这是不可行的。数据模型和底层技术是不同的。
Paratext 的使用场景： 如果您希望将某些单词拆分为语素，但不想严格定义每个语素及其所有异形体。
Fieldworks 解析器的使用场景： 我希望将所有单词拆分为语素，并严格定义每个语素及其所有异形体。
这两个解析器擅长的方面不同。不建议在解析器之间来回切换。
我认为，如果您将 Flex 中的基线文本复制到 Paratext 中的一个额外书籍中，并且手动使用最常见的语素来拆分单词，它很快就会开始识别语素。您可以尝试一下，看看是否喜欢结果。
我快速教会 Paratext 解析器的建议是使用单词列表工具，而不是项目逐词对照工具，来查找并解析一些最具生产力的单词。例如，对单词列表工具中一个非常常见的动词进行排序，并手动解析该动词的几个表层形式。Paratext 解析器会获取这些信息，并将其应用于其他动词。它将很快学会识别该语言的时态/体标记。
It is not possible. The data models and underlying technologies are different.
Use case for Paratext: If you want to break some words into morphemes but you do not want to rigorously define each morpheme and all of its allomorphs.
Use case for Fieldworks parser: I do want to break all words into morphemes and rigorously define each morpheme and all of their allomorphs.
What the two parsers are good at is different. Moving back and forth between parsers is not advisable.
I would say that if you copy the baseline text from Flex to an extra book in Paratext, it will very quickly start recognizing morphemes if you manually divide words with the most common morphemes. You can give it a try and see if you like the results.
My suggestion for teaching the Paratext Parser quickly is to use the wordlist tool and not the project interlinearizer to find and parse some of the most productive words. For example sort the word list tool for a very common verb and manually parse several surface forms of that verb. The Paratext parser will take that information and will apply that to other verbs. It will quickly learn to recognize the tense/aspect markers for the language.
机器翻译自 English
