Semantic code search is the task of retrieving relevant code snippet given a natural language query. Different from typical information retrieval tasks, code search requires to bridge the semantic gap between the programming language and natural language, for better describing intrinsic concepts and semantics. Recently, deep neural network for code search has been a hot research topic. Typical methods for neural code search first represent the code snippet and query text as separate embeddings, and then use vector distance (e.g. dot-product or cosine) to calculate the semantic similarity between them. There exist many different ways for aggregating the variable length of code or query tokens into a learnable embedding, including bi-encoder, cross-encoder, and poly-encoder. The goal of the query encoder and code encoder is to produce embeddings that are close with each other for a related pair of query and the corresponding desired code snippet, in which the choice and design of encoder is very significant.
In this paper, we propose a novel deep semantic model which makes use of the utilities of not only the multi-modal sources, but also feature extractors such as self-attention, the aggregated vectors, combination of the intermediate representations. We apply the proposed model to tackle the CodeSearchNet challenge about semantic code search. We align cross-lingual embedding for multi-modality learning with large batches and hard example mining, and combine different learned representations for better enhancing the representation learning. Our model is trained on CodeSearchNet corpus and evaluated on the held-out data, the final model achieves 0.384 NDCG and won the first place in this benchmark. Models and code are available at https://github.com/overwindows/SemanticCodeSearch.git.
Many word clouds provide no semantics to the word placement, but use a random layout optimized solely for aesthetic purposes. We propose a novel approach to model word significance and word affinity within a document, and in comparison to a large background corpus. We demonstrate its usefulness for generating more meaningful word clouds as a visual summary of a given document. We then select keywords based on their significance and construct the word cloud based on the derived affinity. Based on a modified t-distributed stochastic neighbor embedding (t-SNE), we generate a semantic word placeme…
Université Claude Bernard Lyon1（里昂第一大学）；CNRS UMR5015（法国国家科学研究中心 UMR5015）；Université de Haute Alsace（上阿尔萨斯大学）；CNRS UMR7044（法国国家科学研究中心 UMR7044）
"Semantic Atlas" is a mathematic and statistic model to visualise word senses according to relations between words. The model, that has been applied to proximity relations from a corpus, has shown its ability to distinguish word senses as the corpus' contributors comprehend them. We propose to use the model and a specialised corpus in order to create automatically a specialised dictionary relative to the corpus' domain. A morpho-syntactic analysis performed on the corpus makes it possible to create the dictionary from syntactic relations between lexical units. The semantic resource can be used…
Pontifical Catholic University of Paraná（巴拉那天主教大学）；Philips Research North America（飞利浦北美研究院）
The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of natural language processing (NLP) algorithms. The absence of a multi-purpose clinical corpus outside the scope of the English language, especially in Brazilian Portuguese, is glaring and severely impacts scientific progress in the biomedical NLP field. In this study, we developed a semantically annotated corpus using clinical texts from multiple medi…
In this article, we propose an automatic process to build multi-lingual lexico-semantic resources. The goal of these resources is to browse semantically textual information contained in texts of different languages. This method uses a mathematical model called Atlas sémantiques in order to represent the different senses of each word. It uses the linguistic relations between words to create graphs that are projected into a semantic space. These projections constitute semantic maps that denote the sense trends of each given word. This model is fed with syntactic relations between words extracted…
MPI for Empirical Aesthetics（马克斯·普朗克经验美学研究所）；University of Stuttgart（斯图加特大学）；Technical University of Darmstadt（达姆施塔特工业大学）
Due to its semantic succinctness and novelty of expression, poetry is a great test bed for semantic change analysis. However, so far there is a scarcity of large diachronic corpora. Here, we provide a large corpus of German poetry which consists of about 75k poems with more than 11 million tokens, with poems ranging from the 16th to early 20th century. We then track semantic change in this corpus by investigating the rise of tropes (`love is magic') over time and detecting change points of meaning, which we find to occur particularly within the German Romantic period. Additionally, through sel…
Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each application. Although some semantic information is available in general-purpose knowledge bases such as WordNet and Cyc, many applications require domain-specific lexicons that represent words and categories for a particular topic. In this paper, we present a corpus-based method that can be used to build semantic lexicons for specific categories. The input to the system is a small set of seed words for a category and a representative text corpus. The output is a ranked list of …
University of Washington（华盛顿大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
We consider the case of a domain expert who wishes to explore the extent to which a particular idea is expressed in a text collection. We propose the task of semantically matching the idea, expressed as a natural language proposition, against a corpus. We create two preliminary tasks derived from existing datasets, and then introduce a more realistic one on disaster recovery designed for emergency managers, whom we engaged in a user study. On the latter, we find that a new model built from natural language entailment data produces higher-quality matches than simple word-vector averaging, both …
