ymoslem/acl-abstracts huggingface dataset:
Linguistic vs. encyclopedic knowledge. Classification of MWEs on the base of domain information (2020)

This paper reports on the first steps in the creation of linked data through the mapping of BTB-WordNet and the Bulgarian Wikipedia. The task of expanding the BTB-WordNet with encyclopedic knowledge is done by mapping its synsets to Wikipedia pages with many MWEs found in the articles and subjected to further analysis. We look for a way to filter the Wikipedia MWEs in the effort of selecting the ones most beneficial to the enrichment of BTB-WN.
It Takes Two to Tango -- Towards a Multilingual MWE Resource (2020)

Mature wordnets offer the opportunity of digging out interesting linguistic information otherwise not explicitly marked in the network. The focus in this paper is on the ways the results already obtained at two levels, derivation and multiword expressions, may be further employed. The parallel recent development of the two resources under discussion, the Bulgarian and the Romanian wordnets, has enabled interlingual analyses that reveal similarities and differences between the linguistic knowledge encoded in the two wordnets. In this paper we show how the resources developed and the knowledge gained are put together towards devising a linked MWE resource that is informed by layered dictionary representation and corpus annotation and analysis. This work is a proof of concept for the adopted method of compiling a multilingual MWE resource on the basis of information extracted from the Bulgarian, the Romanian and the Princeton wordnet, as well as additional language resources and automatic procedures.
Generating Natural Language Numerals with TeX (2020)

Sometimes one needs to produce a text in which many numbers have to be written out in words. Writing such a text and ensuring it is error-free can be a burden, especially if the author is not fluent in the language. Such may occur when working on a reference grammar, a research paper or presentation, or a problem on number names for a contest in linguistics. A remedy is to prepare the text with TEX and let some parts be generated automatically. The human effort this takes is to compose a grammar that describes the features of the numeral system. This paper discusses how this is done.
A Natural Language for Bulgarian Primary and Secondary Education (2020)

This paper examines the qualities and applicability of a provisional programming language, especially designed for use by beginner-level students in Bulgarian primary and secondary schools. The necessity for such a language is investigated. Then, relevant features are defined, as inspired by various programming languages (notably, languages used in education and characterised with non- English syntax) and by general trends related to the achievement of natural language in software development. A survey is conducted to test young students' interaction with the language, and the latter`s advantages and limitations are listed and discussed.
Digital Edition of the Life of St. Petka (2020)

This paper presents the construction of a digital edition of multiple versions of the hagiography of St. Petka of Tarnovo. Two related versions are uploaded at first: a Church Slavonic print edition and its later damaskini redaction. Both texts are adapted for user-friendly reading with side-by-side facsimiles. Translations and additional data concerning separate tokens and sentences can be shown up by the cursor on fly. Further metadata will be available for search. Annotation has been adapted for the transitionary status of the language of the texts: it allows us to compare similar morphological forms with various functions. The edition has already been published online and can be used for both teaching and studying. The texts have been digitalized as a part of a larger project concerning the development of the Balkan areal features.
A Bilingual Lexicosemantic Network of Bread Based on a Parallel Corpus (2020)

We present an experiment in using a corpus of Bulgarian and Ukrainian parallel texts for the automatised construction of a bilingual lexicosemantic network representing the semantic field of BREAD. We discuss the extraction of the relevant material from the corpus, the production of networks with varying parameters, some issues of the interpretation of these networks, and possible ways of making them more accurate and informative.
A Customizable WordNet Editor (2020)

This paper presents an open-source wordnet editor that has been developed to ensure further expansion of the Romanian wordnet. It comes with a web interface that offers capabilities in selecting new synsets to be implemented, editing the list of literals and their sense numbers and adding these new synsets to the existing network, by importing from Princeton WordNet (and adjusting, when necessary) all the relations in which the newly created synsets and their literals are involved. The application also comes with an authorization mechanism that ensures control of the new synsets added in novice or lexicographer accounts. Although created to serve the current (more or less specific) needs in the development of the Romanian wordnet, it can be customized to fulfill new requirements from developers, either of the same wordnet or of a different one for which a similar approach is adopted.
Comparison of Genres in Word Sense Disambiguation using Automatically Generated Text Collections (2020)

