Sememes, defined as the minimum semantic units of human languages in linguistics, have been proven useful in many NLP tasks. Since manual construction and update of sememe knowledge bases (KBs) are costly, the task of automatic sememe prediction has been proposed to assist sememe annotation. In this paper, we explore the approach of applying dictionary definitions to predicting sememes for unannotated words. We find that sememes of each word are usually semantically matched to different words in its dictionary definition, and we name this matching relationship local semantic correspondence. Accordingly, we propose a Sememe Correspondence Pooling (SCorP) model, which is able to capture this kind of matching to predict sememes. We evaluate our model and baseline methods on a famous sememe KB HowNet and find that our model achieves state-of-the-art performance. Moreover, further quantitative analysis shows that our model can properly learn the local semantic correspondence between sememes and words in dictionary definitions, which explains the effectiveness of our model. The source codes of this paper can be obtained from https://github.com/thunlp/scorp.
In this paper, we present an open sememe-based lexical knowledge base OpenHowNet. Based on well-known HowNet, OpenHowNet comprises three components: core data which is composed of more than 100 thousand senses annotated with sememes, OpenHowNet Web which gives a brief introduction to OpenHowNet as well as provides online exhibition of OpenHowNet information, and OpenHowNet API which includes several useful APIs such as accessing OpenHowNet core data and drawing sememe tree structures of senses. In the main text, we first give some backgrounds including definition of sememe and details of HowNe…
In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks. Second, we propose an end-to-end joint model for the task, which exploits the state-of-the-art BERT representations as encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks. We manually annotate a benchmark dataset for the task and then conduct experiments on it. Results demonstrate…
Shenyuan Honors College, Beihang University（北京航空航天大学沈元荣誉学院）；Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University（江苏师范大学江苏语言能力协同创新中心）；Tencent（腾讯）
Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic common-sense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the…
Beijing Language and Culture University（北京语言大学）；The University of Hong Kong（香港大学）；Tsinghua University（清华大学）
Chinese definition modeling is a challenging task that generates a dictionary definition in Chinese for a given Chinese word. To accomplish this task, we construct the Chinese Definition Modeling Corpus (CDM), which contains triples of word, sememes and the corresponding definition. We present two novel models to improve Chinese definition modeling: the Adaptive-Attention model (AAM) and the Self- and Adaptive-Attention Model (SAAM). AAM successfully incorporates sememes for generating the definition with an adaptive attention mechanism. It has the capability to decide which sememes to focus o…
Huge numbers of new words emerge every day, leading to a great need for representing them with semantic meaning that is understandable to NLP systems. Sememes are defined as the minimum semantic units of human languages, the combination of which can represent the meaning of a word. Manual construction of sememe based knowledge bases is time-consuming and labor-intensive. Fortunately, communities are devoted to composing the descriptions of words in the wiki websites. In this paper, we explore to automatically predict lexical sememes based on the descriptions of the words in the wiki websites. …
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Institute Guo Qiang, Tsinghua University（清华大学国强研究院）；International Innovation Center of Tsinghua University, Shanghai（清华大学上海国际创新中心）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Peng Cheng Laboratory（鹏城实验室）
In linguistics, a sememe is defined as the minimum semantic unit of languages. Sememe knowledge bases (KBs), which are built by manually annotating words with sememes, have been successfully applied to various NLP tasks. However, existing sememe KBs only cover a few languages, which hinders the wide utilization of sememes. To address this issue, the task of sememe prediction for BabelNet synsets (SPBS) is presented, aiming to build a multilingual sememe KB based on BabelNet, a multilingual encyclopedia dictionary. By automatically predicting sememes for a BabelNet synset, the words in many lan…
Hefei University of Technology（合肥工业大学）；Mininglamp Academy of Sciences（明略科学院）
Lexical simplification has attracted much attention in many languages, which is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. Although the richness of vocabulary in Chinese makes the text very difficult to read for children and non-native speakers, there is no research work for Chinese lexical simplification (CLS) task. To circumvent difficulties in acquiring annotations, we manually create the first benchmark dataset for CLS, which can be used for evaluating the lexical simplification systems automatically. In order to acquire more…