University of Melbourne（墨尔本大学）；National University of Singapore（新加坡国立大学）
Responding to the need for semantic lexical resources in natural language processing applications, we examine methods to acquire noun compounds (NCs), e.g., "orange juice", together with suitable fine-grained semantic interpretations, e.g., "squeezed from", which are directly usable as paraphrases. We employ bootstrapping and web statistics, and utilize the relationship between NCs and paraphrasing patterns to jointly extract NCs and such patterns in multiple alternating iterations. In evaluation, we found that having one compound noun fixed yields both a higher number of semantically interpre…
This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculati…
Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression. Fewer works address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, as well as the reader's perception of the emotion of the headline. This annotation task is comparably challengi…
Peking University Shenzhen Graduate School（北京大学深圳研究生院）
We present MedSim, a novel semantic SIMilarity method based on public well-established bio-MEDical knowledge graphs (KGs) and large-scale corpus, to study the therapeutic substitution of antibiotics. Besides hierarchy and corpus of KGs, MedSim further interprets medicine characteristics by constructing multi-dimensional medicine-specific feature vectors. Dataset of 528 antibiotic pairs scored by doctors is applied for evaluation and MedSim has produced statistically significant improvement over other semantic similarity methods. Furthermore, some promising applications of MedSim in drug substi…
The course description provided by instructors is an essential piece of information as it defines what is expected from the instructor and what he/she is going to deliver during a particular course. One of the key components of a course description is the Learning Objectives section. The contents of this section are used by program managers who are tasked to compare and match two different courses during the development of Transfer Agreements between various institutions. This research introduces the development of semantic similarity algorithms to calculate the similarity between two learning…
Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language. Moreover, there is a dearth of annotated corpora for investigating the cross-linguistic variation of adposition semantics, or for building multilingual disambiguation systems. This paper presents a corpus in which all adpositions have been semantically annotated in Mandarin Chinese; to the best of our knowledge, this is the first Chinese corpus to be broadly annotated with adposition semantics. Our approach adapts a framework that defined a general set of supe…
Calculating the semantic similarity between sentences is a long dealt problem in the area of natural language processing. The semantic analysis field has a crucial role to play in the research related to the text analytics. The semantic similarity differs as the domain of operation differs. In this paper, we present a methodology which deals with this issue by incorporating semantic similarity and corpus statistics. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. The methodology can be applied in a v…
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）
We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bibliographic references, as well as structured full text for 8.1M open access papers. Full text is annotated with automatically-detected inline mentions of citations, figures, and tables, each linked to their corresponding paper objects. In S2ORC, we aggregate papers from hundreds of academic publishers and digital archives into a unified source, and create the largest publicly-available collection of machine-readable …
Research and Technology Center, Robert Bosch（罗伯特·博世研究与技术中心）
Word embeddings are a basic building block of modern NLP pipelines. Efforts have been made to learn rich, efficient, and interpretable embeddings for large generic datasets available in the public domain. However, these embeddings have limited applicability for small corpora from specific domains such as automotive, manufacturing, maintenance and support, etc. In this work, we present a comprehensive notion of interpretability for word embeddings and propose a novel method to generate highly interpretable and efficient embeddings for a domain-specific small corpus. We report the evaluation res…
University Ferhat Abbes Setif 1（费哈特·阿巴斯·塞蒂夫1大学）；Badji Mokhtar University（巴吉·穆克塔尔大学）
in this paper, we present a corpus linguistics based approach applied to analyzing digitized classical multilingual novels and narrative texts, from a semantic point of view. Digitized novels such as "the hobbit (Tolkien J. R. R., 1937)" and "the hound of the Baskervilles (Doyle A. C. 1901-1902)", which were widely translated to dozens of languages, provide rich materials for analyzing languages differences from several perspectives and within a number of disciplines like linguistics, philosophy and cognitive science. Taking motion events conceptualization as a case study, this paper, focus on…
We present new data and semantic parsing methods for the problem of mapping English sentences to Bash commands (NL2Bash). Our long-term goal is to enable any user to perform operations such as file manipulation, search, and application-specific scripting by simply stating their goals in English. We take a first step in this domain, by providing a new dataset of challenging but commonly used Bash commands and expert-written English descriptions, along with baseline methods to establish performance levels on this task.