The best approaches in Word Sense Disambiguation (WSD) are supervised and rely on large amounts of hand-labelled data, which is not always available and costly to create. In our work we describe an approach that is used to create an automatically labelled collection based on the monosemous relatives (related unambiguous entries) for Russian. The main contribution of our work is that we extracted monosemous relatives that can be located at relatively long distances from a target ambiguous word and ranked them according to the similarity measure to the target sense. We evaluated word sense disambiguation models based on a nearest neighbour classification on BERT and ELMo embeddings and two text collections. Our work relies on the Russian wordnet RuWordNet.
Consistency Evaluation towards Enhancing the Conceptual Representation of Verbs in WordNet (2020)

This paper outlines the process of enhancing the conceptual description of verb synsets in WordNet using FrameNet frames. On the one hand we expand the coverage of the mapping between WordNet and FrameNet, while on the other {--} we improve the quality of the mapping using a set of consistency checks and verification procedures. The procedures include an automatic identification of potential inconsistencies and imbalanced relations, as well as suggestions for a more precise frame assignment followed by manual validation. We perform an evaluation of the procedures in terms of the quality of the suggestions measured as the potential improvement in precision and coverage, the relevance of the result and the efficiency of the procedure.
On WordNet Semantic Classes: Is the Sum Always Bigger? (2020)

The paper offers an approach to the validation of the data resulted from a previous effort on expansion of WordNet noun semantic classes by mapping them with the semantic types within the Corpus Pattern Analysis (CPA) ontology employed by the framework of the Pattern Dictionary of English Verbs (PDEV). A case study is presented along with a set of conditions to be checked when validating the combined data.
A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis (2020)

Understanding expressed sentiment and emotions are two crucial factors in human multimodal language. This paper describes a Transformer-based joint-encoding (TBJE) for the task of Emotion Recognition and Sentiment Analysis. In addition to use the Transformer architecture, our approach relies on a modular co-attention and a glimpse layer to jointly encode one or more modalities. The proposed solution has also been submitted to the ACL20: Second Grand-Challenge on Multimodal Language to be evaluated on the CMU-MOSEI dataset. The code to replicate the presented experiments is open-source .
A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews (2020)

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.
Multilogue-Net: A Context-Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation (2020)

Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions. Multi-modal Emotion Detection and Sentiment Analysis can be particularly useful, as applications will be able to use specific subsets of available modalities, as per the available data. Current systems dealing with Multi-modal functionality fail to leverage and capture - the context of the conversation through all modalities, the dependency between the listener(s) and speaker emotional states, and the relevance and relationship between the available modalities. In this paper, we propose an end to end RNN architecture that attempts to take into account all the mentioned drawbacks. Our proposed model, at the time of writing, out-performs the state of the art on a benchmark dataset on a variety of accuracy and regression metrics.
Low Rank Fusion based Transformers for Multimodal Sequences (2020)

Our senses individually work in a coordinated fashion to express our emotional intentions. In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers. The low-rank factorization of multimodal fusion amongst the modalities helps represent approximate multiplicative latent signal interactions. Motivated by the work of (CITATION) and (CITATION), we present our transformer-based cross-fusion architecture without any over-parameterization of the model. The low-rank fusion helps represent the latent signal interactions while the modality-specific attention helps focus on relevant parts of the signal. We present two methods for the Multimodal Sentiment and Emotion Recognition results on CMU-MOSEI, CMU-MOSI, and IEMOCAP datasets and show that our models have lesser parameters, train faster and perform comparably to many larger fusion-based architectures.
Unsupervised Online Grounding of Natural Language during Human-Robot Interactions (2020)

