Sharif Digital Repository

Creating tools for automating trade or creating advisory tools have great importance for stock markets. Regarding stock markets, information varies in type e.g. financial disclosures, news, price history, audit reports, etc. Aforementioned information and data's variance, volume, and the high number of factors affecting the stock price, make the stock market hard to predict all together. Therefore, predictions are usually limited to a subset of data. The goal of this research is to take advantage of the newest language processing techniques in order to analyze financial disclosure documents and predict their effect on their related stock price. Financial disclosures usually have a longer... 

Stock market prediction has always been a challenging task. Due to its stochastic nature, naive models cannot help solve the problem. In the past, Statistical models were used, however nowa- days with the rise of deep learning and more complex models, aggregating data, in order to pre- dict the stock price, has become feasible. Moreover, the emergence of social networks enables researchers to design models for stock prediction.Researchers used recurrent networks and word vector representations to solve this problem. However, recently newer models such as generative models based on VAEs and attention have gained interest. Newer models also don’t rely on a single data source and use multiple... 

Named Entity Recognition (NER) is a key component and the first step of many natural language processing tasks such as question answering systems, information retrieval, machine translation, text summarization, and so on. First NER system initially used rule-based and machine learning methods, which grew significantly with the advent of deep learning architectures as well as the development of hardware and data resources. Traditional deep learning methods used convolutional and recursive neural networks that had disadvantages such as gradient vanishing and non-parallel computing, respectively. In addition, the need for huge corpus and powerful hardware resources was one of the problems of... 

In recent years with advances in artificial intelligence, numerous methods have been proposed for tasks that sometimes are difficult for human or requires a long time to overcome. Text-to-speech systems are among the methods that lead to easier human real life in different applications. The goal of this research is to propose a method for designing a Persian text-to-speech system while this system can be used in a wide domain of Persian texts and its output sound looks natural. In recent years, significant advances have been made in designing these systems for common languages like English. Most of these advances are because of proposed deep learning methods that are suitable for these... 

Conversational Question Answering (CQA) has gained significant attention in recent years due to its potential to facilitate natural language interactions between humans and machines. The ability to effectively incorporate relevant history turns, which are previous utterances in a conversation, plays a crucial role in improving the overall performance of CQA systems. In this master's thesis, we explore the importance of conversational question answering and propose a novel approach for selecting relevant history turns to enhance the accuracy and relevance of the system's responses. Initially, we provide an overview of the recent models developed for addressing the CQA challenge. We analyze... 

In this thesis, we introduce a novel Artificial Intelligence (AI) system inspired by the philosophical and psychoanalytical concept of imagination as a ``Re-construction of Experiences". Our AI system is equipped with an imagination-inspired module that bridges the gap between textual inputs and other modalities, enriching the derived information based on previously learned experiences. A unique feature of our system is its ability to formulate independent perceptions of inputs. This leads to unique interpretations of a concept that may differ from human interpretations but are equally valid, a phenomenon we term as ``Interpretable Misunderstanding". We employ large-scale models,... 

Nowadays Artificial Intelligence systems are ubiquitous. One of the important applications is textual question-answering systems, which provide a means of information retrieval in a user-friendly manner. Reasoning is an inseparable part of human daily life, and people use reasoning to judge and find rational and correct answers to questions. To get the desired output from question-answering systems, these systems must be equipped with reasoning. This research focuses on improving question answering by considering Commonsense Reasoning. The two most important weaknesses of the existing question-answering systems are the questions being in the form of multiple-choice, which is far from a... 

This study is a research on Persian Intonational System, which was carried out within the Autosegmental-Metrical framework.The intonational elements of Persian are represented by two distinctive levels (High and Low). Persian intonation system enjoys three main elements: pitch accents, Phrase accents and boundary tones. Every intonational element has its own meaning. The data of study is about 200 utterances produced by two Persian native speaker one male and one female. These utterances have been annotated in four levels in PToBI: phoneme level, word level, tone level and break index level. In this study we aimed to formulate the acoustic representation of the intonational elements by three... 

The stress positions of the words are determined according to the type of their morphological elements. Persian Words often have a fixed position for stress. However, Persian wordforms show different stress positions, based on their morpheme types or in the other words, their bound-non derivational affixes. Inflectional affixes accept stress, whereas clitics do not. In the present research we studied both types of non derivational affixes considering their phonetic features meaning fundamental frequency and duration. The phonetic representation of the pitch accent as the phonemic- intonational element was given as a result of this study, as well. The differences in the phonetic... 

Relation extraction is known as a main task in information extraction. There are two main approach in this field, rule based and statistical approaches. This thesis applied a rule based relation extraction approach. In this research we tried to recognize Persian syntactic and morphological patterns to extract relation between named entities. At first we annotated a news dataset by person,organization and location named entity tags which is included more than 100 thousand tokens. After that we found there are 1037 relations 2197 candidate relations. Candidate and labled relations extracted between two entities which is located in a clause. These relations are "PERS_PERS-COMMENTING",... 