Economic issues related to the information processing techniques are very important. The development of such technologies is a major asset for developing countries like Cambodia and Laos, and emerging ones like Vietnam, Malaysia and Thailand. The MotAMot project aims to computerize an under-resourced language: Khmer, spoken mainly in Cambodia. The main goal of the project is the development of a multilingual lexical system targeted for Khmer. The macrostructure is a pivot one with each word sense of each language linked to a pivot axi. The microstructure comes from a simplification of the expl…
Wuhan University（武汉大学）；Guangdong University of Foreign Studies（广东外语外贸大学）
Lexical chain consists of cohesion words in a document, which implies the underlying structure of a text, and thus facilitates downstream NLP tasks. Nevertheless, existing work focuses on detecting the simple surface lexicons with shallow syntax associations, ignoring the semantic-aware lexical compounds as well as the latent semantic frames, (e.g., topic), which can be much more crucial for real-world NLP applications. In this paper, we introduce a novel task, Nominal Compound Chain Extraction (NCCE), extracting and clustering all the nominal compounds that share identical semantic topics. In…
The frame-based knowledge representation model adopted in IDHS (Intelligent Dictionary Help System) is described in this paper. It is used to represent the lexical knowledge acquired automatically from a conventional dictionary. Moreover, the enrichment processes that have been performed on the Dictionary Knowledge Base and the dynamic exploitation of this knowledge - both based on the exploitation of the properties of lexical semantic relations - are also described.
University of Oslo（奥斯陆大学）；Oslo University Hospital Rikshospitalet（奥斯陆大学医院里克医院）
We present a large Norwegian lexical resource of categorized medical terms. The resource merges information from large medical databases, and contains over 77,000 unique entries, including automatically mapped terms from a Norwegian medical dictionary. We describe the methodology behind this automatic dictionary entry mapping based on keywords and suffixes and further present the results of a manual evaluation performed on a subset by a domain expert. The evaluation indicated that ca. 80% of the mappings were correct.
Bilingual machine-readable dictionaries are knowledge resources useful in many automatic tasks. However, compared to monolingual computational lexicons like WordNet, bilingual dictionaries typically provide a lower amount of structured information, such as lexical and semantic relations, and often do not cover the entire range of possible translations for a word of interest. In this paper we present Cycles and Quasi-Cycles (CQC), a novel algorithm for the automated disambiguation of ambiguous translations in the lexical entries of a bilingual machine-readable dictionary. The dictionary is repr…
University of Cambridge（剑桥大学）；Courant Institute of Mathematical Sciences（库朗数学科学研究所）；New York University（纽约大学）；Université de Montréal（蒙特利尔大学）
Distributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phrases and sentences has proved far harder. We propose using the definitions found in everyday dictionaries as a means of bridging this gap between lexical and phrasal semantics. Neural language embedding models can be effectively trained to map dictionary definitions (phrases) to (lexical) representations of the words defined by those definitions. We present two applications of these architectures: "reverse dictionarie…
This paper explores the automatic construction of a multilingual Lexical Knowledge Base from pre-existing lexical resources. We present a new and robust approach for linking already existing lexical/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select --among all the candidate translations proposed by a bilingual dictionary-- the right English WordNet synset for each sense in a taxonomy automatically derived from a Spanish monolingual dictionary. Although on average, there are 15 possible WordNet connections for each sense in the taxonomy, the metho…
An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and corresponding natural language realizations. Typically, labor-intensive knowledge-based methods are used to construct the dictionary. We instead propose to acquire it automatically via a novel multiple-pass algorithm employing multiple-sequence alignment, a technique commonly used in bioinformatics. Crucially, our method leverages latent information contained in multi-parallel corpora -- datasets that supply several verbalizations of the corresponding semantics rather tha…
Vassar College（瓦萨学院）；University of Brighton（布莱顿大学）；LORIA/CNRS（洛里亚国家计算机科学与自动化实验室/法国国家科学研究中心）
We show that a general model of lexical information conforms to an abstract model that reflects the hierarchy of information found in a typical dictionary entry. We show that this model can be mapped into a well-formed XML document, and how the XSL transformation language can be used to implement a semantics defined over the abstract model to enable extraction and manipulation of the information in any format.
Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0
Faculty of Information Technology and Computer Science, Sinai University（信息科技与计算机科学学院， Sinai大学）；Inria；ISO committee TC 37 (Language and terminology)（ISO术语委员会TC 37（语言和术语））
This paper deals with the exploitation of dictionaries for the semi-automatic construction of lexicons and lexical knowledge bases. The final goal of our research is to enrich the Basque Lexical Database with semantic information such as senses, definitions, semantic relations, etc., extracted from a Basque monolingual dictionary. The work here presented focuses on the extraction of the semantic relations that best characterise the headword, that is, those of synonymy, antonymy, hypernymy, and other relations marked by specific relators and derivation. All nominal, verbal and adjectival entrie…
This paper describes an approach to the automatic identification of lexical information in on-line dictionaries. This approach uses bootstrapping techniques, specifically so that ambiguity in the dictionary text can be treated properly. This approach consists of processing an on-line dictionary multiple times, each time refining the lexical information previously acquired and identifying new lexical information. The strength of this approach is that lexical information can be acquired from definitions which are syntactically ambiguous, given that information acquired during the first pass can …
Peking University（北京大学）；Beijing Key Laboratory of Internet Culture and Digital Dissemination Research（北京网络文化与数字传播重点实验室）
Using low dimensional vector space to represent words has been very effective in many NLP tasks. However, it doesn't work well when faced with the problem of rare and unseen words. In this paper, we propose to leverage the knowledge in semantic dictionary in combination with some morphological information to build an enhanced vector space. We get an improvement of 2.3% over the state-of-the-art Heidel Time system in temporal expression recognition, and obtain a large gain in other name entity recognition (NER) tasks. The semantic dictionary Hownet alone also shows promising results in computin…
A large class of unsupervised algorithms for Word Sense Disambiguation (WSD) is that of dictionary-based methods. Various algorithms have as the root Lesk's algorithm, which exploits the sense definitions in the dictionary directly. Our approach uses the lexical base WordNet for a new algorithm originated in Lesk's, namely "chain algorithm for disambiguation of all words", CHAD. We show how translation from a language into another one and also text entailment verification could be accomplished by this disambiguation.
Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction
Department of Computer Science（计算机科学系）；Department of EECE（电子工程与计算机科学系）；Faculty of Computers and Information, Cairo University（开罗大学计算机与信息学院）；Faculty of Engineering, Cairo University（开罗大学工程学院）；Department of Arabic Language and Literatures（阿拉伯语言与文学系）；Faculty of Arts, Cairo University（开罗大学艺术学院）
Insight SFI Research Centre for Data Analytics（Insight SFI数据分析研究中心）；School of ICS（信息与计算科学学院）；University College Dublin（都柏林大学学院）
This paper presents a new method for automatically detecting words with lexical gender in large-scale language datasets. Currently, the evaluation of gender bias in natural language processing relies on manually compiled lexicons of gendered expressions, such as pronouns ('he', 'she', etc.) and nouns with lexical gender ('mother', 'boyfriend', 'policewoman', etc.). However, manual compilation of such lists can lead to static information if they are not periodically updated and often involve value judgments by individual annotators and researchers. Moreover, terms not included in the list fall …
Knowledge structures called Concept Clustering Knowledge Graphs (CCKGs) are introduced along with a process for their construction from a machine readable dictionary. CCKGs contain multiple concepts interrelated through multiple semantic relations together forming a semantic cluster represented by a conceptual graph. The knowledge acquisition is performed on a children's first dictionary. A collection of conceptual clusters together can form the basis of a lexical knowledge base, where each CCKG contains a limited number of highly connected words giving useful information about a particular do…
University of Rochester（罗切斯特大学）；Institute of Human and Machine Cognition（人机认知研究所）
Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and example…
