Semantic Relations

To achieve our goal of building a compre- hensive lexical database out of various on-line resources, it is necessary to interpret and disambiguate the information found in these resources. In …...

We. present a study of the mappings from semantic content to syntactic ex- pression with the aim of isolating the precise locus and role of pragmatic infor- mation in the …...

We are developing corpus-based techniques for iden- tifying semantic relations at an intermediate level of description (more specific than those used in case frames, but more general than those used …...

The discovery of semantic relations from text becomes increasingly important for applica- tions such as Question Answering, Informa- tion Extraction, Text Summarization, Text Un- derstanding, and others. The semantic rela- …...

A crucial step toward the goal of au- tomatic extraction of propositional in- formation from natural language text is the identification of semantic relations between constituents in sentences. We examine …...

Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, …...

We present an unsupervised learning al- gorithm that mines large text corpora for patterns that express implicit semantic re- lations. For a given input word pair X :Y with some …...

Psychiatric document retrieval attempts to help people to efficiently and effectively locate the consultation documents relevant to their depressive problems. Individuals can understand how to alleviate their symp- toms according …...

Obtaining labeled data is a significant obstacle for many NLP tasks. Recently, online games have been proposed as a new way of obtain- ing labeled data; games attract users by …...

Addressing the task of acquiring semantic relations between events from a large cor- pus, we first argue the complementarity be- tween the pattern-based relation-oriented approach and the anchor-based argument- oriented …...

AbstractWe introduce a method for automatically labelling edges of word co-occurrence graphs with semantic relations. Therefore we only make use of training data already contained within the graph. Starting point …...

AbstractInformation extraction deals with extracting entities (such as people, organizations or locations) and named relations between entities (such as ""People born-in Country"") from text documents. An important challenge in information …...

This paper describes our approach to the automatic identification of semantic rela- tions between nominals in English sen- tences. The basic idea of our strategy is to develop machine-learning classifiers …...

AbstractIn this study, we extracted brain activities related to semantic relations and distances to improve the precision of distance calculation among concepts in the Associated Concept Dictionary (ACD). For the …...

The automatic interpretation of semantic relations between nominals is an impor- tant subproblem within natural language understanding applications and is an area of increasing interest. In this paper, we present …...

One of the main obstacles to high- performance Word Sense Disambigua- tion (WSD) is the knowledge acquisi- tion bottleneck. In this paper, we present a methodology to automatically extend WordNet …...

A challenging problem in open informa- tion extraction and text mining is the learn- ing of the selectional restrictions of se- mantic relations. We propose a mini- mally supervised bootstrapping …...

AbstractIn this paper we discuss noun compounding, a highly generative, productive process, in three distinct languages: Czech, English and Zulu. Derivational morphology presents a large grey area between regular, compositional …...

AbstractThis paper presents the PolNet-Polish WordNet project which aims at building a linguistically oriented ontology for Polish compatible with other WordNet projects such as Princeton WordNet, EuroWordNet and other similarly …...

We combine relational and attributional similarity for the task of identifying instances of semantic relations, such as PRODUCT-PRODUCER and ORIGIN- ENTITY, between nominals in text. We use no pre-existing lexical …...

This paper presents an unsupervised method for deriving inference axioms by composing semantic relations. The method is indepen- dent of any particular relation inventory. It relies on describing semantic relations …...

AbstractThis paper presents a model to enrich an ontology with a thesaurus based on a domain corpus and WordNet. The model is applied to the data privacy domain and the …...

Each expanding and developing system requires some feedback to evaluate the normal trends of the system and also the unsystematic steps. In this paper two lexical- semantic databases – Princeton …...

AbstractThis paper presents our efforts aimed at collecting and annotating a free Polish corpus. The corpus will serve for us as training and testing material for experiments with Machine Learning …...

Grouping of terms based on linguistic and semantic regularities in a cross-lingual context We propose to exploit the Natural Language Processing methods dedicated to terminology structuring independently in two languages …...

