66,720 research outputs found
Neural Ranking Models with Weak Supervision
Despite the impressive improvements achieved by unsupervised deep neural
networks in computer vision and NLP tasks, such improvements have not yet been
observed in ranking for information retrieval. The reason may be the complexity
of the ranking problem, as it is not obvious how to learn from queries and
documents when no supervised signal is available. Hence, in this paper, we
propose to train a neural ranking model using weak supervision, where labels
are obtained automatically without human annotators or any external resources
(e.g., click data). To this aim, we use the output of an unsupervised ranking
model, such as BM25, as a weak supervision signal. We further train a set of
simple yet effective ranking models based on feed-forward neural networks. We
study their effectiveness under various learning scenarios (point-wise and
pair-wise models) and using different input representations (i.e., from
encoding query-document pairs into dense/sparse vectors to using word embedding
representation). We train our networks using tens of millions of training
instances and evaluate it on two standard collections: a homogeneous news
collection(Robust) and a heterogeneous large-scale web collection (ClueWeb).
Our experiments indicate that employing proper objective functions and letting
the networks to learn the input representation based on weakly supervised data
leads to impressive performance, with over 13% and 35% MAP improvements over
the BM25 model on the Robust and the ClueWeb collections. Our findings also
suggest that supervised neural ranking models can greatly benefit from
pre-training on large amounts of weakly labeled data that can be easily
obtained from unsupervised IR models.Comment: In proceedings of The 40th International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR2017
Graph-Embedding Empowered Entity Retrieval
In this research, we improve upon the current state of the art in entity
retrieval by re-ranking the result list using graph embeddings. The paper shows
that graph embeddings are useful for entity-oriented search tasks. We
demonstrate empirically that encoding information from the knowledge graph into
(graph) embeddings contributes to a higher increase in effectiveness of entity
retrieval results than using plain word embeddings. We analyze the impact of
the accuracy of the entity linker on the overall retrieval effectiveness. Our
analysis further deploys the cluster hypothesis to explain the observed
advantages of graph embeddings over the more widely used word embeddings, for
user tasks involving ranking entities
Learning to Rank Academic Experts in the DBLP Dataset
Expert finding is an information retrieval task that is concerned with the
search for the most knowledgeable people with respect to a specific topic, and
the search is based on documents that describe people's activities. The task
involves taking a user query as input and returning a list of people who are
sorted by their level of expertise with respect to the user query. Despite
recent interest in the area, the current state-of-the-art techniques lack in
principled approaches for optimally combining different sources of evidence.
This article proposes two frameworks for combining multiple estimators of
expertise. These estimators are derived from textual contents, from
graph-structure of the citation patterns for the community of experts, and from
profile information about the experts. More specifically, this article explores
the use of supervised learning to rank methods, as well as rank aggregation
approaches, for combing all of the estimators of expertise. Several supervised
learning algorithms, which are representative of the pointwise, pairwise and
listwise approaches, were tested, and various state-of-the-art data fusion
techniques were also explored for the rank aggregation framework. Experiments
that were performed on a dataset of academic publications from the Computer
Science domain attest the adequacy of the proposed approaches.Comment: Expert Systems, 2013. arXiv admin note: text overlap with
arXiv:1302.041
Role of Ranking Algorithms for Information Retrieval
As the use of web is increasing more day by day, the web users get easily
lost in the web's rich hyper structure. The main aim of the owner of the
website is to give the relevant information according their needs to the users.
We explained the Web mining is used to categorize users and pages by analyzing
user's behavior, the content of pages and then describe Web Structure mining.
This paper includes different Page Ranking algorithms and compares those
algorithms used for Information Retrieval. Different Page Rank based algorithms
like Page Rank (PR), WPR (Weighted Page Rank), HITS (Hyperlink Induced Topic
Selection), Distance Rank and EigenRumor algorithms are discussed and compared.
Simulation Interface has been designed for PageRank algorithm and Weighted
PageRank algorithm but PageRank is the only ranking algorithm on which Google
search engine works.Comment: Keywords: Page Rank, Web Mining, Web Structured Mining, Web Content
Minin
Improving Entity Retrieval on Structured Data
The increasing amount of data on the Web, in particular of Linked Data, has
led to a diverse landscape of datasets, which make entity retrieval a
challenging task. Explicit cross-dataset links, for instance to indicate
co-references or related entities can significantly improve entity retrieval.
However, only a small fraction of entities are interlinked through explicit
statements. In this paper, we propose a two-fold entity retrieval approach. In
a first, offline preprocessing step, we cluster entities based on the
\emph{x--means} and \emph{spectral} clustering algorithms. In the second step,
we propose an optimized retrieval model which takes advantage of our
precomputed clusters. For a given set of entities retrieved by the BM25F
retrieval approach and a given user query, we further expand the result set
with relevant entities by considering features of the queries, entities and the
precomputed clusters. Finally, we re-rank the expanded result set with respect
to the relevance to the query. We perform a thorough experimental evaluation on
the Billions Triple Challenge (BTC12) dataset. The proposed approach shows
significant improvements compared to the baseline and state of the art
approaches
Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application
We present two novel models of document coherence and their application to
information retrieval (IR). Both models approximate document coherence using
discourse entities, e.g. the subject or object of a sentence. Our first model
views text as a Markov process generating sequences of discourse entities
(entity n-grams); we use the entropy of these entity n-grams to approximate the
rate at which new information appears in text, reasoning that as more new words
appear, the topic increasingly drifts and text coherence decreases. Our second
model extends the work of Guinaudeau & Strube [28] that represents text as a
graph of discourse entities, linked by different relations, such as their
distance or adjacency in text. We use several graph topology metrics to
approximate different aspects of the discourse flow that can indicate
coherence, such as the average clustering or betweenness of discourse entities
in text. Experiments with several instantiations of these models show that: (i)
our models perform on a par with two other well-known models of text coherence
even without any parameter tuning, and (ii) reranking retrieval results
according to their coherence scores gives notable performance gains, confirming
a relation between document coherence and relevance. This work contributes two
novel models of document coherence, the application of which to IR complements
recent work in the integration of document cohesiveness or comprehensibility to
ranking [5, 56]
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
- …