Allowing humans to communicate through natural language with robots requires connections between words and percepts. The process of creating these connections is called symbol grounding and has been studied for nearly three decades. Although many studies have been conducted, not many considered grounding of synonyms and the employed algorithms either work only offline or in a supervised manner. In this paper, a cross-situational learning based grounding framework is proposed that allows grounding of words and phrases through corresponding percepts without human supervision and online, i.e. it does not require any explicit training phase, but instead updates the obtained mappings for every new encountered situation. The proposed framework is evaluated through an interaction experiment between a human tutor and a robot, and compared to an existing unsupervised grounding framework. The results show that the proposed framework is able to ground words through their corresponding percepts online and in an unsupervised manner, while outperforming the baseline framework.
Leveraging Multimodal Behavioral Analytics for Automated Job Interview Performance Assessment and Feedback (2020)

Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring interviews are structured to evaluate expansively a potential employee`s suitability for the position - their professional qualifications, interpersonal skills, ability to perform in critical and stressful situations, in the presence of time and resource constraints, etc. Candidates, therefore, need to be aware of their positive and negative attributes and be mindful of behavioral cues that might have adverse effects on their success. We propose a multimodal analytical framework that analyzes the candidate in an interview scenario and provides feedback for predefined labels such as engagement, speaking rate, eye contact, etc. We perform a comprehensive analysis that includes the interviewee`s facial expressions, speech, and prosodic information, using the video, audio, and text transcripts obtained from the recorded interview. We use these multimodal data sources to construct a composite representation, which is used for training machine learning classifiers to predict the class labels. Such analysis is then used to provide constructive feedback to the interviewee for their behavioral cues and body language. Experimental validation showed that the proposed methodology achieved promising results.
Audio-Visual Understanding of Passenger Intents for In-Cabin Conversational Agents (2020)

Building multimodal dialogue understanding capabilities situated in the in-cabin context is crucial to enhance passenger comfort in autonomous vehicle (AV) interaction systems. To this end, understanding passenger intents from spoken interactions and vehicle vision systems is an important building block for developing contextual and visually grounded conversational agents for AV. Towards this goal, we explore AMIE (Automated-vehicle Multimodal In-cabin Experience), the in-cabin agent responsible for handling multimodal passenger-vehicle interactions. In this work, we discuss the benefits of multimodal understanding of in-cabin utterances by incorporating verbal/language input together with the non-verbal/acoustic and visual input from inside and outside the vehicle. Our experimental results outperformed text-only baselines as we achieved improved performances for intent detection with multimodal approach.
AI Sensing for Robotics using Deep Learning based Visual and Language Modeling (2020)

An artificial intelligence(AI) system should be capable of processing the sensory inputs to extract both task-specific and general information about its environment. However, most of the existing algorithms extract only task specific information. In this work, an innovative approach to address the problem of processing visual sensory data is presented by utilizing convolutional neural network (CNN). It recognizes and represents the physical and semantic nature of the surrounding in both human readable and machine processable format. This work utilizes the image captioning model to capture the semantics of the input image and a modular design to generate a probability distribution for semantic topics. It gives any autonomous system the ability to process visual information in a human-like way and generates more insights which are hardly possible with a conventional algorithm. Here a model and data collection method are proposed.
Exploring Weaknesses of VQA Models through Attribution Driven Insights (2020)

Deep Neural Networks have been successfully used for the task of Visual Question Answering for the past few years owing to the availability of relevant large scale datasets. However these datasets are created in artificial settings and rarely reflect the real world scenario. Recent research effectively applies these VQA models for answering visual questions for the blind. Despite achieving high accuracy these models appear to be susceptible to variation in input questions. We analyze popular VQA models through the lens of attribution (input`s influence on predictions) to gain valuable insights. Further, We use these insights to craft adversarial attacks which inflict significant damage to these systems with negligible change in meaning of the input questions. We believe this will enhance development of systems more robust to the possible variations in inputs when deployed to assist the visually impaired.
基于规则的双重否定识别------以\textquotedblleft不v1不v2\textquotedblright为例(Double Negative Recognition Based on Rules------Taking \textquotedblleft不v1不v2\textquotedblright as an Example) (2020)

