Paraphrase Recognition Research Articles

Imagery Recognition and Semantic Analysis Techniques in Chinese Literary Texts

Abstract Chinese literary texts contain a sizeable vivid imagery vocabulary, which makes it difficult for average readers to judge the boundaries between words, and the current pre-trained language model is also difficult for them to learn its implicit knowledge effectively, which brings troubles to machine semantic analysis. The study uses CRF training to obtain a semantic analysis model of Chinese literary texts that recognizes the semantic relationship between two words. SVM is used to train classifiers for confusing categories, and the two semantic relations in the output of the CRF model are further recognized to determine the final semantic relations between word pairs. Finally, the LCQMC dataset is used as the experimental data, and the semantic analysis technique based on CRF and SVM is employed to obtain the participle, lexical, and dependent syntactic annotations. According to the results, the model’s correct rates on the LAS for paraphrase recognition and dependency analysis of Chinese literary texts are 74.83% and 92.05%, respectively. The study enhances the efficiency of semantic analysis of relevant Chinese texts and is crucial for the study on the semantic analysis of terms.

BERT: A Review of Applications in Sentiment Analysis

E-commerce reviews are becoming more valued by both customers and companies. The high demand for sentiment analysis is driven by businesses relying on it as a crucial tool to improve product quality and make informed decisions in a fiercely competitive business environment. The purpose of this review paper is to explore and evaluate the applications of the BERT model, a Natural Language Processing (NLP) technique, in sentiment analysis across various fields. The model has been utilized in certain studies for various languages, restaurant businesses, agriculture, Automated Essay Scoring (AES), Twitter, and Google Play. The BERT model's fine-tuning steps involve using pre-trained BERT to perform various language understanding tasks. Text pre-processing is conducted to clean up the data and convert it to numbers before feeding it into BERT, which generates vectors for each input token. We found that BERT outperformed the norm on a range of general language understanding tasks, including sentiment analysis, paraphrase recognition, question-answering, and linguistic acceptability. The detection of neutral reviews and the presence of false reviews in the dataset are two problems that have an impact on the model's accuracy. Training is also slow because it is huge and there are many weights to update. Additional research could be conducted to improve the BERT model's accuracy by constructing a false review categorization model and providing more training to the model in recognizing neutral reviews. Doi: 10.28991/HIJ-2023-04-02-015 Full Text: PDF

Applying BERT for Early-Stage Recognition of Persistence in Chat-Based Social Engineering Attacks

Chat-based social engineering (CSE) attacks are attracting increasing attention in the Small-Medium Enterprise (SME) environment, given the ease and potential impact of such an attack. During a CSE attack, malicious users will repeatedly use linguistic tricks to eventually deceive their victims. Thus, to protect SME users, it would be beneficial to have a cyber-defense mechanism able to detect persistent interlocutors who repeatedly bring up critical topics that could lead to sensitive data exposure. We build a natural language processing model, called CSE-PersistenceBERT, for paraphrase detection to recognize persistency as a social engineering attacker’s behavior during a chat-based dialogue. The CSE-PersistenceBERT model consists of a pre-trained BERT model fine-tuned using our handcrafted CSE-Persistence corpus; a corpus appropriately annotated for the specific downstream task of paraphrase recognition. The model identifies the linguistic relationship between the sentences uttered during the dialogue and exposes the malicious intent of the attacker. The results are satisfactory and prove the efficiency of CSE-PersistenceBERT as a recognition mechanism of a social engineer’s persistent behavior during a CSE attack.

KGAnet: a knowledge graph attention network for enhancing natural language inference

Natural language inference (NLI) is the basic task of many applications such as question answering and paraphrase recognition. Existing methods have solved the key issue of how the NLI model can benefit from external knowledge. Inspired by this, we attempt to further explore the following two problems: (1) how to make better use of external knowledge when the total amount of such knowledge is constant and (2) how to bring external knowledge to the NLI model more conveniently in the application scenario. In this paper, we propose a novel joint training framework that consists of a modified graph attention network, called the knowledge graph attention network, and an NLI model. We demonstrate that the proposed method outperforms the existing method which introduces external knowledge, and we improve the performance of multiple NLI models without additional external knowledge.

Paraphrase Extraction using fuzzy hierarchical clustering

Paraphrase Extraction involves the discovery of equivalent text segments from large corpora and finds application in tasks such as multi-document summarization and document clustering. Semantic similarity identification is a challenging problem which is further compounded by the large size of the corpus. In this paper a two-stage approach which involves clustering followed by Paraphrase Recognition has been proposed for extraction of sentence-level paraphrases from text collections. In order to handle the ambiguity and inherent variability of natural language a fuzzy hierarchical clustering approach which combines agglomeration based on verbs and division on nouns has been used. Sentences within each resultant cluster are then processed by a machine-learning based Paraphrase Recognizer to discover the paraphrases. The two-stage approach has been applied on the Microsoft Research Paraphrase Corpus and a subset of the Microsoft Research Video Description Corpus. The performance has been evaluated against an existing k-means clustering approach as well as cosine-similarity technique and Fuzzy C-Means clustering and the two-stage system has consistently demonstrated better performance.