University of Liverpool（利物浦大学）；Shizuoka University（静冈大学）；National Institute of Informatics（国立信息学研究所）
Methods for learning word representations using large text corpora have received much attention lately due to their impressive performance in numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection. Despite their success, these data-driven word representation learning methods do not consider the rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNet that represent the meanings of words by definin…
Khulna University of Engineering & Technology (KUET)（库尔纳工程技术大学）；the University of Bolton（博尔顿大学）；University of Dhaka（达卡大学）
The aim of this paper is to evaluate a Text to Knowledge Mapping (TKM) Prototype. The prototype is domain-specific, the purpose of which is to map instructional text onto a knowledge domain. The context of the knowledge domain is DC electrical circuit. During development, the prototype has been tested with a limited data set from the domain. The prototype reached a stage where it needs to be evaluated with a representative linguistic data set called corpus. A corpus is a collection of text drawn from typical sources which can be used as a test data set to evaluate NLP systems. As there is no a…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；University of Toronto（多伦多大学）；Academia Sinica（中央研究院）
Chinese dynastic histories form a large continuous linguistic space of approximately 2000 years, from the 3rd century BCE to the 18th century CE. The histories are documented in Classical (Literary) Chinese in a corpus of over 20 million characters, suitable for the computational analysis of historical lexicon and semantic change. However, there is no freely available open-source corpus of these histories, making Classical Chinese low-resource. This project introduces a new open-source corpus of twenty-four dynastic histories covered by Creative Commons license. An original list of Classical C…
Dartmouth College（达特茅斯学院）；Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）
Objective: Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that a concept can be referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework for incorporating domain knowledge from multiple ontologies into a distributional semantic model, learned from a corpus of clinical text. Materials and Methods: We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based part, we use the Medical Subject Headings (Me…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Massachusetts Institute of Technology（麻省理工学院）
Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express …
We release a corpus of 43 million atomic edits across 8 languages. These edits are mined from Wikipedia edit history and consist of instances in which a human editor has inserted a single contiguous phrase into, or deleted a single contiguous phrase from, an existing sentence. We use the collected data to show that the language generated during editing differs from the language that we observe in standard corpora, and that models trained on edits encode different aspects of semantics and discourse than models trained on raw, unstructured text. We release the full corpus as a resource to aid on…
The categorical compositional distributional (DisCoCat) model of meaning rigorously connects distributional semantics and pregroup grammars, and has found a variety of applications in computational linguistics. From a more abstract standpoint, the DisCoCat paradigm predicates the construction of a mapping from syntax to categorical semantics. In this work we present a concrete construction of one such mapping, from a toy model of syntax for corpora annotated with constituent structure trees, to categorical semantics taking place in a category of free R-semimodules over an involutive commutativ…
HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Hong Kong University of Science and Technology（香港科技大学）
Given a small set of seed entities (e.g., ``USA'', ``Russia''), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus. Set expansion benefits a wide range of downstream applications in knowledge discovery, such as web search, taxonomy construction, and query suggestion. Existing corpus-based set expansion algorithms typically bootstrap the given seeds by incorporating lexical patterns and distributional similarity. However, due to no negative sets provided explicitly, these methods suffer from sema…
