20,802 research outputs found
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
Proceedings of the Workshop Semantic Content Acquisition and Representation (SCAR) 2007
This is the proceedings of the Workshop on Semantic Content Acquisition and Representation, held in conjunction with NODALIDA 2007, on May 24 2007 in Tartu, Estonia.</p
Learning to Embed Words in Context for Syntactic Tasks
We present models for embedding words in the context of surrounding words.
Such models, which we refer to as token embeddings, represent the
characteristics of a word that are specific to a given context, such as word
sense, syntactic category, and semantic role. We explore simple, efficient
token embedding models based on standard neural network architectures. We learn
token embeddings on a large amount of unannotated text and evaluate them as
features for part-of-speech taggers and dependency parsers trained on much
smaller amounts of annotated data. We find that predictors endowed with token
embeddings consistently outperform baseline predictors across a range of
context window and training set sizes.Comment: Accepted by ACL 2017 Repl4NLP worksho
Political Text Scaling Meets Computational Semantics
During the last fifteen years, automatic text scaling has become one of the
key tools of the Text as Data community in political science. Prominent text
scaling algorithms, however, rely on the assumption that latent positions can
be captured just by leveraging the information about word frequencies in
documents under study. We challenge this traditional view and present a new,
semantically aware text scaling algorithm, SemScale, which combines recent
developments in the area of computational linguistics with unsupervised
graph-based clustering. We conduct an extensive quantitative analysis over a
collection of speeches from the European Parliament in five different languages
and from two different legislative terms, and show that a scaling approach
relying on semantic document representations is often better at capturing known
underlying political dimensions than the established frequency-based (i.e.,
symbolic) scaling method. We further validate our findings through a series of
experiments focused on text preprocessing and feature selection, document
representation, scaling of party manifestos, and a supervised extension of our
algorithm. To catalyze further research on this new branch of text scaling
methods, we release a Python implementation of SemScale with all included data
sets and evaluation procedures.Comment: Updated version - accepted for Transactions on Data Science (TDS
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