assigned to interlinear glosses using Coreference Resolution models from Natural Language Processing, where the interlinear gloss instance was tagged with the language name (and ID) that appears in the scholarly document the interlinear gloss instance was extracted from. In the manual correction phase, the database creators manually corrected the boundaries of the interlinear gloss instances discovered by the sequence-labelling method in Step 2 of the automatic construction phase. The creators then verified the language names and language codes in a second and third pass over the data, respectively. Automatic processing of interlinear gloss instances Natural Language Processing models leveraging interlinear gloss resources, such as the Online Database of Interlinear Text, have been developed. Automatic glossing Natural Language Processing systems, for example, have been developed to automatically produce interlinear glosses.: Given the morpheme segmented line (first line above) and the free translation line (third line above), the task is to produce the middle glossed line comprising stem translations (e.g., mi:you) and the grammatical category labels corresponding to affixes (e.g., a:ERG.1.PL). Sequence prediction models from Natural Language Processing have been used to perform this task. Two factors contribute to the difficulty of this task: The translation is not necessarily in alignment with the morpheme segmented line (e.g., camel is the last word in the translation but the second word in the morpheme segmented line). Some words in the morpheme segmented line have multiple correspondences in the gloss (e.g., anu:be.NEG). Automatic discovery of morphological structure from glosses Researchers have used interlinear glosses is to obtain the morphological paradigms of the object language (i.e., the language being glossed). To automatically create morphological paradigms from interlinear glosses, researchers have created tables for every stem in the gloss and a (possibly empty) slot for every grammatical category (e.g., ERG) in the gloss. For instance, given the glossed sentence below: There would be a paradigm for the stem pobeja with slots for PFV.PST.SG.FEM and PFV.PST.SG.MASC: The slot for PFV.PST.SG.FEM would be filled (since it was observed in the interlinear gloss data) but the slot for PFV.PST.SG.MASC would be empty (assuming that no other interlinear gloss instance contains pobeja inflected for the PFV.PST.SG.MASC grammatical category). A statistical machine learning model for morphological inflection can be used to fill in the missing entries. See also Kanbun – Japanese tradition of glossing Classical Chinese texts Ruby text – a gloss sometimes used with Chinese or Japanese to show the pronunciation