Research in naturallanguageprocessing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant research papers and their contribution to the domain is a challengi...
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ISBN:
(纸本)9781954085701
Research in naturallanguageprocessing is making rapid advances, resulting in the publication of a large number of research papers. Finding relevant research papers and their contribution to the domain is a challenging problem. In this paper, we address this challenge via the SemEval 2021 Task 11: NLPContributiongraph, by developing a system for a research paper contributions-focused knowledge graph over naturallanguageprocessing literature. The task is divided into three sub-tasks: extracting contribution sentences that show important contributions in the research article, extracting phrases from the contribution sentences, and predicting the information units in the research article together with triplet formation from the phrases. The proposed system is agnostic to the subject domain and can be applied for building a knowledge graph for any area. We found that transformer-basedlanguage models can significantly improve existing techniques and utilized the SciBERT-based model. Our first sub-task uses Bidirectional LSTM (BiLSTM) stacked on top of SciBERT model layers, while the second sub-task uses Conditional Random Field (CRF) on top of SciBERT with BiLSTM. The third sub-task uses a combined SciBERT based neural approach with heuristics for information unit prediction and triplet formation from the phrases. Our system achieved F1 score of 0.38, 0.63 and 0.76 in end-to-end pipeline testing, phrase extraction testing and triplet extraction testing respectively.
This paper describes the topic-based Chinese message polarity classification system submitted by LCYS-TEAM at SIGHAN8-Task2. The system mainly includes two parts: 1) a graph-based ranking model integrating local and g...
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This work extends the study of Germann et al. (2010) in investigating the lexical organization of verbs. Particularly, we look at the influence of frequency on the process of lexical acquis ition and use. We examine d...
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Degree distributions for word forms cooccurrences for large Russian text collections are obtained. Two power laws fit the distributions pretty good, thus supporting Dorogovtsev-Mendes model for Russian. Few different ...
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Degree distributions for word forms cooccurrences for large Russian text collections are obtained. Two power laws fit the distributions pretty good, thus supporting Dorogovtsev-Mendes model for Russian. Few different Russian text collections were studied, and statistical errors are shown to be negligible. The model exponents for Russian are found to differ from those for English, the difference probably being due to the difference in the collections structure. On the contrary, the estimated size of the supposed kernel lexicon appeared to be almost the same for the both languages, thus supporting the idea of importance of word forms for a perceptual lexicon of a human.
Estimating the software projects' efforts developed by agile methods is important for project managers or technical leads. It provides a summary as a first view of how many hours and developers are required to com...
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ISBN:
(纸本)9781450393430
Estimating the software projects' efforts developed by agile methods is important for project managers or technical leads. It provides a summary as a first view of how many hours and developers are required to complete the tasks. There are research works on automatic predicting the software efforts, including Term Frequency Inverse Document Frequency (TFIDF) as the traditional approach for this problem. graph Neural Network is a new approach that has been applied in naturallanguageprocessing for text classification. The advantages of graph Neural Network are based on the ability to learn information via graph data structure, which has more representations such as the relationships between words compared to approaches of vectorizing sequence of words. In this paper, we show the potential and possible challenges of graph Neural Network text classification in story point level estimation. By the experiments, we show that the GNN Text Level Classification can achieve as high accuracy as about 80% for story points level classification, which is comparable to the traditional approach. We also analyze the GNN approach and point out several current disadvantages that the GNN approach can improve for this problem or other problems in software engineering.
Contextual advertising provides advertisers with the opportunity to target the context which is most relevant to their ads. The large variety of potential topics makes it very challenging to collect training documents...
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Corpus2graph is an open-source NLP-application-oriented Python package that generates a word co-occurrence network from a large corpus. It not only contains different built-in methods to preprocess words, analyze sent...
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Syntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and relate...
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ISBN:
(纸本)9781950737901
Syntactic relations are broadly used in many NLP tasks. For event detection, syntactic relation representations based on dependency tree can better capture the interrelations between candidate trigger words and related entities than sentence representations. But, existing studies only use first-order syntactic relations (i.e., the arcs) in dependency trees to identify trigger words. For this reason, this paper proposes a new method for event detection, which uses a dependency tree basedgraph convolution network with aggregative attention to explicitly model and aggregate multi-order syntactic representations in sentences. Experimental comparison with state-of-the-art baselines shows the superiority of the proposed method.
The proceedings contain 30 papers. The topics discussed include: graph-based alignment of narratives for automated neurological assessment;bootstrapping biomedical ontologies for scientific text using nell;semantic di...
ISBN:
(纸本)9781937284206
The proceedings contain 30 papers. The topics discussed include: graph-based alignment of narratives for automated neurological assessment;bootstrapping biomedical ontologies for scientific text using nell;semantic distance and terminology structuring methods for the detection of semantically close terms;temporal classification of medical events;analyzing patient records to establish if and when a patient suffered from a medical condition;alignment-HMM-based extraction of abbreviations from biomedical text;medical diagnosis lost in translation – analysis of uncertainty and negation expressions in English and Swedish clinical texts;and a hybrid stepwise approach for de-identifying person names in clinical documents.
BERT is a popular language model whose main pre-training task is to fill in the blank, i.e., predicting a word that was masked out of a sentence, based on the remaining words. In some applications, however, having an ...
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