Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network

In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and pooling layers, which can model a variety of compositions by the feature maps and choose the most informative compositions by the pooling layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list of candidate dependency parsing trees. The experiments show that RCNN is very effective to improve the state-of-the-art dependency parsing on both English and Chinese datasets

From Paraphrase Database to Compositional Paraphrase Model and Back

The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates. However, it is still unclear how it can best be used, due to the heuristic nature of the confidences and its necessarily incomplete coverage. We propose models to leverage the phrase pairs from the PPDB to build parametric paraphrase models that score paraphrase pairs more accurately than the PPDB's internal scores while simultaneously improving its coverage. They allow for learning phrase embeddings as well as improved word embeddings. Moreover, we introduce two new, manually annotated datasets to evaluate short-phrase paraphrasing models. Using our paraphrase model trained using PPDB, we achieve state-of-the-art results on standard word and bigram similarity tasks and beat strong baselines on our new short phrase paraphrase tasks.Comment: 2015 TACL paper updated with an appendix describing new 300 dimensional embeddings. Submitted 1/2015. Accepted 2/2015. Published 6/201

구문론을 활용한 신경망 기반 문장 표현의 학습 및 분석

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2021.8. 김태욱.구문론(syntax)은 언어학의 한 갈래로써, 자연어 문장의 형성 과정에 내포되어 있 는 원리와 그로 인해 촉발되는 여러 언어적 현상을 규정하고 이를 검증하는 연구 분야를 총칭한다. 구문론은 단어, 구 및 절과 같은 문장 내의 구성 요소로부터 해당 문장의 의미를 점진적으로 구축해 나가는 과정에 대한 체계적인 이론적 절차를 제공하며, 따라서 이는 자연어처리에서 문장 표현 학습 및 분석을 위한 방법론을 구상하는데 있어 활용될 수 있는 잠재성을 지니고 있다. 본 논문에서는 신경망 기반의 문장 표현 방법론을 개발하는 데 있어 구문론을 활용하는 두 측면에 관하여 논한다. 먼저, 언어학적인 파스 트리의 형태로 표현되 어 있거나 혹은 타 신경망 모델의 파라미터에 암시적으로 저장되어 있는 구문론적 지식을 도입하여 더 나은 문장 표현을 만드는 보다 직접적인 방법론을 제시한다. 이에 더하여, 구문론에 바탕한 문법적 체계를 이용하여 학습이 완료된 신경망 기반 문장 표현 모델들의 작동 원리를 규명하고 이들의 개선점을 찾는데 도움을 줄 수 있 는 분석적 접근법 또한 소개한다. 실제 환경에서의 다각적인 실험과 검증을 통하여 규칙 및 통계 기반 자연어처리에서 귀중한 자원으로 간주되었던 구문론이 신경망 기반의 모델이 대중적으로 사용되고 있는 현재의 자연어처리에서도 보완재로써 기능할 수 있음을 보인다. 구체적으로, 구문론이 고성능의 문장 표현을 위한 신경 망 모델 혹은 이를 위한 학습 방법론을 개발하는데 있어 효과적인 직관을 제공할 수 있음을 실증하고, 문장 표현 신경망 모델이 자체적으로 파스 트리를 복원해낼 수 있는 능력을 평가함으로써 구문론을 내부 작동 체계가 불명확한 신경망 모델의 작동 원리에 대한 이해도를 증진시키는 분석 도구로 활용한다.Syntax is a theory in linguistics that deals with the principles underlying the composition of sentences. As this theoretical framework provides formal instructions regarding the procedure of constructing a sentence with its constituents, it has been considered as a valuable reference in sentence representation learning, whose objective is to discover an approach of transforming a sentence into the vector that illustrates its meaning in a computationally tractable manner. This dissertation provides two particular perspectives on harmonizing syntax with neural sentence representation models, especially focusing on constituency grammar. We first propose two methods for enriching the quality of sentence embeddings by exploiting the syntactic knowledge either represented as explicit parse trees or implicitly stored in neural models. Second, we regard syntactic