National and Kapodistrian University of Athens（雅典国立与卡波迪斯特里亚大学）
One of the major challenges that NLP faces is metaphor detection, especially by automatic means, a task that becomes even more difficult for languages lacking in linguistic resources and tools. Our purpose is the automatic differentiation between literal and metaphorical meaning in authentic non-annotated phrases from the Corpus of Greek Texts by means of computational methods of machine learning. For this purpose the theoretical background of distributional semantics is discussed and employed. Distributional Semantics Theory develops concepts and methods for the quantification and classificat…
A plethora of words are used to describe the spectrum of human emotions, but how many emotions are there really, and how do they interact? Over the past few decades, several theories of emotion have been proposed, each based around the existence of a set of 'basic emotions', and each supported by an extensive variety of research including studies in facial expression, ethology, neurology and physiology. Here we present research based on a theory that people transmit their understanding of emotions through the language they use surrounding emotion keywords. Using a labelled corpus of over 21,00…
University of Edinburgh（爱丁堡大学）；IHMC（人类与机器认知研究所）；University of Utah（犹他大学）；University of Colorado at Boulder（科罗拉多大学博尔德分校）
We present the first corpus annotated with preposition supersenses, unlexicalized categories for semantic functions that can be marked by English prepositions (Schneider et al., 2015). That scheme improves upon its predecessors to better facilitate comprehensive manual annotation. Moreover, unlike the previous schemes, the preposition supersenses are organized hierarchically. Our data will be publicly released on the web upon publication.
This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nod…
Previous studies on Chinese semantic role labeling (SRL) have concentrated on single semantically annotated corpus. But the training data of single corpus is often limited. Meanwhile, there usually exists other semantically annotated corpora for Chinese SRL scattered across different annotation frameworks. Data sparsity remains a bottleneck. This situation calls for larger training datasets, or effective approaches which can take advantage of highly heterogeneous data. In these papers, we focus mainly on the latter, that is, to improve Chinese SRL by using heterogeneous corpora together. We pr…
University of Oslo（奥斯陆大学）；University of Tyumen（秋明大学）
In this paper, we present a distributional word embedding model trained on one of the largest available Russian corpora: Araneum Russicum Maximum (over 10 billion words crawled from the web). We compare this model to the model trained on the Russian National Corpus (RNC). The two corpora are much different in their size and compilation procedures. We test these differences by evaluating the trained models against the Russian part of the Multilingual SimLex999 semantic similarity dataset. We detect and describe numerous issues in this dataset and publish a new corrected version. Aside from the …
Universidad de Buenos Aires（布宜诺斯艾利斯大学）；Instituto de Investigación en Ciencias de la Computación, UBA-CONICET（布宜诺斯艾利斯大学-阿根廷国家科学技术研究委员会计算机科学研究所）；Universidad Torcuato Di Tella - CONICET（托尔夸托·迪特利亚大学-阿根廷国家科学技术研究委员会）
Latent Semantic Analysis (LSA) and Word2vec are some of the most widely used word embeddings. Despite the popularity of these techniques, the precise mechanisms by which they acquire new semantic relations between words remain unclear. In the present article we investigate whether LSA and Word2vec capacity to identify relevant semantic dimensions increases with size of corpus. One intuitive hypothesis is that the capacity to identify relevant dimensions should increase as the amount of data increases. However, if corpus size grow in topics which are not specific to the domain of interest, sign…
This article presents an automatic frame analysis system evaluated on a corpus of French encyclopedic history texts annotated according to the FrameNet formalism. The chosen approach relies on an integrated sequence labeling model which jointly optimizes frame identification and semantic role segmentation and identification. The purpose of this study is to analyze the task complexity from several dimensions. Hence we provide detailed evaluations from a feature selection point of view and from the data point of view.