This paper proposes a method for measuring semantic similarity between words as a new tool for text analysis. The similarity is measured on a semantic network constructed systematically from a subset of the English dictionary, LDOCE (Longman Dictionary of Contemporary English). Spreading activation on the network can directly compute the similarity between any two words in the Longman Defining Vocabulary, and indirectly the similarity of all the other words in LDOCE. The similarity represents the strength of lexical cohesion or semantic relation, and also provides valuable information about si…
This work describes a system that performs morphological analysis and generation of Pali words. The system works with regular inflectional paradigms and a lexical database. The generator is used to build a collection of inflected and derived words, which in turn is used by the analyzer. Generating and storing morphological forms along with the corresponding morphological information allows for efficient and simple look up by the analyzer. Indeed, by looking up a word and extracting the attached morphological information, the analyzer does not have to compute this information. As we must, howev…
Université du Québec à Montréal（魁北克大学蒙特利尔分校）；Université du Québec à Chicoutimi（魁北克大学希库蒂米分校）；Universidade de São Paulo（圣保罗大学）；University of Southampton（南安普顿大学）；Centre de recherches mathématiques, Université de Montréal（蒙特利尔大学数学研究中心）
How many words are needed to define all the words in a dictionary? Graph-theoretic analysis reveals that about 10% of a dictionary is a unique Kernel of words that define one another and all the rest, but this is not the smallest such subset. The Kernel consists of one huge strongly connected component (SCC), about half its size, the Core, surrounded by many small SCCs, the Satellites. Core words can define one another but not the rest of the dictionary. The Kernel also contains many overlapping Minimal Grounding Sets (MGSs), each about the same size as the Core, each part-Core, part-Satellite…
LIMSI-CNRS（LIMSI-CNRS法国国家科学研究中心）；Université Paris XI（巴黎第十一大学）
The study of the Tip of the Tongue phenomenon (TOT) provides valuable clues and insights concerning the organisation of the mental lexicon (meaning, number of syllables, relation with other words, etc.). This paper describes a tool based on psycho-linguistic observations concerning the TOT phenomenon. We've built it to enable a speaker/writer to find the word he is looking for, word he may know, but which he is unable to access in time. We try to simulate the TOT phenomenon by creating a situation where the system knows the target word, yet is unable to access it. In order to find the target w…
In an effort to better understand meaning from natural language texts, we explore methods aimed at organizing lexical objects into contexts. A number of these methods for organization fall into a family defined by word ordering. Unlike demographic or spatial partitions of data, these collocation models are of special importance for their universal applicability. While we are interested here in text and have framed our treatment appropriately, our work is potentially applicable to other areas of research (e.g., speech, genomics, and mobility patterns) where one has ordered categorical data, (e.…
Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries
Nara Institute of Science and Technology（奈良科学技术大学）；RIKEN（日本研究机构）；Resolve Research（Resolve研究）；The University of Tokyo（东京大学）；Tohoku University（东北大学）；Serpenti Sei Japan（日本Serpenti Sei）
Towards Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars
Department of Computer Science, Faculty of Computers and Information, Cairo University（计算机科学系，计算机与信息学院，开罗大学）；Department of EECE, Faculty of Engineering, Cairo University（电子工程系，工程学院，开罗大学）；Department of Arabic Language and Literatures, Faculty of Arts, Cairo University（阿拉伯语言与文学系，艺术学院，开罗大学）
Lexical resources such as WordNet and the EDR electronic dictionary have been used in several NLP tasks. Probably, partly due to the fact that the EDR is not freely available, WordNet has been used far more often than the EDR. We have used both resources on the same task in order to make a comparison possible. The task is automatic assignment of keywords to multi-party dialogue episodes (i.e. thematically coherent stretches of spoken text). We show that the use of lexical resources in such a task results in slightly higher performances than the use of a purely statistically based method.
