.createStatement();
             rs = st.executeQuery(sql);
             List<Map.Entry<String, String>> libraryList = rsToMap(rs, true);

public/src/main/java/org/diagbot/pub/jdbc/MysqlJdbc.java

@@ -151,13 +151,13 @@ public class MysqlJdbc {
                 sb.append(")");
                 first_index++;
 
-                if ((cursor + 1) % 10000 == 0) {
+                if ((cursor + 1) % 1000 == 0) {
                     st.executeUpdate(sql + sb.toString());
                     sb = new StringBuffer();
                     first_index = 0;
                 }
             }
-            if (data.size() % 10000 != 0) {
+            if (data.size() % 1000 != 0) {
                 st.executeUpdate(sql + sb.toString());
             }
             conn.commit();