{\textquotedblleft}不v1不v2{\textquotedblright}是汉语中典型的双重否定结构形式之一,它包括{\textquotedblleft}不+助动词+不+v2{\textquotedblright}(不得不去)、{\textquotedblleft}不+是+不v2{\textquotedblright}(不是不好)、述宾结构{\textquotedblleft}不v1...不v2{\textquotedblright}(不认为他不去)等多种双重否定结构,情况复杂。本文以{\textquotedblleft}不v1不v2{\textquotedblright}为例,结合{\textquotedblleft}元语否定{\textquotedblright}、{\textquotedblleft}动词叙实性{\textquotedblright}、{\textquotedblleft}否定焦点{\textquotedblright}等概念,对{\textquotedblleft}不v1不v2{\textquotedblright}进行了全面的考察,制定了{\textquotedblleft}不v1不v2{\textquotedblright}双重否定结构的识别策略。根据识别策略,设计了双重否定自动识别程序,并在此过程中补充了助动词表、非叙实动词表等词库。最终,对28033句语料进行了识别,识别正确率为97.87{\%},召回率约为93.10{\%}。
基于语料库的武侠与仙侠网络小说文体、词汇及主题对比分析(A Corpus-based Contrastive Analysis of Style, Vocabulary and Theme of Wuxia and Xianxia Internet Novels) (2020)

网络文学在我国发展迅猛,其数量和影响力呈现逐年上升的趋势,但目前尚无公开的较大规模网络文学作品语料库,鲜见基于语料库对网络文学具体类别作品的定量研究。本文初步建立了一个网络文学语料库,其中包括武侠和仙侠网络小说,使用文本计量、词频统计以及主题挖掘的方法对两类小说的文体风格、具体词汇使用和小说主题进行对比分析。通过比较,我们发现两类小说的文体风格大致相同,它们在词汇的使用和主题上既有共性又各具特色。从微观到宏观,从表面到内容,将定量统计和定性分析相结合,多角度、多层次的对武侠和仙侠网络小说进行比较。
基于计量的百年中国人名用字性别特征研究(A Quantified Research on Gender Characteristics of Chinese Names in A Century) (2020)

本文构建了一个包含11万以上条目规模的中国名人人名数据库,每条数据含有人名、性别、出生地等社会文化标签,同时含有拼音、笔画、偏旁等文字信息标签,这是目前已知最大的可用于研究的汉语真人人名数据库。基于该数据库,本文从中选择1919年至今的人名,用定性与定量结合的方法探究人名中汉字的特征和其性别差异以及历时变化。从人名长度来看,男性人名比女性人名长;从人名用字的难易度来看,女性用字比男性更复杂;从用字丰富度来看,人名用字越来越单一和集中化,男性人名的用字丰富度大于女性人名。计算人名用字的性别偏度后发现女性人名的专用自更多。两性用字意象有明显的不同,用字的意象随着时间发生改变,但改变最明显的时间节点是改革开放前后,其中女性的变化比男性显著。除此之外,我们还得出人名中的性别极性字表、各个阶段的高频字表、用字变化趋势表等。
伟大的男人和倔强的女人:基于语料库的形容词性别偏度历时研究(Great Males and Stubborn Females: A Diachronic Study of Corpus-Based Gendered Skewness in Chinese Adjectives) (2020)