formalism as a lens through which we reveal the inner workings of pre-trained language models which are state-of-the-art in sentence representation learning. With a series of demonstrations in practical scenarios, we show that syntax is useful even in the neural era where the models trained with huge corpora in an end-to-end manner are prevalent, functioning as either (i) a source of inductive biases that facilitate fast and effective learning of such models or (ii) an analytic tool that increases the interpretability of the black-box models.Chapter 1 Introduction 1 1.1 Dissertation Outline 5 1.2 Related Publications 6 Chapter 2 Background 8 2.1 Introduction to Syntax 8 2.2 Neural Networks for Sentence Representations 10 2.2.1 Recursive Neural Network 11 2.2.2 Transformer 12 2.2.3 Pre-trained Language Models 14 2.3 Related Literature 16 2.3.1 Sentence Representation Learning 16 2.3.2 Probing Methods for Neural NLP Models 17 2.3.3 Grammar Induction and Unsupervised Parsing 18 Chapter 3 Sentence Representation Learning with Explicit Syntactic Structure 19 3.1 Introduction 19 3.2 Related Work 21 3.3 Method 23 3.3.1 Tree-LSTM 24 3.3.2 Structure-aware Tag Representation 25 3.3.3 Leaf-LSTM 28 3.3.4 SATA Tree-LSTM 29 3.4 Experiments 31 3.4.1 General Configurations 31 3.4.2 Sentence Classification Tasks 32 3.4.3 Natural Language Inference 35 3.5 Analysis 36 3.5.1 Ablation Study 36 3.5.2 Representation Visualization 38 3.6 Limitations and Future Work 39 3.7 Summary 40 Chapter 4 Sentence Representation Learning with Implicit Syntactic Knowledge 41 4.1 Introduction 41 4.2 Related Work 44 4.3 Method 46 4.3.1 Contrastive Learning with Self-Guidance 47 4.3.2 Learning Objective Optimization 50 4.4 Experiments 52 4.4.1 General Configurations 52 4.4.2 Semantic Textual Similarity Tasks 53 4.4.3 Multilingual STS Tasks 58 4.4.4 SentEval Benchmark 59 4.5 Analysis 60 4.5.1 Ablation Study 60 4.5.2 Robustness to Domain Shifts 61 4.5.3 Computational Efficiency 62 4.5.4 Representation Visualization 63 4.6 Limitations and Future Work 63 4.7 Summary 65 Chapter 5 Syntactic Analysis of Sentence Representation Models 66 5.1 Introduction 66 5.2 Related Work 68 5.3 Motivation 70 5.4 Method 72 5.4.1 CPE-PLM 72 5.4.2 Top-down CPE-PLM 73 5.4.3 Pre-trained Language Models 74 5.4.4 Distance Measure Functions 76 5.4.5 Injecting Bias into Syntactic Distances 77 5.5 Experiments 78 5.5.1 General Configurations 78 5.5.2 Experimental Results on PTB 80 5.5.3 Experimental Results on MNLI 83 5.6 Analysis 85 5.6.1 Performance Comparison by Layer 85 5.6.2 Estimating the Upper Limit of Distance Measure Functions 86 5.6.3 Constituency Tree Examples 88 5.7 Summary 93 Chapter 6 Multilingual Syntactic Analysis with Enhanced Techniques 94 6.1 Introduction 94 6.2 Related work 96 6.3 Method 97 6.3.1 Chart-based CPE-PLM 97 6.3.2 Top-K Ensemble for CPE-PLM 100 6.4 Experiments 100 6.4.1 General Configurations 100 6.4.2 Experiments on Monolingual Settings 102 6.4.3 Experiments on Multilingual Settings 103 6.5 Analysis 106 6.5.1 Factor Correlation Analysis 108 6.5.2 Visualization of Attention Heads 108 6.5.3 Recall Scores on Noun and Verb Phrases 109 6.6 Limitations and Future Work 110 6.7 Summary 111 Chapter 7 Conclusion 112 Bibliography 116 초록 138박

Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.Comment: Accepted for presentation at EMNLP 201