results to several other researchers, including the competitors in the SemEval competitions. If this algorithm had competed in the SemEval competitions, it would have rank in the middle. This means that it is not the optimal algorithm, but it still works well enough to be of interest since it is not in the lower ranks. Algorithm Weaknesses One of the reasons why the results are not 100% has to do with the available semantic relations. An in-depth investigation of some of the words reveals that there is simply not enough information provided in context. Some words have a correct sense of something other than the most common sense, yet frequency is the only semantic relation that applies to the word. Frequency directs the result to the incorrect sense. False positives and misleading information do have an effect, but a large proportion of the words have insufficient information in order to provide acceptable results. In most of these cases, the information supplied by WordNet will not help. This includes several other semantic relations that were not part of this project. The author cursorily explored Lesk, but upon reflection it proved unhelpful. It is significant that many other algorithms appear to have similar problems. One of the top competitors in the third SemEval competition, Sense Learner, openly admits that they do have any information on 15% of the words (Mihalcea & Csomai, 2005). These words simply chose the most frequent definition as a default. Regardless, something needs to account for these words. This algorithm does appear to work fairly well with the available WordNet information. Many other researchers assume that the highest scores work while this algorithm attempts to adjust for the weaknesses within each semantic relation. However, there still are possible improvements around semantic relations in the cost function. One of the more significant problems is how large the 90% window is for some semantic relations. If there were a way to dynamically adjust the maximum point and reduce the deviation, the answers would improve. At this writing, both the distributions and the relations rely on averages. The distributions require more investigation and adjustments. Perhaps the
results to several other researchers, including the competitors in the SemEval competitions. If this algorithm had competed in the SemEval competitions, it would have rank in the middle. This means that it is not the optimal algorithm, but it still works well enough to be of interest since it is not in the lower ranks. Algorithm Weaknesses One of the reasons why the results are not 100% has to do with the available semantic relations. An in-depth investigation of some of the words reveals that there is simply not enough information provided in context. Some words have a correct sense of something other than the most common sense, yet frequency is the only semantic relation that applies to the word. Frequency directs the result to the incorrect sense. False positives and misleading information do have an effect, but a large proportion of the words have insufficient information in order to provide acceptable results. In most of these cases, the information supplied by WordNet will not help. This includes several other semantic relations that were not part of this project. The author cursorily explored Lesk, but upon reflection it proved unhelpful. It is significant that many other algorithms appear to have similar problems. One of the top competitors in the third SemEval competition, Sense Learner, openly admits that they do have any information on 15% of the words (Mihalcea & Csomai, 2005). These words simply chose the most frequent definition as a default. Regardless, something needs to account for these words. This algorithm does appear to work fairly well with the available WordNet information. Many other researchers assume that the highest scores work while this algorithm attempts to adjust for the weaknesses within each semantic relation. However, there still are possible improvements around semantic relations in the cost function. One of the more significant problems is how large the 90% window is for some semantic relations. If there were a way to dynamically adjust the maximum point and reduce the deviation, the answers would improve. At this writing, both the distributions and the relations rely on averages. The distributions require more investigation and adjustments. Perhaps the