Characterizing mention mismatching problems for improving recognition results

Mentions to real world things which are recognized by software tools in text often mismatch the ground truth. This paper proposes a formal classification of mention mismatching problems, including partial matching. Then, it depicts evidence that some longer mentions are associated with higher precision and more specific things than shorter mentions that overlap them. Based on this, some algorithms are proposed to automatically improve mentions by increasing their sizes whenever and as much as possible. Experimental results applying a variety of state-of-the-art annotation tools against several datasets made from real world texts show that over-segmentation (returned mention contained in the corresponding one of the ground truth) is the most prevalent partial matching problem among those of the proposed classification. In addition, some of the proposed algorithms for mention enhancing were able to correct most over-segmented mentions returned by tools used in the experiments with prominent benchmarks, leading to gains in precision and recall.