245,982 research outputs found
Knowledge Representation and WordNets
Knowledge itself is a representation of âreal factsâ.
Knowledge is a logical model that presents facts from âthe real worldâ witch can be expressed in a formal language. Representation means the construction of a model of some part of reality.
Knowledge representation is contingent to both cognitive science and artificial intelligence. In cognitive science it expresses the way people store and process the information. In the AI field the goal is to store knowledge in such way that permits intelligent programs to represent information as nearly as possible to human intelligence.
Knowledge Representation is referred to the formal representation of knowledge intended to be processed and stored by computers and to draw conclusions from this knowledge.
Examples of applications are expert systems, machine translation systems, computer-aided maintenance systems and information retrieval systems (including database front-ends).knowledge, representation, ai models, databases, cams
Korean to English Translation Using Synchronous TAGs
It is often argued that accurate machine translation requires reference to
contextual knowledge for the correct treatment of linguistic phenomena such as
dropped arguments and accurate lexical selection. One of the historical
arguments in favor of the interlingua approach has been that, since it revolves
around a deep semantic representation, it is better able to handle the types of
linguistic phenomena that are seen as requiring a knowledge-based approach. In
this paper we present an alternative approach, exemplified by a prototype
system for machine translation of English and Korean which is implemented in
Synchronous TAGs. This approach is essentially transfer based, and uses
semantic feature unification for accurate lexical selection of polysemous
verbs. The same semantic features, when combined with a discourse model which
stores previously mentioned entities, can also be used for the recovery of
topicalized arguments. In this paper we concentrate on the translation of
Korean to English.Comment: ps file. 8 page
"Bilingual Expert" Can Find Translation Errors
Recent advances in statistical machine translation via the adoption of neural
sequence-to-sequence models empower the end-to-end system to achieve
state-of-the-art in many WMT benchmarks. The performance of such machine
translation (MT) system is usually evaluated by automatic metric BLEU when the
golden references are provided for validation. However, for model inference or
production deployment, the golden references are prohibitively available or
require expensive human annotation with bilingual expertise. In order to
address the issue of quality evaluation (QE) without reference, we propose a
general framework for automatic evaluation of translation output for most WMT
quality evaluation tasks. We first build a conditional target language model
with a novel bidirectional transformer, named neural bilingual expert model,
which is pre-trained on large parallel corpora for feature extraction. For QE
inference, the bilingual expert model can simultaneously produce the joint
latent representation between the source and the translation, and real-valued
measurements of possible erroneous tokens based on the prior knowledge learned
from parallel data. Subsequently, the features will further be fed into a
simple Bi-LSTM predictive model for quality evaluation. The experimental
results show that our approach achieves the state-of-the-art performance in the
quality estimation track of WMT 2017/2018.Comment: Accepted to AAAI 201
Knowledge Graphs and Knowledge Graph Embeddings
Knowledge graphs provide machines with structured knowledge of the world. Structured, machine-readable knowledge is necessary for a wide variety of artificial intelligence tasks such as search, translation, and recommender systems. These knowledge graphs can be embedded into a dense matrix representation for easier usage and storage. We first discuss knowledge graph components and knowledge base population to provide the necessary background knowledge. We then discuss popular methods of embedding knowledge graphs in chronological order. Lastly, we cover how knowledge graph embeddings improve both knowledge base population and a variety of artificial intelligence tasks
- âŠ