This paper introduces the hmBlogs corpus for Persian, as a low resource language. This corpus has been prepared based on a collection of nearly 20 million blog posts over a period of about 15 years from a space of Persian blogs and includes more than 6.8 billion tokens. It can be claimed that this corpus is currently the largest Persian corpus that has been prepared independently for the Persian language. This corpus is presented in both raw and preprocessed forms, and based on the preprocessed corpus some word embedding models are produced. By the provided models, the hmBlogs is compared with…
Laboratoire de Physique Statistique, École Normale Supérieure, PSL Research University（巴黎高等师范学院统计物理实验室，PSL研究大学）；Université Paris Diderot, Sorbonne Paris-Cité（巴黎狄德罗大学，索邦巴黎西岱大学）；Sorbonne Universités, UPMC – Univ. Paris 06（索邦大学，巴黎第六大学）；CNRS（法国国家科学研究中心）；Laboratoire Langues, Textes, Traitements informatique, Cognition (Lattice, CNRS, ENS & Université Paris 3, PSL & USPC), École normale supérieure（语言、文本、信息处理与认知实验室（Lattice，法国国家科学研究中心、巴黎高等师范学院与巴黎第三大学，PSL与USPC），巴黎高等师范学院）；École des Hautes Études en Sciences Sociales, PSL Research University（社会科学高等学院，PSL研究大学）；Centre d’Analyse et de Mathématique Sociales（社会分析与数学中心）
It is generally believed that, when a linguistic item acquires a new meaning, its overall frequency of use in the language rises with time with an S-shaped growth curve. Yet, this claim has only been supported by a limited number of case studies. In this paper, we provide the first corpus-based quantitative confirmation of the genericity of the S-curve in language change. Moreover, we uncover another generic pattern, a latency phase of variable duration preceding the S-growth, during which the frequency of use of the semantically expanding word remains low and more or less constant. We also pr…
Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
Explicit Semantic Analysis (ESA) is a technique used to represent a piece of text as a vector in the space of concepts, such as Articles found in Wikipedia. We propose a methodology to incorporate knowledge of Inter-relatedness between Wikipedia Articles to the vectors obtained from ESA using a technique called Retrofitting to improve the performance of subsequent tasks that use ESA to form vector embeddings. Especially we use an undirected Graph to represent this knowledge with nodes as Articles and edges as inter relations between two Articles. Here, we also emphasize how the ESA step could …
University of West Bohemia（西波希米亚大学）；NTIS—New Technologies for the Information Society（NTIS—新信息技术学会）
The word embedding methods have been proven to be very useful in many tasks of NLP (Natural Language Processing). Much has been investigated about word embeddings of English words and phrases, but only little attention has been dedicated to other languages. Our goal in this paper is to explore the behavior of state-of-the-art word embedding methods on Czech, the language that is characterized by very rich morphology. We introduce new corpus for word analogy task that inspects syntactic, morphosyntactic and semantic properties of Czech words and phrases. We experiment with Word2Vec and GloVe al…
A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described. The suggested approach builds on the authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well as on a number of prior papers and approaches from the statistical language learning literature. If successful, this approach would enable the mining of all the information needed to power a natural language comprehension and generation system, directly from a large, unannotated corpus.