性别偏见现象是社会语言学和计算语学学者均关注的研究热点,但目前大多数研究都是基于英语的,鲜有对汉语中性别偏见现象,特别是基于形容词的研究缺乏。而形容词是衡量社会对男性和女性角色规约的有力抓手。本文首先利用调查问卷的方法,构建了一个含有466个形容词的数据集,定义性别偏度为特定形容词词义和男性或女性群体相匹配的程度,并计算了数据集中每个形容词的性别偏度。然后基于DCC语料库,研究了《人民日报》的形容词性别偏度的历时总体变化,并考察了和姓名搭配的形容词的历时变化。发现《人民日报》所使用的形容词随时间的推移整体呈现中性化趋势,但在文化大革命期间呈现非常男性化的特征,和男性姓名搭配的形容词整体呈现中性化趋势。
用计量风格学方法考察《水浒传》的作者争议问题------以罗贯中《平妖传》为参照(Quantitive Stylistics Based Research on the Controversy of the Author of \textquotedblleftTales of the Marshes\textquotedblright: Comparing with \textquotedblleftPingyaozhuan\textquotedblright of Luo Guanzhong) (2020)

《水浒传》是独著还是合著,施耐庵和罗贯中是何关系一直存在争议。本文将其作者争议粗略归纳为施耐庵作、罗贯中作、施作罗续、罗作他续、施作罗改五种情况,以罗贯中的《平妖传》为参照,用假设检验、文本聚类、文本分类、波动风格计量等方法,结合对文本内容的分析,考察《水浒传》的写作风格,试图为其作者身份认定提供参考。结果显示,只有罗作他续的可能性大,即前70回为罗贯中所作,后由他人续写,其他四种情况可能性都较小。
多轮对话的篇章级抽象语义表示标注体系研究(Research on Discourse-level Abstract Meaning Representation Annotation framework in Multi-round Dialogue) (2020)

对话分析是智能客服、聊天机器人等自然语言对话应用的基础课题,而对话语料与常规书面语料有较大差异,存在大量的称谓、情感短语、省略、语序颠倒、冗余等复杂现象,对句法和语义分析器的影响较大,对话自动分析的准确率相对书面语料一直不高。其主要原因在于对多轮对话缺乏严整的形式化描写方式,不利于后续的分析计算。因此,本文在梳理国内外针对对话的标注体系和语料库的基础上,提出了基于抽象语义表示的篇章级多轮对话标注体系。具体探讨了了篇章级别的语义结构标注方法,给出了词语和概念关系的对齐方案,针对称谓语和情感短语增加了相应的语义关系和概念,调整了表示主观情感词语的论元结构,并对对话中一些特殊现象进行了规定,设计了人工标注平台,为大规模的多轮对话语料库标注与计算研究奠定基础。
发音属性优化建模及其在偏误检测的应用(Speech attributes optimization modeling and application in mispronunciation detection) (2020)

近年来,发音属性常常被用于计算机辅助发音训练系统(CAPT)中。本文针对使用发音属性的一些难点,提出了一种建模细颗粒度发音属性(FSA)的方法,并在跨语言属性识别、发音偏误检测中进行测试。最终,我们得到了最优平均识别准确率约为95{\%}的属性检测器组;在两个二语测试集上的偏误检测,相比基线,基于FSA方法均获得了超过1{\%}的性能提升。此外,我们还根据发音属性的跨语言特性设置了对照实验,并在上述任务中测试和分析。
基于抽象语义表示的汉语疑问句的标注与分析(Chinese Interrogative Sentences Annotation and Analysis Based on the Abstract Meaning Representation) (2020)

疑问句的句法语义分析在搜索引擎、信息抽取和问答系统等领域有着广泛的应用。计算语言学多采取问句分类和句法分析相结合的方式来处理疑问句,精度和效率还不理想。而疑问句的语言学研究成果丰富,比如疑问句的结构类型、疑问焦点和疑问代词的非疑问用法等,但缺乏系统的形式化表示。本文致力于解决这一难题,采用基于图结构的汉语句子语义的整体表示方法{---}中文抽象语义表示(CAMR)来标注疑问句的语义结构,将疑问焦点和整句语义一体化表示出来。然后选取了宾州中文树库CTB8.0网络媒体语料、小学语文教材以及《小王子》中文译本的2万句语料中共计2071句疑问句,统计了疑问句的主要特点。统计表明,各种疑问代词都可以通过疑问概念amr-unknown和语义关系的组合来表示,能够完整地表示出疑问句的关键信息、疑问焦点和语义结构。最后,根据疑问代词所关联的语义关系,统计了疑问焦点的概率分布,其中原因、修饰语和受事的占比最高,分别占26.53{\%}、16.73{\%}以及16.44{\%}。基于抽象语义表示的疑问句标注与分析可以为汉语疑问句研究提供基础理论与资源。
语用视角下复述句生成方式的类型考察(A Pragmatic Study of Generation Method of Paraphrase Sentence) (2020)

