Korpusna analiza sintaktičko-semantičkih struktura s pomoću grafova: semantičke domene pojma osjećaj

This research exemplifies the corpus-based graph approach to the syntactic-semantic analysis of a concept feeling using the Construction Grammar Conceptual network methodology. by constructing a lexical network from grammatically tagged collocations of the English and the Croatian web corpora, the structure of the semantic domains is revealed as a set of sub-graphs derived from the source lexeme’s friend-of-a-friend graph. the subgraph structures, calculated with the community detection algorithm, are interpreted as the semantic domains associated with the source lexeme’s conceptual matrix. lexical structures are analyzed using a centrality algorithm that determines the overall rank of the salience and semantic relatedness to the source concept feeling. this empirical approach can be used for developing NLP methods and tasks, such as computing semantic similarity, sense disambiguation, sense structuring, as well as for comparative corpus and cross-cultural studies. ConGraCnet has a web application on the page http://emocnet.uniri.hr/congracnet.Ova studija prikazuje metodu ConGraCnet na primjeru korpusne sintaktičko-semantičke analize s pomoću grafova pojma osjećaj/feeling. analizom mreža leksičkih kolokacija koordinirane konstrukcije iz korpusa enTenTen i hrWac struktura semantičkih domena ishodišnih pojmova razlučuje se algoritmom prepoznavanja graf-zajednica. leksičke se zajednice sagledavaju kao apstrakcija semantičkih domena povezanih s pojmovnom matricom ishodišnoga leksema. Korištenjem algoritmom centralnosti koji prepoznaje istaknuto umrežene lekseme određuje se stupanj povezanosti semantičke domene s izvornim pojmom. ovaj empirijski pristup može se upotrebljavati za razvijanje nlP metoda za prepoznavanje semantičke sličnosti, razlučivanja višeznačnosti, strukturiranje značenja te za komparativne korpusne i međukulturne studije. Metoda ConGraCnet objavljena je kao mrežna aplikacija na stranici http://emocnet.uniri.hr/congracnet

A Rewriting Framework for Activities Subject to Regulations

Activities such as clinical investigations or financial processes are subject to regulations to ensure quality of results and avoid negative consequences. Regulations may be imposed by multiple governmental agencies as well as by institutional policies and protocols. Due to the complexity of both regulations and activities there is great potential for violation due to human error, misunderstanding, or even intent. Executable formal models of regulations, protocols, and activities can form the foundation for automated assistants to aid planning, monitoring, and compliance checking. We propose a model based on multiset rewriting where time is discrete and is specified by timestamps attached to facts. Actions, as well as initial, goal and critical states may be constrained by means of relative time constraints. Moreover, actions may have non-deterministic effects, that is, they may have different outcomes whenever applied. We demonstrate how specifications in our model can be straightforwardly mapped to the rewriting logic language Maude, and how one can use existing techniques to improve performance. Finally, we also determine the complexity of the plan compliance problem, that is, finding a plan that leads from an initial state to a desired goal state without reaching any undesired critical state. We consider all actions to be balanced, that is, their pre and post-conditions have the same number of facts. Under this assumption on actions, we show that the plan compliance problem is PSPACE-complete when all actions have only deterministic effects and is EXPTIME-complete when actions may have non-deterministic effects

Semi-Local Integration Measure of Node Importance

Numerous centrality measures have been introduced as tools to determine the import ance of nodes in complex networks, reflecting various network properties, including connectivity, survivability, and robustness. In this paper, we introduce Semi- Local Integration (SLI), a node centrality measure for undirected and weighted graphs that takes into account the coherence of the locally connected subnetwork and evaluates the integration of nodes within their neighbourhood. We illustrate SLI node importance differentiation among nodes in lexical networks and demonstrate its potential in natural language processing (NLP). In the NLP task of sense identification and sense structure analysis, the SLI centrality measure evaluates node integration and provides the necessary local resolution by differentiating the importance of nodes to a greater extent than standard centrality measures. This provides the relevant topological information about different subnetworks based on relatively local information, revealing the more complex sense structure. In addition, we show how the SLI measure can improve the results of sentiment analysis. The SLI measure has the potential to be used in various types of complex networks in different research areas