When speaking or writing, people omit information that seems clear and evident, such that only part of the message is expressed in words. Especially in argumentative texts it is very common that (important) parts of the argument are implied and omitted. We hypothesize that for argument analysis it will be beneficial to reconstruct this implied information. As a starting point for filling such knowledge gaps, we build a corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. To learn more about the characteristics of both the argumentative …
Russian-Armenian University（俄罗斯-亚美尼亚大学）；Ivannikov Laboratory for System Programming（伊万尼科夫系统编程实验室）
In this work, we employ a semi-automatic method based on back translation to generate a sentential paraphrase corpus for the Armenian language. The initial collection of sentences is translated from Armenian to English and back twice, resulting in pairs of lexically distant but semantically similar sentences. The generated paraphrases are then manually reviewed and annotated. Using the method train and test datasets are created, containing 2360 paraphrases in total. In addition, the datasets are used to train and evaluate BERTbased models for detecting paraphrase in Armenian, achieving results…
Brown University（布朗大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Sussex（苏塞克斯大学）
We present a novel corpus of 445 human- and computer-generated documents, comprising about 27,000 clauses, annotated for semantic clause types and coherence relations that allow for nuanced comparison of artificial and natural discourse modes. The corpus covers both formal and informal discourse, and contains documents generated using fine-tuned GPT-2 (Zellers et al., 2019) and GPT-3(Brown et al., 2020). We showcase the usefulness of this corpus for detailed discourse analysis of text generation by providing preliminary evidence that less numerous, shorter and more often incoherent clause rela…
Microsoft Egypt Development Center（微软埃及开发中心）；Microsoft Corporation（微软公司）
This paper describes our submission to the WMT20 sentence filtering task. We combine scores from (1) a custom LASER built for each source language, (2) a classifier built to distinguish positive and negative pairs by semantic alignment, and (3) the original scores included in the task devkit. For the mBART finetuning setup, provided by the organizers, our method shows 7% and 5% relative improvement over baseline, in sacreBLEU score on the test set for Pashto and Khmer respectively.
University of St.Gallen（圣加仑大学）；University of Passau（帕绍大学）；University of Manchester（曼彻斯特大学）
We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences. Contrary to previously proposed text simplification corpora, which contain only a small number of split examples, we present a dataset where each input sentence is broken down into a set of minimal propositions, i.e. a sequence of sound, self-contained utterances with each of them presenting a minimal semantic unit that cannot be further decomposed into meaningful propositions. This corpus is useful for developing sentence splitting approaches that learn how to …
Norwegian Computing Center（挪威计算中心）；University of Oslo（奥斯陆大学）；Universitat Rovira i Virgili（罗维拉-威尔吉利大学）
We present a novel benchmark and associated evaluation metrics for assessing the performance of text anonymization methods. Text anonymization, defined as the task of editing a text document to prevent the disclosure of personal information, currently suffers from a shortage of privacy-oriented annotated text resources, making it difficult to properly evaluate the level of privacy protection offered by various anonymization methods. This paper presents TAB (Text Anonymization Benchmark), a new, open-source annotated corpus developed to address this shortage. The corpus comprises 1,268 English-…
We present a resource for the task of FrameNet semantic frame disambiguation of over 5,000 word-sentence pairs from the Wikipedia corpus. The annotations were collected using a novel crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. In contrast to the typical approach of attributing the best single frame to each word, we provide a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. This is based on the idea that inter-annotator disagreement is at least partly caused by ambiguity th…
We present the WASABI Song Corpus, a large corpus of songs enriched with metadata extracted from music databases on the Web, and resulting from the processing of song lyrics and from audio analysis. More specifically, given that lyrics encode an important part of the semantics of a song, we focus here on the description of the methods we proposed to extract relevant information from the lyrics, such as their structure segmentation, their topics, the explicitness of the lyrics content, the salient passages of a song and the emotions conveyed. The creation of the resource is still ongoing: so fa…
The Gutenberg Literary English Corpus (GLEC, Jacobs, 2018a) provides a rich source of textual data for research in digital humanities, computational linguistics or neurocognitive poetics. In this study we address differences among the different literature categories in GLEC, as well as differences between authors. We report the results of three studies providing i) topic and sentiment analyses for six text categories of GLEC (i.e., children and youth, essays, novels, plays, poems, stories) and its >100 authors, ii) novel measures of semantic complexity as indices of the literariness, creativit…