本文将汉语母语者的160份复述文本与其原文进行以小句为单位的逐句比对,发现其中出现了6484对复述句对。从其生成的方式来看,可以分为改换词语和重铸整句两大类。以语用学原理对这些复述句进行分析,发现与以往研究的复述现象不同的是:句对间往往不具有相同的逻辑语义真值,但在特定语境下却能传达同一个语用意义,具有等效的语用功能。这说明在自然语言处理中,识别进入真实交际中的复述句不仅依赖语法、语义知识库,还需要借助含有语用知识和语境信息的知识库。
面向汉语作为第二语言学习的个性化语法纠错(Personalizing Grammatical Error Correction for Chinese as a Second Language) (2020)

语法纠错任务旨在通过自然语言处理技术自动检测并纠正文本中的语序、拼写等语法错误。当前许多针对汉语的语法纠错方法已取得较好的效果,但往往忽略了学习者的个性化特征,如二语等级、母语背景等。因此,本文面向汉语作为第二语言的学习者,提出个性化语法纠错,对不同特征的学习者所犯的错误分别进行纠正,并构建了不同领域汉语学习者的数据集进行实验。实验结果表明,将语法纠错模型适应到学习者的各个领域后,性能得到明显提升。
中文问句的形式分类和资源建设(Formal classification and resource construction of Chinese questions) (2020)

本文归纳了问句形式在问句语料筛选中的作用,探索了问句分类必需的形式特征,同时通过人工标注建设了中文问句分类语料库,并在此基础上进行了基于规则和统计的分类实验,通过多轮实验迭代优化特征组合形成特征规则集,为当前问答提供形式上的分类基础。实验中,基于优化特征规则集的有限状态自动机可实现宏平均F1值为0.94;统计机器学习中随机森林模型的分类效果较好,F1值宏平均达到0.98,表明问句形式分类具有相当可行性和准确性。
基于组块分析的汉语块依存语法(Chinese Chunk-Based Dependency Grammar) (2020)

基于词单元的经典依存语法在面向中文的句子分析中遇到诸多汉语特性引起的困难。为此,本文提出汉语的块依存语法,以谓词为核心,以组块为研究对象,在句内和句间寻找谓词所支配的组块,构建句群级别的句法分析框架。这一操作不仅仅是提升叶子节点的语言单位,而且还针对汉语语义特点进行了分析方式和分析规则上的创新,能够较好地解决微观层次的逻辑结构知识,并为中观论元知识和宏观篇章知识打好铺垫。本文主要介绍了块依存语法理念、表示、分析方法及特点,并简要介绍了块依存树库的构建情况。截至目前为止,树库规模为187万字符(超过4万复句、10万小句),其中包含67{\%}新闻文本和32{\%}百科文本。
新支话题的句法成分和语义角色研究(A Study of Syntactic Constituent and Semantic Role of New Branch Topic) (2020)

