Downloads | Siva Reddy

CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation.

Data

Code

GraphParser: An Ungrounded and Grounded Semantic Parser

Graph Parser is a semantic parser which converts Natural Language Sentences/Questions to predicate-argument graphs, which can in-turn be converted to logical queries and executed on Freebase knowledge-graph. Please read more about it in our paper Large-scale Semantic Parsing without Question-Answer Pairs.

Download the code and data

Compound Noun Compositionality Dataset

Compositionality Dataset described in Reddy, McCarthy and Manandhar (2011, IJCNLP).
Alternate download link from Diana McCarthy

POS Taggers, Corpora, Lemmatizers, Morph Analyzers for Indian Languages

Most of these tools are developed by the methods described in Reddy and Sharoff (2011, CLIA @ IJCNLP). Some of the taggers are built using cross-lingual resources and some using mono-lingual resources. Please read corresponding README's of each tool for additional information.

This work is supported by Sketch Engine and Intellitext project.

If you need resources for any other Indian languages, please contact me.

CoQA: A Conversational Question Answering Challenge

GraphParser: An Ungrounded and Grounded Semantic Parser

Compound Noun Compositionality Dataset

POS Taggers, Corpora, Lemmatizers, Morph Analyzers for Indian Languages

Kannada Tools

Telugu Tools

Hindi Tools

Indonesian and Malay morphological analyzer, part-of-speech (POS) tagger, Machine Translation System

Hindi WordNet in Python

Hindi Dependency Parser