9,542 research outputs found
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
User information needs vary significantly across different tasks, and
therefore their queries will also differ considerably in their expressiveness
and semantics. Many studies have been proposed to model such query diversity by
obtaining query types and building query-dependent ranking models. These
studies typically require either a labeled query dataset or clicks from
multiple users aggregated over the same document. These techniques, however,
are not applicable when manual query labeling is not viable, and aggregated
clicks are unavailable due to the private nature of the document collection,
e.g., in email search scenarios. In this paper, we study how to obtain query
type in an unsupervised fashion and how to incorporate this information into
query-dependent ranking models. We first develop a hierarchical clustering
algorithm based on truncated SVD and varimax rotation to obtain coarse-to-fine
query types. Then, we study three query-dependent ranking models, including two
neural models that leverage query type information as additional features, and
one novel multi-task neural model that views query type as the label for the
auxiliary query cluster prediction task. This multi-task model is trained to
simultaneously rank documents and predict query types. Our experiments on tens
of millions of real-world email search queries demonstrate that the proposed
multi-task model can significantly outperform the baseline neural ranking
models, which either do not incorporate query type information or just simply
feed query type as an additional feature.Comment: CIKM 201
Russian word sense induction by clustering averaged word embeddings
The paper reports our participation in the shared task on word sense
induction and disambiguation for the Russian language (RUSSE-2018). Our team
was ranked 2nd for the wiki-wiki dataset (containing mostly homonyms) and 5th
for the bts-rnc and active-dict datasets (containing mostly polysemous words)
among all 19 participants.
The method we employed was extremely naive. It implied representing contexts
of ambiguous words as averaged word embedding vectors, using off-the-shelf
pre-trained distributional models. Then, these vector representations were
clustered with mainstream clustering techniques, thus producing the groups
corresponding to the ambiguous word senses. As a side result, we show that word
embedding models trained on small but balanced corpora can be superior to those
trained on large but noisy data - not only in intrinsic evaluation, but also in
downstream tasks like word sense induction.Comment: Proceedings of the 24rd International Conference on Computational
Linguistics and Intellectual Technologies (Dialogue-2018
Supervised Typing of Big Graphs using Semantic Embeddings
We propose a supervised algorithm for generating type embeddings in the same
semantic vector space as a given set of entity embeddings. The algorithm is
agnostic to the derivation of the underlying entity embeddings. It does not
require any manual feature engineering, generalizes well to hundreds of types
and achieves near-linear scaling on Big Graphs containing many millions of
triples and instances by virtue of an incremental execution. We demonstrate the
utility of the embeddings on a type recommendation task, outperforming a
non-parametric feature-agnostic baseline while achieving 15x speedup and
near-constant memory usage on a full partition of DBpedia. Using
state-of-the-art visualization, we illustrate the agreement of our
extensionally derived DBpedia type embeddings with the manually curated domain
ontology. Finally, we use the embeddings to probabilistically cluster about 4
million DBpedia instances into 415 types in the DBpedia ontology.Comment: 6 pages, to be published in Semantic Big Data Workshop at ACM, SIGMOD
2017; extended version in preparation for Open Journal of Semantic Web (OJSW
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
Embedding Words and Senses Together via Joint Knowledge-Enhanced Training
Word embeddings are widely used in Nat-ural Language Processing, mainly due totheir success in capturing semantic infor-mation from massive corpora. However,their creation process does not allow thedifferent meanings of a word to be auto-matically separated, as it conflates theminto a single vector. We address this issueby proposing a new model which learnsword and sense embeddings jointly. Ourmodel exploits large corpora and knowl-edge from semantic networks in order toproduce a unified vector space of wordand sense embeddings. We evaluate themain features of our approach both qual-itatively and quantitatively in a variety oftasks, highlighting the advantages of theproposed method in comparison to state-of-the-art word- and sense-based models
WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking
We present WISER, a new semantic search engine for expert finding in
academia. Our system is unsupervised and it jointly combines classical language
modeling techniques, based on text evidences, with the Wikipedia Knowledge
Graph, via entity linking.
WISER indexes each academic author through a novel profiling technique which
models her expertise with a small, labeled and weighted graph drawn from
Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the
author's publications, whereas the weighted edges express the semantic
relatedness among these entities computed via textual and graph-based
relatedness functions. Every node is also labeled with a relevance score which
models the pertinence of the corresponding entity to author's expertise, and is
computed by means of a proper random-walk calculation over that graph; and with
a latent vector representation which is learned via entity and other kinds of
structural embeddings derived from Wikipedia.
At query time, experts are retrieved by combining classic document-centric
approaches, which exploit the occurrences of query terms in the author's
documents, with a novel set of profile-centric scoring strategies, which
compute the semantic relatedness between the author's expertise and the query
topic via the above graph-based profiles.
The effectiveness of our system is established over a large-scale
experimental test on a standard dataset for this task. We show that WISER
achieves better performance than all the other competitors, thus proving the
effectiveness of modelling author's profile via our "semantic" graph of
entities. Finally, we comment on the use of WISER for indexing and profiling
the whole research community within the University of Pisa, and its application
to technology transfer in our University
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