554 research outputs found
Towards Application of Speech Act Theory to Opinion Mining
Towards the Application of Speech Act Theory to Opinion Mining
The paper refers to the pragmatics’ perspective on opinion mining in Polish and English, inspired by the discrepancy between the coverage of sentiment analysis and the market demand. An analysis of speech acts expressed in opinion texts reveals that almost half of all opinions include ways of indirect evaluation that might not get extracted while applying traditional methods of sentiment analysis based on direct evaluative vocabulary and polarity lexicons. Coding of sentiment with respect to speech acts could vastly broaden data mining results within an NLP-system.
O zastosowaniu teorii aktów mowy w ekstrakcji danych z tekstów opinii internetowych
Jedno z aktualnych zagadnień językoznawstwa komputerowego, jakim jest automatyczne badanie wydźwięku wypowiedzi, nie uwzględniło dotychczas w wystarczającym stopniu pragmatyki językoznawczej, np. aktów mowy Austina (1961) i Searla (1969), a zatem również implicytnych sposobów wyrażania ewaluacji. Tymczasem podejście od pragmatyki ku konstrukcjom przełożonym na reguły programistyczne umożliwiłoby nie tylko szersze spojrzenie na analizę sentymentu, ale też zbliżyłoby automat do sposobu, w jaki odbiera go człowiek. W szczególności chodzi tu sposoby wyrażania (nie)zadowolenia wykraczające poza poziom leksykalny (bez nacechowanej negatywnie leksyki), typu Nigdy więcej tam nie pójdę.
Artykuł prezentuje: 1. aktualne podejścia do analizy wydźwięku w lingwistyce komputerowej, 2. propozycję zastosowania podejścia pragmatycznego, 3. wyniki badania próbki tekstów opinii internetowych pod kątem występowania w nich aktów mowy, 4. propozycję utworzenia reguł ekstrakcji danych na ich podstawie. Zaprezentowane podejście zakłada hipotezę wtórnej oralności, czyli tego, że język opinii jest zapisanym językiem mówionym
The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources
We introduce the STEM (Science, Technology, Engineering, and Medicine)
Dataset for Scientific Entity Extraction, Classification, and Resolution,
version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to
provide a benchmark for the evaluation of scientific entity extraction,
classification, and resolution tasks in a domain-independent fashion. It
comprises abstracts in 10 STEM disciplines that were found to be the most
prolific ones on a major publishing platform. We describe the creation of such
a multidisciplinary corpus and highlight the obtained findings in terms of the
following features: 1) a generic conceptual formalism for scientific entities
in a multidisciplinary scientific context; 2) the feasibility of the
domain-independent human annotation of scientific entities under such a generic
formalism; 3) a performance benchmark obtainable for automatic extraction of
multidisciplinary scientific entities using BERT-based neural models; 4) a
delineated 3-step entity resolution procedure for human annotation of the
scientific entities via encyclopedic entity linking and lexicographic word
sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic
links and lexicographic senses for our entities. Our findings cumulatively
indicate that human annotation and automatic learning of multidisciplinary
scientific concepts as well as their semantic disambiguation in a wide-ranging
setting as STEM is reasonable.Comment: Published in LREC 2020. Publication URL
https://www.aclweb.org/anthology/2020.lrec-1.268/; Dataset DOI
https://doi.org/10.25835/001754
The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources
We introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable
Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization
Abstractive text summarization is surging with the number of training samples
to cater to the needs of the deep learning models. These models tend to exploit
the training data representations to attain superior performance by improving
the quantitative element of the resultant summary. However, increasing the size
of the training set may not always be the ideal solution to maximize the
performance, and therefore, a need to revisit the quality of training samples
and the learning protocol of deep learning models is a must. In this paper, we
aim to discretize the vector space of the abstractive text summarization models
to understand the characteristics learned between the input embedding space and
the models' encoder space. We show that deep models fail to capture the
diversity of the input space. Further, the distribution of data points on the
encoder space indicates that an unchecked increase in the training samples does
not add value; rather, a tear-down of data samples is highly needed to make the
models focus on variability and faithfulness. We employ clustering techniques
to learn the diversity of a model's sample space and how data points are mapped
from the embedding space to the encoder space and vice versa. Further, we
devise a metric to filter out redundant data points to make the model more
robust and less data hungry. We benchmark our proposed method using
quantitative metrics, such as Rouge, and qualitative metrics, such as
BERTScore, FEQA and Pyramid score. We also quantify the reasons that inhibit
the models from learning the diversity from the varied input samples
Computational Sociolinguistics: A Survey
Language is a social phenomenon and variation is inherent to its social
nature. Recently, there has been a surge of interest within the computational
linguistics (CL) community in the social dimension of language. In this article
we present a survey of the emerging field of "Computational Sociolinguistics"
that reflects this increased interest. We aim to provide a comprehensive
overview of CL research on sociolinguistic themes, featuring topics such as the
relation between language and social identity, language use in social
interaction and multilingual communication. Moreover, we demonstrate the
potential for synergy between the research communities involved, by showing how
the large-scale data-driven methods that are widely used in CL can complement
existing sociolinguistic studies, and how sociolinguistics can inform and
challenge the methods and assumptions employed in CL studies. We hope to convey
the possible benefits of a closer collaboration between the two communities and
conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication:
18th February, 201
A Hybrid Environment for Syntax-Semantic Tagging
The thesis describes the application of the relaxation labelling algorithm to
NLP disambiguation. Language is modelled through context constraint inspired on
Constraint Grammars. The constraints enable the use of a real value statind
"compatibility". The technique is applied to POS tagging, Shallow Parsing and
Word Sense Disambigation. Experiments and results are reported. The proposed
approach enables the use of multi-feature constraint models, the simultaneous
resolution of several NL disambiguation tasks, and the collaboration of
linguistic and statistical models.Comment: PhD Thesis. 120 page
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