Package edu.umd.cloud9.collection.medline

Examples of edu.umd.cloud9.collection.medline.NumberMedlineCitations


    return new JUnit4TestAdapter(EnZh_NTCIR8.class);
  }

  public static void main(String[] args) {
    //    HMapSFW gridAPMap = array2Map(Interp_AP);
    HMapSFW tenbestAPMap = array2Map(Nbest_AP.get(2));
    HMapSFW onebestAPMap = array2Map(Onebest_AP.get(1));
    HMapSFW grammarAPMap = array2Map(grammar_AP.get(0));
    HMapSFW tokenAPMap = array2Map(baseline_token_AP);
    //    System.out.println(countNumberOfImprovedTopics(tokenAPMap, gridAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, tenbestAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, onebestAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, grammarAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, tokenAPMap));
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    }
    return cnt;
  }

  private static HMapSFW array2Map(String[] array) {
    HMapSFW map = new HMapSFW();
    for ( int i = 0; i < array.length; i += 2 ) {
      map.put(array[i], Float.parseFloat(array[i+1]));
    }
    return map;
  }
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    transPhrase = transPhrase.trim();

    //LOG.info("Found translation phrase " + transPhrase);

    if (!phrase2score.containsKey(fPhrase)) {
      phrase2score.put(fPhrase, new HMapSFW());
    }
    // if same phrase extracted from multiple rules, average prob.s

    HMapKF<String> scoreTable = phrase2score.get(fPhrase);
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    // scfgDist table is a set of (source_token --> X) maps, where X is a set of (token_trans --> score) maps
    Map<String,HMapSFW> scfgDist = new HashMap<String,HMapSFW>();

    // phrase2count table is a set of (source_phrase --> X) maps, where X is a set of (phrase_trans --> count) maps
    HMapSFW phraseDist = new HMapSFW();

    HMapSIW srcTokenCnt = new HMapSIW();

    Set<String> bagOfTargetTokens = new HashSet<String>();
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          continue;     
        }
        bagOfTargetTokens.add(eTerm);
        if (isOne2Many <= 1) {
          if (probDist.containsKey(fTerm)) {
            HMapSFW eToken2Prob = probDist.get(fTerm);
            eToken2Prob.increment(eTerm, weight);
          }else {
            HMapSFW eToken2Prob = new HMapSFW();
            eToken2Prob.put(eTerm, weight);
            probDist.put(fTerm, eToken2Prob);
          }
        }
      }

      if (isOne2Many == 2) {
        // if ids.size() > 1 eTerm is a multi-token expression
        // even if eTerm is overwritten here, we need to do above loop to update bagOfTargetTokens
        if (ids.size() > 1) {
          eTerm = isConsecutiveWithStopwords(ids, rhs, docLangTokenizer);     // <---- heuristic
        }

        // no proper translation on target-side (e.g., stopword OR non-consecutive multi-word translation), let's skip
        if (eTerm == null) {
          continue;
        }

        eTerm = Utils.removeBorderStopWords(docLangTokenizer, eTerm);
       
        // this is difference between one-to-many and one-to-one heuristics for 1-best MT case
        // we add multi-token expressions in addition to single target tokens,
        bagOfTargetTokens.add(eTerm);

        // update prob. distr.
        if (probDist.containsKey(fTerm)) {
          HMapSFW eToken2Prob = probDist.get(fTerm);
          eToken2Prob.increment(eTerm, weight);
        }else {
          HMapSFW eToken2Prob = new HMapSFW();
          eToken2Prob.put(eTerm, weight);
          probDist.put(fTerm, eToken2Prob);
        }
      }
    }
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   * @param threshold
   * @param scale
   * @param probMap
   */
  public static HMapSFW scaleProbMap(float threshold, float scale, HMapSFW probMap) {
    HMapSFW scaledProbMap = new HMapSFW();

    for (Entry<String> entry : probMap.entrySet()) {
      float pr = entry.getValue() * scale;
      if (pr > threshold) {
        scaledProbMap.put(entry.getKey(), pr);
      }
    }

    return scaledProbMap;
  }
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   *    value between 0 and 1 that determines total probability in final distribution (e.g., 0.2 scale will scale [0.8 0.1 0.1] into [0.16 0.02 0.02])
   * @param probMaps
   *    list of probability distributions
   */
  public static HMapSFW combineProbMaps(float threshold, float scale, List<PairOfFloatMap> probMaps) {
    HMapSFW combinedProbMap = new HMapSFW();

    int numDistributions = probMaps.size();

