Free Text Gene Name Recognition

目前，我们正在进行两个旨在解决基因/蛋白质名称识别问题的项目： 1)我们已经生成了一组20，000个句子，其中所有出现的基因/蛋白质名称都用单词偏移标记为句子中的名称开头和名称结尾。这些句子是从MEDLINE摘要的限制类中随机抽取的。其中一半被选为可能有基因/蛋白质名称，另一半被选为不太可能有这样的名称。由于在标记名称时存在歧义，因此在认为合适的情况下，将替代标记列为正确答案。其中四分之三的名字构成了今年在西班牙格拉纳达举行的最近的BioCreAtIvE（生物学信息提取的关键评估）研讨会的任务的基础。12个团队试图设计出能够正确标记句子中基因/蛋白质名称的系统。几个团队获得了80%以下的精确度和召回率。许多不同的方法是成功的，这些结果表明，我们可以改善ABGene的方法。 2)我们已经确信，可以使用MEDLINE中句子中可能出现的不同类型实体的更多信息来提高名称识别。这使我们设计了一组语义类别，并试图用可以从数据库和网站中获得的实际名称来填充这些类别。我们称之为SEMCAT。它目前可以识别75个类别，包含分布在这些类别中的大约400万个名称字符串。我们正在尝试使用概率上下文无关文法和文本字符串的马尔可夫模型，试图学习如何识别不同类别的实体。

Currently we are pursuing two projects designed to make progress on the problem of gene/protein name recognition: 1) We have produced a set of 20,000 sentences with all occurrences of gene/protein names in them marked up with the word offset for name beginning and name ending in the sentence. The sentences were taken as random samples from restricted classes of MEDLINE abstracts. Half were chosen as likely to have gene/protein names in them and half were selected as unlikely to have such names. Since there is ambiguity in marking names, alternative markings are listed as correct answers where this is thought to be appropriate. Three fourths of these names formed the basis for a task in the recent BioCreAtIvE (Critical Assessment of Information Extraction in Biology) Workshop held in Granada, Spain this year. Twelve teams attempted to designed systems that could correctly tag the gene/protein names in the sentences. Several teams obtained precisions and recalls in the low 80% range. A number of different approaches were successful and these results suggest ways in which we can improve ABGene. 2) We have become convinced that more information about the different types of entities that can occur in sentences in MEDLINE can be used to improve name recognition. This has led us to design a set of semantic categories and to attempt to fill these categories with actual names that can be harvested from databases and from web sites. We call the result SEMCAT. It currently recognizes seventy-five categories and contains about four million name strings distributed over those categories. We are experimenting with probabilistic context free grammars and Markov models of text strings in an attempt to learn how to recognize the entities in different categories.