Following dramatic changes after using deep learning method as a solution for Natural Language Processing tasks, Transformer architecture get popular. Based on that, then BERT Language model presented and get state-of-the-art as a solution for a lot of language processing tasks. It was a turning point in Natural Language Processing field. Also, in cross-lingual methods research line motivated by developing a common space for representation of language units, e.g. words, sentences, in more that one language, get some remarkable improvements. However, for languages distant from English such as Persian or Arabic the methods' performance was not clear. In this work, we performed some innovative... 

Automatic recognition of musical Maqams has been one of the challenging problems in Music Information Retrieval. Despite the increasing amount of related research in recent years, we are still far away from building related real-life applications. Nevertheless, a very small portion of these research is dedicated to automatic recognition of Maqams in recitation of the Holy Quran. In this thesis, as a first attempt, we have used machine learning methods to classify six Maqam families which are commonly used in Quran recitation. Also, due to the lack of pre-exisiting datasets, we have annotated approximately 1325 minutes of Tadwir recitation from two prominent Egyptian reciters, i.e., Muhammad... 

With the increase of internet usage and the volume of available data, the need for data mining and text processing is felt. One of the common obstacles for using these methods is usage of colloquial and non-standard language in writings. Due to this fact, combined with the fact that NLP tasks in Persian language had always faced data shortage issues, in this thesis, we first collect and construct a parallel data set, consisting of colloquial texts used in social media. Then after examining various methods used in other languages for text normalization, we propose a combination of new hybrid methods, involving Statistical Machine Translation methodology with some modification, to normalize... 

In recent years, word embeddings as the word representation have captured the attention of natural language processing (NLP) researches. One of the great advantages of word embeddings is their capability in representing the relationships of the words. Therefore, using word embeddings in NLP applications results in better performance.Despite widespread attention towards word embedding in late years, Persian word embeddings have not achieved sensible progress. One of the Persian word embeddings difficulties is related to that, Persian is a low-resource language in comparison with worldwide languages. Therefore, Persian word embedding quality is lower than English. Consequently, the accuracy of... 

This project has been conducted in two related phases: In the first phase, we have attempted to write a program capable of answering thematic similarity multiple-choice questions without utilizing any training data. The best performance in this phase was attained by the 25-topic LDA model using the Hellinger distance between the probability distributions of the poetic verses. This model managed to attain an accuracy of 42%, which is very close to the average human performance of 43%. In the second phase, two tasks of seven-class classification and binary classification were defined based on the p-value of the questions. To this end, the questions were initially ranked according to the... 

News plays a very important role in stock market trading. Nowadays news from a different part of the world and about different fields can be accessed easily, and for a successful trade, it is necessary to analyze accurately and use this big data and information as soon as possible. For this reason, this thesis tries to present and study models based on Deep Learning networks and Natural Language Processing for financial news analysis and predicting stock indices movement. This research takes advantage of a language model for learning and representing news text, and beside this language model it uses deep learning networks at multiple levels to extract proper features from each news in a day... 

Keyphrase extraction, as an important open problem of Natural Language Processing (NLP), is useful as a stand-alone task in the field of Information Extraction and as an upstream task for Information Retrieval, text summarization and classification,etc. In this study, regarding the needs in Persian NLP, artificial neural networks are adopted to extract keyphrases from single documents and a graph-based re-scoring method is proposed for multidocument keyphrase extraction. The proposed method for extracting keyphrases from multiple documents consists of two steps: (1) extracting keyphrases of each document in a cluster using a sequence to sequence model with attention, and (2) re-scoring the... 

Spoken Language Understanding (SLU) is aimed at extracting meaning from natural spoken language. Meaning extraction ranges from "extracting specific phrases" to "extracting users' intentions from their speech" and goes as far as "extracting the entities and details of their intentions". Extracting the exact intended meaning of the user is a sophisticated process. In this research, considering the lack of standard data in Persian, an SLU system for this language has been implemented using Markov Logic Networks (MLNs), in order to reduce the need for extra datasets. MLNs combine the explanatory power and orderliness of First-Order Logic with the uncertainty of probabilities. Therefore, these... 

Keywords or keyphrases are of importance as the smallest unit of representing the meaning of a text. Automated Keyword Extraction (AKE), as one of the natural language processing tasks is used in various applications such as searching, indexing and information retrieval. Keywords of scientific articles are basically specified manually by their authors, whereas most of the information available on the internet lack such keywords. In this research, we endeavor to automatically extract keywords of a set of Persian paper abstracts using an unsupervised machine learning method. The method used is to extract a set of candidate phrases from the text, and to cluster the document words to find a set...