2,982 research outputs found
Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution
This paper presents the use of probabilistic class-based lexica for
disambiguation in target-word selection. Our method employs minimal but precise
contextual information for disambiguation. That is, only information provided
by the target-verb, enriched by the condensed information of a probabilistic
class-based lexicon, is used. Induction of classes and fine-tuning to verbal
arguments is done in an unsupervised manner by EM-based clustering techniques.
The method shows promising results in an evaluation on real-world translations.Comment: 7 pages, uses colacl.st
Neural Collective Entity Linking
Entity Linking aims to link entity mentions in texts to knowledge bases, and
neural models have achieved recent success in this task. However, most existing
methods rely on local contexts to resolve entities independently, which may
usually fail due to the data sparsity of local information. To address this
issue, we propose a novel neural model for collective entity linking, named as
NCEL. NCEL applies Graph Convolutional Network to integrate both local
contextual features and global coherence information for entity linking. To
improve the computation efficiency, we approximately perform graph convolution
on a subgraph of adjacent entity mentions instead of those in the entire text.
We further introduce an attention scheme to improve the robustness of NCEL to
data noise and train the model on Wikipedia hyperlinks to avoid overfitting and
domain bias. In experiments, we evaluate NCEL on five publicly available
datasets to verify the linking performance as well as generalization ability.
We also conduct an extensive analysis of time complexity, the impact of key
modules, and qualitative results, which demonstrate the effectiveness and
efficiency of our proposed method.Comment: 12 pages, 3 figures, COLING201
Thematic Annotation: extracting concepts out of documents
Contrarily to standard approaches to topic annotation, the technique used in
this work does not centrally rely on some sort of -- possibly statistical --
keyword extraction. In fact, the proposed annotation algorithm uses a large
scale semantic database -- the EDR Electronic Dictionary -- that provides a
concept hierarchy based on hyponym and hypernym relations. This concept
hierarchy is used to generate a synthetic representation of the document by
aggregating the words present in topically homogeneous document segments into a
set of concepts best preserving the document's content.
This new extraction technique uses an unexplored approach to topic selection.
Instead of using semantic similarity measures based on a semantic resource, the
later is processed to extract the part of the conceptual hierarchy relevant to
the document content. Then this conceptual hierarchy is searched to extract the
most relevant set of concepts to represent the topics discussed in the
document. Notice that this algorithm is able to extract generic concepts that
are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure
Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All
Collective entity disambiguation aims to jointly resolve multiple mentions by
linking them to their associated entities in a knowledge base. Previous works
are primarily based on the underlying assumption that entities within the same
document are highly related. However, the extend to which these mentioned
entities are actually connected in reality is rarely studied and therefore
raises interesting research questions. For the first time, we show that the
semantic relationships between the mentioned entities are in fact less dense
than expected. This could be attributed to several reasons such as noise, data
sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE,
a new tree-based objective for the entity disambiguation problem. The key
intuition behind MINTREE is the concept of coherence relaxation which utilizes
the weight of a minimum spanning tree to measure the coherence between
entities. Based on this new objective, we design a novel entity disambiguation
algorithms which we call Pair-Linking. Instead of considering all the given
mentions, Pair-Linking iteratively selects a pair with the highest confidence
at each step for decision making. Via extensive experiments, we show that our
approach is not only more accurate but also surprisingly faster than many
state-of-the-art collective linking algorithms
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
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
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