Plagiarism Detection Using Machine Learning-Based Paraphrase Recognizer

AbstractPlagiarism in free text has become a common occurrence due to the wide availability of voluminous information resources. Automatic plagiarism detection systems aim to identify plagiarized content present in large repositories. This task is rendered difficult by the use of sophisticated plagiarism techniques such as paraphrasing and summarization, which mask the occurrence of plagiarism. In this work, a monolingual plagiarism detection technique has been developed to tackle cases of paraphrased plagiarism. A support vector machine based paraphrase recognition system, which works by extracting lexical, syntactic, and semantic features from input text has been used. Both sentence-level and passage-level approaches have been investigated. The performance of the system has been evaluated on various corpora, and the passage level approach has registered promising results.

Human-inspired semantic similarity between sentences

Abstract Following the Principle of Compositionality, the meaning of a complex expression is influenced, to some extent, not only by the meanings of its individual words, but also the structural way the words are assembled. Compositionality has been a central research issue for linguists and psycholinguists. However, it remains unclear how does syntax influence the meaning of a sentence. In this paper, we propose an interdisciplinary approach to better understand that relation. We present an empirical study that seeks for the different weights given by humans to different syntactic roles when computing semantic similarity. In order to test the validity of the hypotheses derived from the psychological study, we use a computational paradigm. We incorporate the results of that study to a psychologically plausible computational measure of semantic similarity. The results shown by this measure in terms of correlation with human judgments on a paraphrase recognition task confirm the different importance that humans give to different syntactic roles in the computation of semantic similarity. This results contrast with generative grammar theories but support neurolinguistic evidence.

Using part-of-speech tags as deep-syntax indicators in determining short-text semantic similarity

This paper presents POST STSS, a method of determining short-text semantic similarity in which part-of-speech tags are used as indicators of the deeper syntactic information usually extracted by more advanced tools like parsers and semantic role labelers. Our model employs a part-of-speech weighting scheme and is based on a statistical bag-of-words approach. It does not require either hand-crafted knowledge bases or advanced syntactic tools, which makes it easily applicable to languages with limited natural language processing resources. By using a paraphrase recognition test, we demonstrate that our system achieves a higher accuracy than all existing statistical similarity algorithms and solutions of a more structural kind.

Feature Analysis for Paraphrase Recognition and Textual Entailment

Paraphrase recognition is the task of Natural Language Processing of detecting if an expression restated as another expression contains the same information. Textual Entailment recognition, while being similar to paraphrase recognition, is a task that consists in finding out if a given text can be observed as a consequence of another text fragment, sometimes considering only part of the original meaning, or adding some inferences based on common sense. Tra- ditionally, for solving this problem, several lexical, syntactic and semantic based techniques are used. In this work, we seek to use the less resources as possi- ble, while being effective. For this, we perform a feature analysis for performing Paraphrase Recognition and recognizing Textual Entailment experimenting with the combination of several Natural Language Processing techniques like word overlapping, syntactic analysis, and elimination of stop words. Particularly, we explore using the syntactic n-grams technique combined with some auxiliary ap- proaches such as stemming, synonym detection, similarity measures and linear interpolation. We measure and compare the performance of our system by using the Microsoft Research Paraphrase Corpus, and the RTE-3 test set for Paraphras- ing and Textual Entailment, respectively. Syntactic n-grams produce good results for Paraphrase Recognition. As far as we know, syntactic n-grams had not been used for this task. For Textual Entailment, our best results were obtained by using a simple word overlapping algorithm based on stemming and elimination of stop words.

GENETIC ALGORITHM BASED FEATURE SELECTION FOR PARAPHRASE RECOGNITION

Paraphrase Recognition systems most often use various lexical, syntactic and semantic features to recognize paraphrases. This paper presents the work done in designing a Support Vector Machine (SVM) based Paraphrase Recognizer and then improving its performance using feature selection strategy. Wrapper method of feature selection has been adopted by combining Genetic Algorithms with Support Vector Machine Classifiers. Experimental results show that applying Feature selection improves the accuracy besides reducing the number of features. The developed paraphrase recognizer has been applied for the Student Answer Evaluation task. The results obtained show that the performance of Answer Evaluation systems which use only half the number of features is comparable to systems using the original feature set.

