27 research outputs found
Argumentation Mining System for Corpus-based Discourse Analysis based on Structured Arguments
An Argumentation mining system can analyze a large volume of text data through a variety of sources. Nowadays it is highly useful in the areas of business, economics, and finance with digital marketing being the most promising field along with social media. It is the study of corpus-based discourse analysis that involves the automatic identification of argumentative structure in text. Initially, AM talks about extracting structured arguments from natural text, often unstructured or noisy text. Theoretical approaches of AM and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable of adjusting to argumentation conditions that exist in social media. In this scenario it is a very challenging argumentation scheme able to identify the distinct sub-task and capture the needs of social media text, revealing the need for adopting a more flexible and extensible framework. Corpus-based Machine Learning of linguistic annotations has enabled researchers to identify repetitive linguistic patterns of language use and to uncover hidden meaning in all areas of Natural Language Processing
Multilingual Universal Sentence Encoder for Semantic Retrieval
We introduce two pre-trained retrieval focused multilingual sentence encoding
models, respectively based on the Transformer and CNN model architectures. The
models embed text from 16 languages into a single semantic space using a
multi-task trained dual-encoder that learns tied representations using
translation based bridge tasks (Chidambaram al., 2018). The models provide
performance that is competitive with the state-of-the-art on: semantic
retrieval (SR), translation pair bitext retrieval (BR) and retrieval question
answering (ReQA). On English transfer learning tasks, our sentence-level
embeddings approach, and in some cases exceed, the performance of monolingual,
English only, sentence embedding models. Our models are made available for
download on TensorFlow Hub.Comment: 6 pages, 6 tables, 2 listings, and 1 figur
A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining
User-generated content from social media is produced in many languages,
making it technically challenging to compare the discussed themes from one
domain across different cultures and regions. It is relevant for domains in a
globalized world, such as market research, where people from two nations and
markets might have different requirements for a product. We propose a simple,
modern, and effective method for building a single topic model with sentiment
analysis capable of covering multiple languages simultanteously, based on a
pre-trained state-of-the-art deep neural network for natural language
understanding. To demonstrate its feasibility, we apply the model to newspaper
articles and user comments of a specific domain, i.e., organic food products
and related consumption behavior. The themes match across languages.
Additionally, we obtain an high proportion of stable and domain-relevant
topics, a meaningful relation between topics and their respective textual
contents, and an interpretable representation for social media documents.
Marketing can potentially benefit from our method, since it provides an
easy-to-use means of addressing specific customer interests from different
market regions around the globe. For reproducibility, we provide the code,
data, and results of our study.Comment: 10 pages, 2 tables, 5 figures, full paper, peer-reviewed, published
at KDIR/IC3k 2021 conferenc