Graph-Based Taxonomic Semantic Class Labeling

We present a graph-based method for the lexical task of labeling senses of polysemous lexemes. The labeling task aims at generalizing sense features of a lexical item in a corpus using more abstract concepts. In this method, a coordination dependency-based lexical graph is first constructed with clusters of conceptually associated lexemes representing related senses and conceptual domains of a source lexeme. The label abstraction is based on the syntactic patterns of the x is_a y dependency relation. For each sense cluster, an additional lexical graph is constructed by extracting label candidates from a corpus and selecting the most prominent is_a collocates in the constructed label graph. The obtained label lexemes represent the sense abstraction of the cluster of conceptually associated lexemes. In a similar graph-based procedure, the semantic class representation is validated by constructing a WordNet hypernym relation graph. These additional labels indicate the most appropriate hypernym category of a lexical sense community. The proposed labeling method extracts hierarchically abstract conceptual content and the sense semantic features of the polysemous source lexeme, which can facilitate lexical understanding and build corpus-based taxonomies

Lexical Sense Labeling and Sentiment Potential Analysis Using Corpus-Based Dependency Graph

This paper describes a graph method for labeling word senses and identifying lexical sentiment potential by integrating the corpus-based syntactic-semantic dependency graph layer, lexical semantic and sentiment dictionaries. The method, implemented as ConGraCNet application on different languages and corpora, projects a semantic function onto a particular syntactical dependency layer and constructs a seed lexeme graph with collocates of high conceptual similarity. The seed lexeme graph is clustered into subgraphs that reveal the polysemous semantic nature of a lexeme in a corpus. The construction of the WordNet hypernym graph provides a set of synset labels that generalize the senses for each lexical cluster. By integrating sentiment dictionaries, we introduce graph propagation methods for sentiment analysis. Original dictionary sentiment values are integrated into ConGraCNet lexical graph to compute sentiment values of node lexemes and lexical clusters, and identify the sentiment potential of lexemes with respect to a corpus. The method can be used to resolve sparseness of sentiment dictionaries and enrich the sentiment evaluation of lexical structures in sentiment dictionaries by revealing the relative sentiment potential of polysemous lexemes with respect to a specific corpus. The proposed approach has the potential to be used as a complementary method to other NLP resources and tasks, including word disambiguation, domain relatedness, sense structure, metaphoricity, as well as a cross- and intra-cultural discourse variations of prototypical conceptualization patterns and knowledge representations

Lexical Sense Labeling and Sentiment Potential Analysis using Corpus-Based Dependency Graph

This article describes a graph method for labeling word senses and identifying sentiment potential of lexemes by integrating the corpus- based syntactic-semantic dependency graph layer, lexical semantic resources and sentiment dictionaries. The method, implemented as ConGraCNet application, projects a semantic function to a particular syntactical dependency corpus layer and constructs a seed lexeme graph with high conceptual similarity collocates. Seed lexeme graph is clustered into subgraphs that reveal polysemous semantic nature of a lexeme in a corpus. Using a WordNet, a hypernym graph is created to assert a set of synset labels for a lexical cluster of the seed lexeme, thus providing a generalization of community features. In addition, by integrating sentiment dictionaries, we describe graph propagation methods for sentiment analysis. Original dictionary sentiment values are integrated into ConGraCNet lexical graph in order to calculate sentiment values of node lexemes and lexical clusters, and to ultimately identify sentiment potential of a seed lexeme with respect to a specific corpus. The method can be used for resolving sparsity of sentiment dictionaries and enriching the sentiment evaluation of lexical structures in sentiment dictionaries, revealing relative sentiment potential of polysemous lexemes with respect to a specific corpus. We exemplify the application of the methodology on several lexemes in different languages and corpora and present the evaluation results of two surveys. The proposed approach has the potential to be used as a complementary method to other NLP contemporary resources for the enrichment of various semantic tasks including word disambiguation, domain relatedness, sense structure, synonymy, antonymy and metaphoricity, as well as establish a cross- and intra-cultural discourse variations of prototypical conceptualization patterns and knowledge representations

Graph-Based Taxonomic Semantic Class Labeling

We present a graph-based method for the lexical task of labeling senses of polysemous lexemes. The labeling task aims at generalizing sense features of a lexical item in a corpus using more abstract concepts. In this method, a coordination dependency-based lexical graph is first constructed with clusters of conceptually associated lexemes representing related senses and conceptual domains of a source lexeme. The label abstraction is based on the syntactic patterns of the x is_a y dependency relation. For each sense cluster, an additional lexical graph is constructed by extracting label candidates from a corpus and selecting the most prominent is_a collocates in the constructed label graph. The obtained label lexemes represent the sense abstraction of the cluster of conceptually associated lexemes. In a similar graph-based procedure, the semantic class representation is validated by constructing a WordNet hypernym relation graph. These additional labels indicate the most appropriate hypernym category of a lexical sense community. The proposed labeling method extracts hierarchically abstract conceptual content and the sense semantic features of the polysemous source lexeme, which can facilitate lexical understanding and build corpus-based taxonomies