412 research outputs found
Cross-language Information Retrieval
Two key assumptions shape the usual view of ranked retrieval: (1) that the
searcher can choose words for their query that might appear in the documents
that they wish to see, and (2) that ranking retrieved documents will suffice
because the searcher will be able to recognize those which they wished to find.
When the documents to be searched are in a language not known by the searcher,
neither assumption is true. In such cases, Cross-Language Information Retrieval
(CLIR) is needed. This chapter reviews the state of the art for CLIR and
outlines some open research questions.Comment: 49 pages, 0 figure
UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference
Recent advances in distributed language modeling have led to large
performance increases on a variety of natural language processing (NLP) tasks.
However, it is not well understood how these methods may be augmented by
knowledge-based approaches. This paper compares the performance and internal
representation of an Enhanced Sequential Inference Model (ESIM) between three
experimental conditions based on the representation method: Bidirectional
Encoder Representations from Transformers (BERT), Embeddings of Semantic
Predications (ESP), or Cui2Vec. The methods were evaluated on the Medical
Natural Language Inference (MedNLI) subtask of the MEDIQA 2019 shared task.
This task relied heavily on semantic understanding and thus served as a
suitable evaluation set for the comparison of these representation methods
Towards Robust Word Embeddings for Noisy Texts
[Abstract] Research on word embeddings has mainly focused on improving their performance on standard corpora, disregarding the difficulties posed by noisy texts in the form of tweets and other types of non-standard writing from social media. In this work, we propose a simple extension to the skipgram model in which we introduce the concept of bridge-words, which are artificial words added to the model to strengthen the similarity between standard words and their noisy variants. Our new embeddings outperform baseline models on noisy texts on a wide range of evaluation tasks,
both intrinsic and extrinsic, while retaining a good performance on standard texts. To the best of our knowledge, this is the first explicit approach at dealing with these types of noisy texts at the word embedding level that goes beyond the support for out-of-vocabulary words.Ministerio de EconomĂa, Industria y Competitividad. MINECO; TIN2017-85160-C2-2-RMinisterio de EconomĂa, Industria y Competitividad. MINECO; TIN2017-85160-C2-1-REuropean Social Fund. ESF; BES-2015-073768Xunta de Galicia; ED431D 2017/12Xunta de Galicia; ED431B 2017/01Xunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G/0
Social Search: retrieving information in Online Social Platforms -- A Survey
Social Search research deals with studying methodologies exploiting social
information to better satisfy user information needs in Online Social Media
while simplifying the search effort and consequently reducing the time spent
and the computational resources utilized. Starting from previous studies, in
this work, we analyze the current state of the art of the Social Search area,
proposing a new taxonomy and highlighting current limitations and open research
directions. We divide the Social Search area into three subcategories, where
the social aspect plays a pivotal role: Social Question&Answering, Social
Content Search, and Social Collaborative Search. For each subcategory, we
present the key concepts and selected representative approaches in the
literature in greater detail. We found that, up to now, a large body of studies
model users' preferences and their relations by simply combining social
features made available by social platforms. It paves the way for significant
research to exploit more structured information about users' social profiles
and behaviors (as they can be inferred from data available on social platforms)
to optimize their information needs further
Quantum Theory and Conceptuality: Matter, Stories, Semantics and Space-Time
We elaborate the new interpretation of quantum theory that we recently
proposed, according to which quantum particles are considered conceptual
entities mediating between pieces of ordinary matter which are considered to
act as memory structures for them. Our aim is to identify what is the
equivalent for the human cognitive realm of what physical space-time is for the
realm of quantum particles and ordinary matter. For this purpose, we identify
the notion of 'story' as the equivalent within the human cognitive realm of
what ordinary matter is in the physical quantum realm, and analyze the role
played by the logical connectives of disjunction and conjunction with respect
to the notion of locality. Similarly to what we have done in earlier
investigations on this new quantum interpretation, we use the specific
cognitive environment of the World-Wide Web to elucidate the comparisons we
make between the human cognitive realm and the physical quantum realm.Comment: 14 page
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