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Derivation of CPG Labels for English and its Applications in Language and VisionAuthor: SAI KIRAN GORTHI 200802012 Date: 2022-11-05 Report no: IIIT/TH/2022/132 Advisor:Radhika Mamidi AbstractAll the parsers built for English till date are either syntactic (constructs possible syntactic structures of the sentence) or semantic (maps text to formal meaning representations). CPG is syntactico-semantic, a machine-understandable representation of the sentence with a syntactic structure as well as semantic labels. More than 80% of the dependency relations can be represented with just 7 of the Karaka relations. We first designed and developed a rule-based mapper to convert Stanford dependencies(42) to Karaka labels(7). The accuracy of the mapper was 52% and is domain-independent. This was used in the academic/courses domain based NLIDB [27] system to identify and map to verb frames/templates to further generate the SQL query. More details on that in further chapters. The next version was to analyze the English Tree-bank (manually annotated Karaka labels) data and build a generic mapper using ML methods. We examined the Karaka dependencies and listed some features that could affect the nature of the Karaka relation. We took the English Tree-bank data, manually annotated by LTRC and converted it into a format suitable for application of statistical methods. After cross-validation within the data set, accuracy of the results went upto 67% using CRF++. Re-examining and adding one more feature increased the accuracy by 2%. Then using SVM (LIBSVM) on the same data took the overall accuracy to 74%. The previous 2 experimental results were applied in the context of Language (NLIDB) model only with limited dataset. Compared to other Vision + Language tasks, datasets for Embodied AI tasks tend to be longer and contain more actions, which makes it naturally suitable for CPG analysis as CPG is also centered around actions. As the concluding experiment, we used the Karaka relations in a MultiModal system to improve the syntactic and semantic understanding of the instruction set with a small set of labels. We compared the results with other labeling systems like NLTK (PoS tags) and Stanford parser (Dependency labels). Full thesis: pdf Centre for Language Technologies Research Centre |
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