Yogarshi Vyas

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Code-mixing is frequently observed in user generated content on social media, especially from multilingual users. The linguistic complexity of such content is compounded by presence of spelling variations , transliteration and non-adherance to formal grammar. We describe our initial efforts to create a multi-level annotated corpus of Hindi-English(More)
In this paper we describe our approach to the Abstract Meaning Representation (AMR) parsing shared task as part of SemEval 2016. We develop a novel technique to parse En-glish sentences into AMR using Learning to Search. We decompose the AMR parsing task into three subtasks-that of predicting the concepts , the relations, and the root. Each of these(More)
Visual narrative is often a combination of explicit information and judicious omissions, relying on the viewer to supply missing details. In comics, most movements in time and space are hidden in the " gutters " between panels. To follow the story, readers logically connect panels together by inferring unseen actions through a process called " closure ".(More)
We introduce the task of cross-lingual lexical entailment, which aims to detect whether the meaning of a word in one language can be inferred from the meaning of a word in another language. We construct a gold standard for this task, and propose an unsupervised solution based on distributional word representations. As commonly done in the monolingual(More)
We present a generic method for augmenting unsupervised query segmentation by incorporating Parts-of-Speech (POS) sequence information to detect meaningful but rare n-grams. Our initial experiments with an existing English POS tagger employing two different POS tagsets and an unsupervised POS induction technique specifically adapted for queries show that(More)
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