12 Result(s)
within
Conference Paper
Ganesh Ramakrishnan
Artificial Intelligence
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Chapter and Conference Paper
Difficulty-Controllable Multi-hop Question Generation from Knowledge Graphs
Knowledge graphs have become ubiquitous data sources and their utility has been amplified by the research on ability to answer carefully crafted questions over knowledge graphs. We investigate the problem of q...
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Chapter and Conference Paper
Automating Reading Comprehension by Generating Question and Answer Pairs
Neural network-based methods represent the state-of-the-art in question generation from text. Existing work focuses on generating only questions from text without concerning itself with answer generation. More...
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Chapter and Conference Paper
Building Compact Lexicons for Cross-Domain SMT by Mining Near-Optimal Pattern Sets
Statistical machine translation models are known to benefit from the availability of a domain bilingual lexicon. Bilingual lexicons are traditionally comprised of multiword expressions, either extracted from p...
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Chapter and Conference Paper
Explicit Query Interpretation and Diversification for Context-Driven Concept Search Across Ontologies
Finding relevant concepts from a corpus of ontologies is useful in many scenarios, such as document classification, web page annotation, and automatic ontology population. Many millions of concepts are contain...
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Chapter and Conference Paper
Rel-Div: Generating Diversified Query Interpretations from Semantic Relations
Accelerated growth of the World Wide Web has resulted in an increase in appetite for searching over Internet to fulfill the information needs. Understanding user intent plays a pivotal role in determining the ...
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Chapter and Conference Paper
What Kinds of Relational Features Are Useful for Statistical Learning?
A workmanlike, but nevertheless very effective combination of statistical and relational learning uses a statistical learner to construct models with features identified (quite often, separately) by a relation...
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Chapter and Conference Paper
Probing the Space of Optimal Markov Logic Networks for Sequence Labeling
Discovering relational structure between input features in sequence labeling models has shown to improve their accuracies in several problem settings. The problem of learning relational structure for sequence ...
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Chapter and Conference Paper
BET : An Inductive Logic Programming Workbench
Existing ILP (Inductive Logic Programming) systems are implemented in different languages namely C, Progol, etc. Also, each system has its customized format for the input data. This makes it very tedious and t...
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Chapter and Conference Paper
Pruning Search Space for Weighted First Order Horn Clause Satisfiability
Many SRL models pose logical inference as weighted satisfiability solving. Performing logical inference after completely grounding clauses with all possible constants is computationally expensive and approache...
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Chapter and Conference Paper
Using ILP to Construct Features for Information Extraction from Semi-structured Text
Machine-generated documents containing semi-structured text are rapidly forming the bulk of data being stored in an organisation. Given a feature-based representation of such data, methods like SVMs are able t...
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Chapter and Conference Paper
Feature Construction Using Theory-Guided Sampling and Randomised Search
It has repeatedly been found that very good predictive models can result from using Boolean features constructed by an an Inductive Logic Programming (ILP) system with access to relevant relational information...
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Chapter and Conference Paper
Word Sense Disambiguation Using Inductive Logic Programming
The identification of the correct sense of a word is necessary for many tasks in automatic natural language processing like machine translation, information retrieval, speech and text processing. Automatic Wor...
