• Corpus ID: 64512972

Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

@article{Eshky2012ProceedingsOT,
  title={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
  author={Aciel Eshky and Ben Allison and M. Steedman},
  journal={The Association for Computational Linguistics},
  year={2012}
}
Descriptive Naming & Summarization of large text using Topic Model-A Survey
TLDR
This paper is analyzing several techniques to evaluate Topic Model and focuses on uncovering the thematic structure of a corpus of document that will help in document classification and for compact document topic representation.
Relation Extraction Using Distant Supervision
TLDR
A survey of relation extraction methods that leverage pre-existing structured or semi-structured data to guide the extraction process and introduces a taxonomy of existing methods and describes distant supervision approaches in detail.
Scalable Distributed Semantic Network for knowledge management in cyber physical system
Semantic concept model using Wikipedia semantic features
TLDR
A semantic concept model that incorporates different types of semantic features extracting from Wikipedia that achieves promising results in three tasks and outperforms the baseline methods in most of the evaluation datasets implies that incorporation of explicit and implicit semantic features is useful for representing semantics of concepts in Wikipedia.
A pragmatic guide to geoparsing evaluation
TLDR
A new framework describing the task, metrics and data used to compare state-of-the-art systems and proposing a fine-grained Pragmatic Taxonomy of Toponyms with implications for Named Entity Recognition (NER) and beyond is introduced.
Relation Extraction Using Distant Supervision: a Survey
TLDR
This work presents a survey of relation extraction methods that leverage pre-existing structured or semi-structured data to guide the extraction process, introducing a taxonomy of existing methods and describing distant supervision approaches in detail.
Structured query construction via knowledge graph embedding
TLDR
This paper proposes a novel framework that first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs and uses the learned embedding representations of the knowledge graph to compute the query structure and assemble vertices/edges into the target query.
Conceptualization topic modeling
TLDR
The results show that the proposed models significantly outperform the baselines in terms of case study and perplexity, which means the new assumption is more reasonable than traditional one.
Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus
TLDR
A novel, unsupervised algorithm for automatically learning an IS-A taxonomy from scratch by analyzing a given text corpus is proposed and uses a novel graph-based algorithm to detect and remove incorrect is-a relations from a taxonomy.
CLIP-Event: Connecting Text and Images with Event Structures
TLDR
A contrastive learning framework to enforce vision-language pretraining models to comprehend events and associated argument (par-ticipant) roles is proposed, which takes advantage of text information extraction technologies to obtain event structural knowledge, and utilizes multiple prompt functions to contrast difficult negative descriptions by manipulating event structures.
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