2,649 research outputs found
Adaptive Representations for Tracking Breaking News on Twitter
Twitter is often the most up-to-date source for finding and tracking breaking
news stories. Therefore, there is considerable interest in developing filters
for tweet streams in order to track and summarize stories. This is a
non-trivial text analytics task as tweets are short, and standard retrieval
methods often fail as stories evolve over time. In this paper we examine the
effectiveness of adaptive mechanisms for tracking and summarizing breaking news
stories. We evaluate the effectiveness of these mechanisms on a number of
recent news events for which manually curated timelines are available.
Assessments based on ROUGE metrics indicate that an adaptive approaches are
best suited for tracking evolving stories on Twitter.Comment: 8 Pag
Parameterized Neural Network Language Models for Information Retrieval
Information Retrieval (IR) models need to deal with two difficult issues,
vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to
the difficulty of retrieving relevant documents that do not contain exact query
terms but semantically related terms. Term dependencies refers to the need of
considering the relationship between the words of the query when estimating the
relevance of a document. A multitude of solutions has been proposed to solve
each of these two problems, but no principled model solve both. In parallel, in
the last few years, language models based on neural networks have been used to
cope with complex natural language processing tasks like emotion and paraphrase
detection. Although they present good abilities to cope with both term
dependencies and vocabulary mismatch problems, thanks to the distributed
representation of words they are based upon, such models could not be used
readily in IR, where the estimation of one language model per document (or
query) is required. This is both computationally unfeasible and prone to
over-fitting. Based on a recent work that proposed to learn a generic language
model that can be modified through a set of document-specific parameters, we
explore use of new neural network models that are adapted to ad-hoc IR tasks.
Within the language model IR framework, we propose and study the use of a
generic language model as well as a document-specific language model. Both can
be used as a smoothing component, but the latter is more adapted to the
document at hand and has the potential of being used as a full document
language model. We experiment with such models and analyze their results on
TREC-1 to 8 datasets
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
Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration
Cross-language information retrieval (CLIR), where queries and documents are
in different languages, has of late become one of the major topics within the
information retrieval community. This paper proposes a Japanese/English CLIR
system, where we combine a query translation and retrieval modules. We
currently target the retrieval of technical documents, and therefore the
performance of our system is highly dependent on the quality of the translation
of technical terms. However, the technical term translation is still
problematic in that technical terms are often compound words, and thus new
terms are progressively created by combining existing base words. In addition,
Japanese often represents loanwords based on its special phonogram.
Consequently, existing dictionaries find it difficult to achieve sufficient
coverage. To counter the first problem, we produce a Japanese/English
dictionary for base words, and translate compound words on a word-by-word
basis. We also use a probabilistic method to resolve translation ambiguity. For
the second problem, we use a transliteration method, which corresponds words
unlisted in the base word dictionary to their phonetic equivalents in the
target language. We evaluate our system using a test collection for CLIR, and
show that both the compound word translation and transliteration methods
improve the system performance
Finding Semantically Related Videos in Closed Collections
Modern newsroom tools offer advanced functionality for automatic and semi-automatic content collection from the web and social media sources to accompany news stories. However, the content collected in this way often tends to be unstructured and may include irrelevant items. An important step in the verification process is to organize this content, both with respect to what it shows, and with respect to its origin. This chapter presents our efforts in this direction, which resulted in two components. One aims to detect semantic concepts in video shots, to help annotation and organization of content collections. We implement a system based on deep learning, featuring a number of advances and adaptations of existing algorithms to increase performance for the task. The other component aims to detect logos in videos in order to identify their provenance. We present our progress from a keypoint-based detection system to a system based on deep learning
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