Search Results for author:
Found 10 papers, 0 papers with code
Statistical modality tagging from rule-based annotations and crowdsourcing
We explore training an automatic modality tagger.
Generating Extractive Summaries of Scientific Paradigms
Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material.
Lexical Conceptual Structure of Literal and Metaphorical Spatial Language: A Case Study of ``Push''
Prior methodologies for understanding spatial language have treated literal expressions such as {``}Mary pushed the car over the edge{''} differently from metaphorical extensions such as {``}Mary{'}s job pushed her over the edge{''}.
The Case for Systematically Derived Spatial Language Usage
This position paper argues that, while prior work in spatial language understanding for tasks such as robot navigation focuses on mapping natural language into deep conceptual or non-linguistic representations, it is possible to systematically derive regular patterns of spatial language usage from existing lexical-semantic resources.
STYLUS: A Resource for Systematically Derived Language Usage
We describe a resource derived through extraction of a set of argument realizations from an existing lexical-conceptual structure (LCS) Verb Database of 500 verb classes (containing a total of 9525 verb entries) to include information about realization of arguments for a range of different verb classes.
Characterization of Divergence in Impaired Speech of ALS Patients
Approximately 80{\%} to 95{\%} of patients with Amyotrophic Lateral Sclerosis (ALS) eventually develop speech impairments, such as defective articulation, slow laborious speech and hypernasality.
Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish
The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset.
Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network
Analytically, the performance of a PBSMT system is enhanced by using the conditional probabilities of phrase pairs computed by an LSTM encoder-decoder as an additional feature in the existing log-linear model.
Learning to Plan and Realize Separately for Open-Ended Dialogue Systems
Achieving true human-like ability to conduct a conversation remains an elusive goal for open-ended dialogue systems.
BeSt: The Belief and Sentiment Corpus
We present the BeSt corpus, which records cognitive state: who believes what (i. e., factuality), and who has what sentiment towards what.
