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A Shortest Path Dependency Kernel for Relation Extraction
Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels. Expand
Using Encyclopedic Knowledge for Named entity Disambiguation
A disambiguation SVM kernel is trained to exploit the high coverage and rich structure of the knowledge encoded in an online encyclopedia and significantly outperforms a less informed baseline. Expand
Comparative experiments on learning information extractors for proteins and their interactions
The results show that it is promising to use machine learning to automatically build systems for extracting information from biomedical text with higher precision than manually-developed rules. Expand
Subsequence Kernels for Relation Extraction
We present a new kernel method for extracting semantic relations between entities in natural language text, based on a generalization of subsequence kernels. This kernel uses three types ofExpand
Learning to rank relevant files for bug reports using domain knowledge
An adaptive ranking approach that leverages domain knowledge through functional decompositions of source code files into methods, API descriptions of library components used in the code, the bug-fixing history, and the code change history is introduced. Expand
Multiple instance learning for sparse positive bags
This work presents a new approach to multiple instance learning (MIL) that is particularly effective when the positive bags are sparse (i.e. contain few positive instances) and is the best performing method for image region classification. Expand
Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques
This work presents sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents using natural language processing (NLP) techniques. Expand
Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments
This work combines several graph alignment features with lexical semantic similarity measures using machine learning techniques and shows that the student answers can be more accurately graded than if the semantic measures were used in isolation. Expand
From Word Embeddings to Document Similarities for Improved Information Retrieval in Software Engineering
This paper proposes bridging the lexical gap by projecting natural language statements and code snippets as meaning vectors in a shared representation space and shows that the learned vector space embeddings lead to improvements in a previously explored bug localization task and a newly introduced task of linking API documents to computer programming questions. Expand
FALCON: Boosting Knowledge for Answer Engines
FALCON, an answer engine that integrates different forms of syntactic, semantic and pragmatic knowledge for the goal of achieving better performance is discussed. Expand