10,298 research outputs found
Bringing Structure into Summaries: Crowdsourcing a Benchmark Corpus of Concept Maps
Concept maps can be used to concisely represent important information and
bring structure into large document collections. Therefore, we study a variant
of multi-document summarization that produces summaries in the form of concept
maps. However, suitable evaluation datasets for this task are currently
missing. To close this gap, we present a newly created corpus of concept maps
that summarize heterogeneous collections of web documents on educational
topics. It was created using a novel crowdsourcing approach that allows us to
efficiently determine important elements in large document collections. We
release the corpus along with a baseline system and proposed evaluation
protocol to enable further research on this variant of summarization.Comment: Published at EMNLP 201
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
From corpus-based collocation frequencies to readability measure
This paper provides a broad overview of three separate but related areas of research. Firstly, corpus linguistics is a growing discipline that applies analytical results from large language corpora to a wide variety of problems in linguistics and related disciplines. Secondly, readability research, as the name suggests, seeks to understand what makes texts more or less comprehensible to readers, and aims to apply this understanding to issues such as text rating and matching of texts to readers. Thirdly, collocation is a language feature that occurs when particular words are used frequently together for other than purely grammatical reasons. The intersection of these three aspects provides the basis for on-going research within the Department of Computer and Information Sciences at the University of Strathclyde and is the motivation for this overview. Specifically, we aim through analysis of collocation frequencies in major corpora, to afford valuable insight on the content of texts, which we believe will, in turn, provide a novel basis for estimating text readability
Performance of Latent Dirichlet Allocation with Different Topic and Document Structures
Topic modeling has been used widely to extract the structures (topics) in a collection (corpus) of documents. One popular method is the Latent Dirichlet Allocation (LDA). LDA assumes a Bayesian generative model with multinomial distributions of topics and vocabularies within the topics. The LDA model result (i.e., the number and types of topics in the corpus) depends on tuning parameters. Several methods, ad hoc or heuristic, have been proposed and analyzed for selecting these parameters. But all these methods have been developed using one or more real corpora. Unfortunately, with real corpora, the true number and types of topics are unknown and it is difficult to assess how well the data follow the assumptions of LDA. To address this issue, we developed a factorial simulation design to create corpora with known structure that varied on the following four factors: 1) number of topics, 2) proportions of topics in documents, 3) size of the vocabulary in topics, and 4) proportion of vocabulary that is contained in documents. Results suggest that the quality of LDA fitting depends on the document-topic distribution and the fitting performs the best when the document lengths are at least four times the vocabulary size. We have also proposed a pre-processing method that may be used to increase quality of the LDA result in some of the worst-case scenarios from the factorial simulation study
Patent Analytics Based on Feature Vector Space Model: A Case of IoT
The number of approved patents worldwide increases rapidly each year, which
requires new patent analytics to efficiently mine the valuable information
attached to these patents. Vector space model (VSM) represents documents as
high-dimensional vectors, where each dimension corresponds to a unique term.
While originally proposed for information retrieval systems, VSM has also seen
wide applications in patent analytics, and used as a fundamental tool to map
patent documents to structured data. However, VSM method suffers from several
limitations when applied to patent analysis tasks, such as loss of
sentence-level semantics and curse-of-dimensionality problems. In order to
address the above limitations, we propose a patent analytics based on feature
vector space model (FVSM), where the FVSM is constructed by mapping patent
documents to feature vectors extracted by convolutional neural networks (CNN).
The applications of FVSM for three typical patent analysis tasks, i.e., patents
similarity comparison, patent clustering, and patent map generation are
discussed. A case study using patents related to Internet of Things (IoT)
technology is illustrated to demonstrate the performance and effectiveness of
FVSM. The proposed FVSM can be adopted by other patent analysis studies to
replace VSM, based on which various big data learning tasks can be performed
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