AbstractThis paper introduces the problem of predicting semantic relations expressed by prepositions and develops statistical learning models for predicting the relations, their arguments and the semantic types of the arguments. …...

AbstractNowadays we are facing a growing demand for semantic knowledge in computational applications, particularly in Natural Language Processing (NLP). However, there aren’t sufficient human resources to produce that knowledge at …...

We describe a method of encoding cooc- currence information in a three-way tensor from which HAL-style word space mod- els can be derived. We use these models to identify semantic …...

This work is concerned with distinguishing different semantic relations which exist between distributionally similar words. We compare a novel approach based on training a linear Support Vector Machine on pairs …...

. This paper presents a knowledge base containing triples involving pairs of verbs associated with seman- tic or discourse relations. The relations in these triples are marked by discourse connectors …...

Learning semantic representations and tree structures of bilingual phrases is ben- eficial for statistical machine translation. In this paper, we propose a new neu- ral network model called Bilingual Corre- …...

AbstractAspect-level analysis of sentiments contained in a review text is important to reveal a detailed picture of consumer opinions. While a plethora of methods have been traditionally employed for this …...

AbstractThis paper presents a novel gold standard of German noun-noun compounds (Ghost-NN) including 868 compounds annotated with corpus frequencies of the compounds and their constituents, productivity and ambiguity of the …...

AbstractFor the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributed representations, which could encode …...

AbstractThis paper describes the process of creating a corpus annotated for concepts and semantic relations in the scientific domain. A part of the ACL Anthology Corpus was selected for annotation, …...

AbstractSemantic relations play an important role in linguistic knowledge representation. Although their role is relevant in the context of written text, there is no approach or dataset that makes use …...

In this paper, we explore the role of con- stituent properties in English and Ger- man noun-noun compounds (corpus fre- quencies of the compounds and their con- stituents; productivity and …...

AbstractGLAWI is a free, large-scale and versatile Machine-Readable Dictionary (MRD) that has been extracted from the French language edition of Wiktionary, called Wiktionnaire. In (Sajous and Hathout, 2015), we introduced …...

AbstractExtracting semantic relations from texts is a good way to build and supply a knowledge base, an indispensable resource for text analysis. We propose and evaluate the combination of three …...

AbstractWith the aid of recently proposed word embedding algorithms, the study of semantic similarity has progressed and advanced rapidly. However, many natural language processing tasks need sense level representation. To …...

AbstractCapturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of possible worlds (interpretations, or situations) where a premise …...

AbstractRepresenting the semantic relations that exist between two given words (or entities) is an important first step in a wide-range of NLP applications such as analogical reasoning, knowledge base completion …...

AbstractWords in different languages rarely cover the exact same semantic space. This work characterizes differences in meaning between words across languages using semantic relations that have been used to relate …...

AbstractWe present a comparison between deep learning and traditional machine learning methods for various NLP tasks in Italian. We carried on experiments using available datasets (e.g., from the Evalita shared …...

AbstractSemantic relations are core to how humans understand and express concepts in the real world using language. Recently, there has been a thread of research aimed at modeling these relations …...

AbstractProcedural text understanding aims at tracking the states (e.g., create, move, destroy) and locations of the entities mentioned in a given paragraph. To effectively track the states and locations, it …...

AbstractToday, natural language processing heavily relies on pre-trained large language models. Even though such models are criticized for the poor interpretability, they still yield state-of-the-art solutions for a wide set …...

AbstractEvent detection, which aims to identify instances of specific event types from pieces of text, is a fundamental task in information extraction. Most existing approaches leverage syntactic knowledge with a …...

AbstractSemantic Storytelling describes the goal to automatically and semi-automatically generate stories based on extracted, processed, classified and annotated information from large content resources. Essential is the automated processing of text …...

AbstractPre-trained language models have made great progress on dialogue tasks. However, these models are typically trained on surface dialogue text, thus are proven to be weak in understanding the main …...