话题的延续和转换是篇章中重要的语用功能。本文从句首话题共享的角度对话题延续和转换进行了分类,分为句首话题延续、句中子话题延续、完全话题转换、兼语话题转换、新支话题转换五种,进而对话题转换的特殊情况{---}{---}新支话题展开研究。基于33万字的广义话题结构语料库,本文对新支话题的句法成分、语义角色进行了统计和分析,发现能够成为新支话题的成分绝大多数是有关具体事物的体词性短语;句法成分方面,宾语从句或补语从句主语、主谓谓语句小主语、状语起始句的主语、句末宾语、连谓句非句末宾语、兼语句兼语、介词宾语甚至状语等都能成为新支话题,从而引出新支句,其中句末宾语做新支话题的情况最多,但未发现间接宾语作为新支话题的情况;语义角色方面,大部分主体论元(施事、感事、经事、主事)和客体论元(受事、系事、结果、对象、与事),及少数凭借论元(工具、方式,材料)和环境论元(处所、终点、路径)能成为新支话题引出新支句。其中,系事和受事成为新支话题情况最显著,施事、结果和对象次之。本文的研究揭示了句法、语义对话题转换这一语用现象的一种可能的约束途径。这将有助于人和计算机更深入地理解汉语篇章的话题转换机制,以期将这种语用现象逐步落实到语义直至句法的形式中,最终实现计算机对话题转换的自动分析。
眼动记录与主旨结构标注的关联性分析研究(Research on the correlation between eye movement feature and thematic structure label) (2020)

给定包含主旨概括句的汉语句群,针对该句群的内部结构标注是基于语言学的分析结果,而阅读句群时的眼动轨迹则蕴含着人的心理认知,两者的信息融合和内在关联性分析是该文主要工作。该文使用基于径向基函数支持向量机和递归特征消除的分类模型,根据标点小句片段对应的眼动指标数据预测该片段是否为包含主旨内容的关键信息,达到了0.76的准确率,并通过分析关键片段上眼动数据的分布特点,提取出对句群主旨概括信息区分度较好的眼动指标。
汉语竞争类多人游戏语言中疑问句的形式与功能(The Form and Function of Interrogatives in Multi-party Chinese Competitive Game Conversation) (2020)

本文基于自建的竞争类多人游戏对话语料库对汉语疑问句的形式与功能进行了考察。文章首先在前人研究的基础上将疑问句的类型分为五大类,然后考察不同类型的疑问句在对话中出现的位置与功能。研究显示,是非问(包括反复问)与特指问是最常见的类型,选择问使用频率最低。大部分疑问句会引起话轮转换,具有询问功能,此外,否定与指出事实也是疑问句的主要功能。特指问的否定功能与附加问指出事实的 功能比较突出。
融合目标端句法的AMR-to-Text生成(AMR-to-Text Generation with Target Syntax) (2020)

抽象语义表示到文本(AMR-to-Text)生成的任务是给定AMR图,生成相同语义表示的文本。可以把此任务当作一个从源端AMR图到目标端句子的机器翻译任务。目前存在的一些方法都在探索如何更好的对图结构进行建模。然而,它们都存在一个未限定的问题,因为在生成阶段许多句法的决策并不受语义图的约束,从而忽略了句子内部潜藏的句法信息。为了明确考虑这一不足,该文提出一种直接而有效的方法,显示的在AMR-to-Text生成的任务中融入句法信息,并在Transformer和目前该任务最优性能的模型上进行了实验。实验结果表明,在现存的两份标准英文数据集LDC2018E86和LDC2017T10上,都取得了显著的提升,达到了新的最高性能。
基于神经网络的连动句识别(Recognition of serial-verb sentences based on Neural Network) (2020)

连动句是具有连动结构的句子,是汉语中的特殊句法结构,在现代汉语中十分常见且使用频繁。连动句语法结构和语义关系都很复杂,在识别中存在许多问题,对此本文针对连动句的识别问题进行了研究,提出了一种基于神经网络的连动句识别方法。本方法分两步:第一步,运用简单的规则对语料进行预处理;第二步,用文本分类的思想,使用BERT编码,利用多层CNN与BiLSTM模型联合提取特征进行分类,进而完成连动句识别任务。在人工标注的语料上进行实验,实验结果达到92.71{\%}的准确率,F1值为87.41{\%}。