2022 Papers
Direct Relation Detection for Knowledge-based Question Answering
Abbas Shahini , Reza Ramezani, Hadi Khosravi, Mohammad ali Nematbakhsha
This study addresses the problem of relation detection for answering single-relation factoid questions over knowledge bases (KBs). In this kind of questions, the answer is obtained from a single KB fact in the form of subject-predicate-object. Conventional fact extraction methods have two steps: entity linking and relation detection, in which the output of the entity linking is used by the relation detection step to first find candidate relations, and then choose the best relation from candidate relations. Such methods have difficulties with the relation detection if there is an error or ambiguity in the entity linking step. read more
Adaptive Reinforcement-Based Genetic Algorithm for Combinatorial Optimization
Combinatorial optimization is the procedure of optimizing an objective function over the discrete configuration space. A genetic algorithm (GA) has been applied successfully to solve various NP-complete combinatorial optimization problems. One of the most challenging problems in applying GA is selecting mutation operators and associated probabilities for each situation. GA uses just one type of mutation operator with a specified probability in the basic form. read more
PerAnSel: A Novel Deep Neural Network-Based System for Persian Question Answering
Jamshid Mozafari , Arefeh Kazemi , Parham Moradi , and Mohammad Ali Nematbakhsh
Question answering (QA) systems have attracted considerable attention in recent years. They receive the user’s questions in natural language and respond to them with precise answers. Most of the works on QA were initially proposed for the English language, but some research studies have recently been performed on non-English languages. Answer selection (AS) is a critical component in QA systems. To the best of our knowledge, there is no research on AS for the Persian language. read more
PersianQuAD: The Native Question Answering Dataset for the Persian Language
Jamshid Mozafari , Arefeh Kazemi , and Mohammad Ali Nematbakhsh
Developing Question Answering systems (QA) is one of the main goals in Artificial Intelligence. With the advent of Deep Learning (DL) techniques, QA systems have witnessed significant advances. Although DL performs very well on QA, it requires a considerable amount of annotated data for training. Many annotated datasets have been built for the QA task; most of them are exclusively in English. In order to address the need for a high-quality QA dataset in the Persian language, we present PersianQuAD, the native QA dataset for the Persian language. read more
SParseQA: Sequential word reordering and parsing for answering complex natural language questions over knowledge graphs
Mahdi Bakhshia, Mohammad Ali Nematbakhsh, Mehran Mohsen zadeha, and Amir Masoud Rahmani
One of the effective approaches for answering natural language questions (NLQs) over knowledge graphs consists of two main stages. It first creates a query graph based on the NLQ and then matches this graph over the knowledge graph to construct a structured query. An obstacle in the first stage is the need to build question interpretations with candidate resources, even if some implicit phrases exist in the sentence. In the second stage, a serious problem is to map diverse NLQ relations to their corresponding predicates. read more
Sign prediction in sparse social networks using clustering and collaborative filtering
Mina Nasrazadani, Afsaneh Fatemi, and Mohammad Ali Nematbakhsh
Today, social networks have created a wide variety of relationships between users. Friendships on Facebook and trust in the Epinions network are examples of these relationships. Most social media research has often focused on positive interpersonal relationships, such as friendships. However, in many real-world applications, there are also networks of negative relationships whose communication between users is either distrustful or hostile in nature. Such networks are called signed networks. In this work, sign prediction is made based on existing links between nodes. read more
Graph-based abstractive biomedical text summarization
Azadeh Givchi, Reza Ramezani,and Ahmad Baraani
Summarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. The first challenge of these methods is to detect the concepts that well convey the main topic of the text and extract sentences that better describe these essential concepts. The second challenge is the correct interpretation of the essential concepts to generate new paraphrased sentences such that they are not exactly the same as the sentences in the main text. read more
SParseQA: Sequential word reordering and parsing for answering complex natural language questions over knowledge graphs
Mahdi Bakhshia, Mohammad Ali Nematbakhsh, Mehran Mohsen zadeh, and Amir Masoud Rahmani
One of the effective approaches for answering natural language questions (NLQs) over knowledge graphs consists of two main stages. It first creates a query graph based on the NLQ and then matches this graph over the knowledge graph to construct a structured query. An obstacle in the first stage is the need to build question interpretations with candidate resources, even if some implicit phrases exist in the sentence. In the second stage, a serious problem is to map diverse NLQ relations to their corresponding predicates. read more
A dual framework for implicit and explicit emotion recognition: An ensemble of language models and computational linguistics
Fereshteh Khoshnam, and Ahmad Baraani
One of the research domains in the field of sentiment analysis is automatic emotion recognition in texts which is a worthy topic in human-computer interaction. Text processing has always faced many challenges. The main one is the structural and semantic differences of sentences which have had a significant impact on the malfunction of auto-recognition systems. This problem becomes more prominent in short texts in which words and their concurrences are limited and insufficient. read more
Semantic schema based genetic programming for symbolic regression
Zahra Zojaji, Mohammad Mehdi Ebadzadeh, Hamid Nasiri
Despite the empirical success of Genetic programming (GP) in various symbolic regression applications, GP is not still known as a reliable problem-solving technique in this domain. Non-locality of GP representation and operators causes ineffectiveness of its search procedure. This study employs semantic schema theory to control and guide the GP search and proposes a local GP called semantic schema-based genetic programming (SBGP). SBGP partitions the semantic search space into semantic schemas and biases the search to the significant schema of the population, which is gradually progressing towards the optimal solution. read more