술어-논항 튜플 기반 근사 정렬을 이용한 문장 단위 바꿔쓰기표현 유형 및 오류 분석

본 논문에서는 Predicate-Argument Tuple (PAT)를 기반으로 텍스트 간 심층적 근사 정렬(Approximate Alignment)을 통한 문장 단위 바꿔쓰기표현(sentential paraphrase) 식별 모델을 제안한다. 두 문장 간의 PAT 기반 근사 정렬 결과를 바탕으로, 두 문장의 의미적 연관성을 효과적으로 표현하는 다양한 정렬 자질(alignment feature)들을 정의함으로써, 바꿔쓰기표현 식별 문제를 지도 학습(supervised learning) 기반의 자동 분류 모델로 접근하였다. 실험을 통해서 제안 모델의 가능성을 확인할 수 있었으며, 시스템의 오류 분석을 통해 제안 방법이 아직 해결하지 못하는 다양한 바꿔쓰기표현 유형들을 식별함으로써 향후 시스템의 성능 개선 방향을 도출하였다. This paper proposes a model for recognizing sentential paraphrases through Predicate-Argument Tuple (PAT)-based approximate alignment between two texts. We cast the paraphrase recognition problem as a binary classification by defining and applying various alignment features which could effectively express the semantic relatedness between two sentences. Experiment confirmed the potential of our approach and error analysis revealed various paraphrase patterns not being solved by our system, which can help us devise methods for further performance improvement.

A Study on Paraphrase Recognition Using Radial Basis Function Neural Network

Paraphrasing is the restatement of a given text using alternate words. Recognition of paraphrases is vital in applications such as question answering, information extraction, and multi-document summarization. Lexical, syntactic, and semantic features of text can be used either individually or in combinations for recognizing paraphrases. Several machine-learning classifiers such as support vector machines (SVM), nearest neighbour method, and decision trees have been used for paraphrase recognition with SVM recognizers being the most popular ones. This paper explores the applicability of neural networks for paraphrase recognition. A radial basis function neural network (RBFNN) has been designed and implemented for recognizing paraphrases. Experiments have been carried out on the Microsoft research paraphrase corpus. From the results of the experiments, it has been observed that the RBFNN recognizer consistently outperforms the SVM recognizer with respect to accuracy and that the best performance was achieved when a combination of lexical, syntactic, and semantic features were used.

Paraphrase Recognition using Neural Network Classification

Paraphrasing refers to conveying the same content in several ways. The successful recognition of paraphrases is crucial to various natural language processing tasks such as Information Extraction, Document Summarization, Question Answering etc. Several techniques have been employed for paraphrase recognition using lexical, syntactic and semantic features. Many of these systems have been tested on the MicroSoft Research Paraphrase Corpus. But the performance of these systems has scope for further improvement. Since neural network architectures model the human brain structure which excels at natural language processing tasks, this paper presents a neural network classifier for recognizing paraphrases. A combination of lexical, syntactic and semantic features has been used to train a Back Propagation network. The system can be utilized for detecting similar sentences in applications such as Question Answering and detection of plagiarized content.

Text Relatedness Based on a Word Thesaurus

The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.

Extracting paraphrase patterns from bilingual parallel corpora

AbstractParaphrase patterns are semantically equivalent patterns, which are useful in both paraphrase recognition and generation. This paper presents a pivot approach for extracting paraphrase patterns from bilingual parallel corpora, whereby the paraphrase patterns in English are extracted using the patterns in another language as pivots. We make use of log-linear models for computing the paraphrase likelihood between pattern pairs and exploit feature functions based on maximum likelihood estimation (MLE), lexical weighting (LW), and monolingual word alignment (MWA). Using the presented method, we extract more than 1 million pairs of paraphrase patterns from about 2 million pairs of bilingual parallel sentences. The precision of the extracted paraphrase patterns is above 78%. Experimental results show that the presented method significantly outperforms a well-known method called discovery of inference rules from text (DIRT). Additionally, the log-linear model with the proposed feature functions are effective. The extracted paraphrase patterns are fully analyzed. Especially, we found that the extracted paraphrase patterns can be classified into five types, which are useful in multiple natural language processing (NLP) applications.

Young children and the say/mean distinction: verbatim and paraphrase recognition in narrative and nursery rhyme contexts.

Children's ability to distinguish between the text, what was said, and the intentional structure, what was meant, was interrogated by means of verbatim and paraphrase questions in two types of discourse, narratives and nursery rhymes. Three- to seven-year-olds participated (n = 119, mean age 5.1). There was an interaction between the type of discourse and the younger children's ability to separate wording from intentional structure. In the narrative form they had difficulty rejecting true paraphrases when asked to focus on wording, while in the nursery rhyme form the difficulty was accepting a true paraphrase when asked to focus on intention.