17,003 research outputs found
Using graph distances for named-entity linking
Entity-linking is a natural-language-processing task that consists in identifying strings of text that refer to a particular item in some reference knowledge base. When the knowledge base is Wikipedia, the problem is also referred to as wikification (in this case, items are Wikipedia articles). Entity-linking consists conceptually of many different phases: identifying the portions of text that may refer to an entity (sometimes called "entity detection"), determining a set of concepts (candidates) from the knowledge base that may match each such portion, and choosing one candidate for each set; the latter step, known as candidate selection, is the phase on which this paper focuses. One instance of candidate selection can be formalized as an optimization problem on the underlying concept graph, where the quantity to be optimized is the average distance between the selected items. Inspired by this application, we define a new graph problem which is a natural variant of the Maximum Capacity Representative Set. We prove that our problem is NP-hard for general graphs; we propose several heuristics trying to optimize similar easier objective functions; we show experimentally how these approaches perform with respect to some baselines on a real-world dataset. Finally, in the appendix, we show an exact linear time algorithm that works under some more restrictive assumptions
Entity-Linking via Graph-Distance Minimization
Entity-linking is a natural-language-processing task that consists in
identifying the entities mentioned in a piece of text, linking each to an
appropriate item in some knowledge base; when the knowledge base is Wikipedia,
the problem comes to be known as wikification (in this case, items are
wikipedia articles). One instance of entity-linking can be formalized as an
optimization problem on the underlying concept graph, where the quantity to be
optimized is the average distance between chosen items. Inspired by this
application, we define a new graph problem which is a natural variant of the
Maximum Capacity Representative Set. We prove that our problem is NP-hard for
general graphs; nonetheless, under some restrictive assumptions, it turns out
to be solvable in linear time. For the general case, we propose two heuristics:
one tries to enforce the above assumptions and another one is based on the
notion of hitting distance; we show experimentally how these approaches perform
with respect to some baselines on a real-world dataset.Comment: In Proceedings GRAPHITE 2014, arXiv:1407.7671. The second and third
authors were supported by the EU-FET grant NADINE (GA 288956
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
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines
TopExNet: Entity-Centric Network Topic Exploration in News Streams
The recent introduction of entity-centric implicit network representations of
unstructured text offers novel ways for exploring entity relations in document
collections and streams efficiently and interactively. Here, we present
TopExNet as a tool for exploring entity-centric network topics in streams of
news articles. The application is available as a web service at
https://topexnet.ifi.uni-heidelberg.de/ .Comment: Published in Proceedings of the Twelfth ACM International Conference
on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February
11-15, 201
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
- …