Vowels in Arabic are optional orthographic symbols written as diacritics above or below letters. In Arabic texts, typically more than 97 percent of written words do not explicitly show any of the vowels they contain; that is to say, depending on the author, genre and field, less than 3 percent of words include any explicit vowel. Although numerous studies have been published on the issue of restoring the omitted vowels in speech technologies, little attention has been given to this problem in papers dedicated to written Arabic technologies.f In this research, we present Arabic-Unitex, an Arabi…
How Do Lexical Senses Correspond Between Spoken German and German Sign Language?
Institute for Computational Linguistics, University of Heidelberg（计算语言学研究所，海德堡大学）；Department of Computing Science, University of Aberdeen（计算机科学系，阿伯丁大学）
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…
Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. But, these tasks only evaluate lexical semantics indirectly. In this paper, we study whether it is possible to utilize distributed representations to generate dictionary definitions of words, as a more direct and transparent representation of the embeddings' semantics. We introduce definition modeling, the task of generating a definition for a given word and its embedding. We present several definition model architectures ba…
Finding the appropriate words to convey concepts (i.e., lexical access) is essential for effective communication. Reverse dictionaries fulfill this need by helping individuals to find the word(s) which could relate to a specific concept or idea. To the best of our knowledge, this resource has not been available for the Persian language. In this paper, we compare four different architectures for implementing a Persian reverse dictionary (PREDICT). We evaluate our models using (phrase,word) tuples extracted from the only Persian dictionaries available online, namely Amid, Moein, and Dehkhoda whe…
Creating a Hybrid Rule and Neural Network Based Semantic Tagger using Silver Standard Data: the PyMUSAS framework for Multilingual Semantic Annotation
1 UCREL, Lancaster University, UK；2 Manchester Metropolitan University, UK；3 Centre for Language；Communication Studies, Trinity College, Dublin, Ireland；4 School of English, Communication；Philosophy, Cardiff University, Wales；5 Independent Researcher；6 Fiontar \& Scoil na Gaeilge, Dublin City University, Ireland；7 Hubei University, China
How many words does ChatGPT know? The answer is ChatWords
Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Universidad Politécnica de Madrid（马德里理工大学）；Universidad de Valladolid（巴利亚多利德大学）；Northeastern University（东北大学）
Search is one of the key functionalities in digital platforms and applications such as an electronic dictionary, a search engine, and an e-commerce platform. While the search function in some languages is trivial, Khmer word search is challenging given its complex writing system. Multiple orders of characters and different spelling realizations of words impose a constraint on Khmer word search functionality. Additionally, spelling mistakes are common since robust spellcheckers are not commonly available across the input device platforms. These challenges hinder the use of Khmer language in sea…
University of Technology Sydney（悉尼科技大学）；China University of Petroleum (East China)（中国石油大学（华东））；University of Melbourne（墨尔本大学）；JD Explore Academy（京东探索研究院）；The University of Sydney（悉尼大学）
Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification is rarely studied. Several lines of text attack methods have been proposed in the literature, including character-level, word-level, and sentence-level attacks. However, it is still a challenge to minimize the number of word changes necessary to induce misclassification, while simultaneously ensuring lexical correctness, syntactic soundness, and semantic similarity. In this paper, we propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) m…
Zur Darstellung eines mehrstufigen Prototypbegriffs in der multilingualen automatischen Sprachgenerierung: vom Korpus über word embeddings bis hin zum automatischen Wörterbuch
Universidade de Santiago de Compostela（圣地亚哥-德孔波斯特拉大学）
University of California, Los Angeles（加州大学洛杉矶分校）；The State University of New York, Stony Brook（纽约州立大学石溪分校）
Bilingual word embeddings have been widely used to capture the similarity of lexical semantics in different human languages. However, many applications, such as cross-lingual semantic search and question answering, can be largely benefited from the cross-lingual correspondence between sentences and lexicons. To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries. The proposed model is trained to map the literal word definitions to the cross-lingual target words, for which we explore with different sentence encoding techniques. To enhance the learning proc…