    // get a combined set of all translation alternatives
    // compute normalization factor when sum of weights is not 1.0
    Set<String> translationAlternatives = new HashSet<String>();
    float sumWeights = 0;
    for (int i=0; i < numDistributions; i++) {
      HMapSFW dist = probMaps.get(i).getMap();
      float weight = probMaps.get(i).getWeight();

      // don't add vocabulary from a distribution that has 0 weight
      if (weight > 0) {
        translationAlternatives.addAll(dist.keySet());
        sumWeights += weight;
      }
    }

    // normalize by sumWeights
    for (String e : translationAlternatives) {
      float combinedProb = 0f;
      for (int i=0; i < numDistributions; i++) {
        HMapSFW dist = probMaps.get(i).getMap();
        float weight = probMaps.get(i).getWeight();
        combinedProb += (weight/sumWeights) * dist.get(e);    // Prob(e|f) = weighted average of all distributions
      }
      combinedProb *= scale;
      if (combinedProb > threshold) {
        combinedProbMap.put(e, combinedProb);
      }
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   * @param cumProbThreshold
   * @param maxNumTrans
   */
  public static void normalize(Map<String, HMapSFW> probMap, float lexProbThreshold, float cumProbThreshold, int maxNumTrans) {
    for (String sourceTerm : probMap.keySet()) {
      HMapSFW probDist = probMap.get(sourceTerm);
      TreeSet<PairOfStringFloat> sortedFilteredProbDist = new TreeSet<PairOfStringFloat>();
      HMapSFW normProbDist = new HMapSFW();

      // compute normalization factor
      float sumProb = 0;
      for (Entry<String> entry : probDist.entrySet()) {
        sumProb += entry.getValue();
      }

      // normalize values and remove low-prob entries based on normalized values
      float sumProb2 = 0;
      for (Entry<String> entry : probDist.entrySet()) {
        float pr = entry.getValue() / sumProb;
        if (pr > lexProbThreshold) {
          sumProb2 += pr;
          sortedFilteredProbDist.add(new PairOfStringFloat(entry.getKey(), pr));
        }
      }

      // re-normalize values after removal of low-prob entries
      float cumProb = 0;
      int cnt = 0;
      while (cnt < maxNumTrans && cumProb < cumProbThreshold && !sortedFilteredProbDist.isEmpty()) {
        PairOfStringFloat entry = sortedFilteredProbDist.pollLast();
        float pr = entry.getValue() / sumProb2;
        cumProb += pr;
        normProbDist.put(entry.getKey(), pr);
        cnt++;
      }

      probMap.put(sourceTerm, normProbDist);
    }
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    return new JUnit4TestAdapter(EnAr_TREC02.class);
  }

  public static void main(String[] args) {
    //    HMapSFW gridAPMap = array2Map(Interp_AP);
    HMapSFW tenbestAPMap = array2Map(Nbest_AP.get(2));
    HMapSFW onebestAPMap = array2Map(Onebest_AP.get(1));
    HMapSFW grammarAPMap = array2Map(grammar_AP.get(0));
    HMapSFW tokenAPMap = array2Map(baseline_token_AP);
    //    System.out.println(countNumberOfImprovedTopics(tokenAPMap, gridAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, tenbestAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, onebestAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, grammarAPMap));
    System.out.println(countNumberOfImprovedTopics(tokenAPMap, tokenAPMap));
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    }
    return cnt;
  }

  private static HMapSFW array2Map(String[] array) {
    HMapSFW map = new HMapSFW();
    for ( int i = 0; i < array.length; i += 2 ) {
      map.put(array[i], Float.parseFloat(array[i+1]));
    }
    return map;
  }
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