85,480 research outputs found
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
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters
To bridge the gap between the capabilities of the state-of-the-art in factoid
question answering (QA) and what users ask, we need large datasets of real user
questions that capture the various question phenomena users are interested in,
and the diverse ways in which these questions are formulated. We introduce
ComQA, a large dataset of real user questions that exhibit different
challenging aspects such as compositionality, temporal reasoning, and
comparisons. ComQA questions come from the WikiAnswers community QA platform,
which typically contains questions that are not satisfactorily answerable by
existing search engine technology. Through a large crowdsourcing effort, we
clean the question dataset, group questions into paraphrase clusters, and
annotate clusters with their answers. ComQA contains 11,214 questions grouped
into 4,834 paraphrase clusters. We detail the process of constructing ComQA,
including the measures taken to ensure its high quality while making effective
use of crowdsourcing. We also present an extensive analysis of the dataset and
the results achieved by state-of-the-art systems on ComQA, demonstrating that
our dataset can be a driver of future research on QA.Comment: 11 pages, NAACL 201
Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon
This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for diļ¬erent languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are
extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which aļ¬ects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The diļ¬erent steps of the procedure (mapping, disambiguation, extraction, NE identiļ¬cation and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the systemās accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented
Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus
In Web search, entity-seeking queries often trigger a special Question
Answering (QA) system. It may use a parser to interpret the question to a
structured query, execute that on a knowledge graph (KG), and return direct
entity responses. QA systems based on precise parsing tend to be brittle: minor
syntax variations may dramatically change the response. Moreover, KG coverage
is patchy. At the other extreme, a large corpus may provide broader coverage,
but in an unstructured, unreliable form. We present AQQUCN, a QA system that
gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of
query syntax, between well-formed questions to short `telegraphic' keyword
sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals
from KGs and large corpora to directly rank KG entities, rather than commit to
one semantic interpretation of the query. AQQUCN models the ideal
interpretation as an unobservable or latent variable. Interpretations and
candidate entity responses are scored as pairs, by combining signals from
multiple convolutional networks that operate collectively on the query, KG and
corpus. On four public query workloads, amounting to over 8,000 queries with
diverse query syntax, we see 5--16% absolute improvement in mean average
precision (MAP), compared to the entity ranking performance of recent systems.
Our system is also competitive at entity set retrieval, almost doubling F1
scores for challenging short queries.Comment: Accepted to Information Retrieval Journa
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