A Novel Distributed Representation of News (DRNews) for Stock Market Predictions

In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions. With the merit of integrating contextual information and cross-documental knowledge, the DRNews model creates news vectors that describe both the semantic information and potential linkages among news events through an attributed news network. Two stock market prediction tasks, namely the short-term stock movement prediction and stock crises early warning, are implemented in the framework of the attention-based Long Short Term-Memory (LSTM) network. It is suggested that DRNews substantially enhances the results of both tasks comparing with five baselines of news embedding models. Further, the attention mechanism suggests that short-term stock trend and stock market crises both receive influences from daily news with the former demonstrates more critical responses on the information related to the stock market {\em per se}, whilst the latter draws more concerns on the banking sector and economic policies.Comment: 25 page

Leveraging Knowledge Graph Embeddings to Enhance Contextual Representations for Relation Extraction

Relation extraction task is a crucial and challenging aspect of Natural Language Processing. Several methods have surfaced as of late, exhibiting notable performance in addressing the task; however, most of these approaches rely on vast amounts of data from large-scale knowledge graphs or language models pretrained on voluminous corpora. In this paper, we hone in on the effective utilization of solely the knowledge supplied by a corpus to create a high-performing model. Our objective is to showcase that by leveraging the hierarchical structure and relational distribution of entities within a corpus without introducing external knowledge, a relation extraction model can achieve significantly enhanced performance. We therefore proposed a relation extraction approach based on the incorporation of pretrained knowledge graph embeddings at the corpus scale into the sentence-level contextual representation. We conducted a series of experiments which revealed promising and very interesting results for our proposed approach.The obtained results demonstrated an outperformance of our method compared to context-based relation extraction models.Comment: 15 pages, 1 figures, The 17th International Conference on Document Analysis and Recognitio

Comparative Analysis of Contextual Relation Extraction based on Deep Learning Models

Contextual Relation Extraction (CRE) is mainly used for constructing a knowledge graph with a help of ontology. It performs various tasks such as semantic search, query answering, and textual entailment. Relation extraction identifies the entities from raw texts and the relations among them. An efficient and accurate CRE system is essential for creating domain knowledge in the biomedical industry. Existing Machine Learning and Natural Language Processing (NLP) techniques are not suitable to predict complex relations from sentences that consist of more than two relations and unspecified entities efficiently. In this work, deep learning techniques have been used to identify the appropriate semantic relation based on the context from multiple sentences. Even though various machine learning models have been used for relation extraction, they provide better results only for binary relations, i.e., relations occurred exactly between the two entities in a sentence. Machine learning models are not suited for complex sentences that consist of the words that have various meanings. To address these issues, hybrid deep learning models have been used to extract the relations from complex sentence effectively. This paper explores the analysis of various deep learning models that are used for relation extraction.Comment: This Paper Presented in the International Conference on FOSS Approaches towards Computational Intelligence and Language TTechnolog on February 2023, Thiruvananthapura

Enriching Word Embeddings with Food Knowledge for Ingredient Retrieval

Smart assistants and recommender systems must deal with lots of information coming from different sources and having different formats. This is more frequent in text data, which presents increased variability and complexity, and is rather common for conversational assistants or chatbots. Moreover, this issue is very evident in the food and nutrition lexicon, where the semantics present increased variability, namely due to hypernyms and hyponyms. This work describes the creation of a set of word embeddings based on the incorporation of information from a food thesaurus - LanguaL - through retrofitting. The ingredients were classified according to three different facet label groups. Retrofitted embeddings seem to properly encode food-specific knowledge, as shown by an increase on accuracy as compared to generic embeddings (+23%, +10% and +31% per group). Moreover, a weighing mechanism based on TF-IDF was applied to embedding creation before retrofitting, also bringing an increase on accuracy (+5%, +9% and +5% per group). Finally, the approach has been tested with human users in an ingredient retrieval exercise, showing very positive evaluation (77.3% of the volunteer testers preferred this method over a string-based matching algorithm)

Focusing Knowledge-based Graph Argument Mining via Topic Modeling

Decision-making usually takes five steps: identifying the problem, collecting data, extracting evidence, identifying pro and con arguments, and making decisions. Focusing on extracting evidence, this paper presents a hybrid model that combines latent Dirichlet allocation and word embeddings to obtain external knowledge from structured and unstructured data. We study the task of sentence-level argument mining, as arguments mostly require some degree of world knowledge to be identified and understood. Given a topic and a sentence, the goal is to classify whether a sentence represents an argument in regard to the topic. We use a topic model to extract topic- and sentence-specific evidence from the structured knowledge base Wikidata, building a graph based on the cosine similarity between the entity word vectors of Wikidata and the vector of the given sentence. Also, we build a second graph based on topic-specific articles found via Google to tackle the general incompleteness of structured knowledge bases. Combining these graphs, we obtain a graph-based model which, as our evaluation shows, successfully capitalizes on both structured and unstructured data