KurdNet: State-of-the-Art | 0 Expand: in this model, the synsets are built in correspondence with the WordNet synsets and the semantic relations are directly imported. |
KurdNet: State-of-the-Art | 0 Merge: in this model, the synsets and relations are first built independently and then they are aligned with WordNet’s. |
KurdNet: State-of-the-Art | synsets ) that play a major role in the wordnets. |
Evaluation 1: Agreement with Sentiment Lexicons | The construction of the connotation graph, denoted by GWORD+SENSE, which includes words and synsets , has been described in Section 2. |
Introduction | 1Hence a sense in WordNet is defined by synset (= synonym set), which is the set of words sharing the same sense. |
Network of Words and Senses | As shown in Figure 1, it contains two types of nodes; (i) lemmas (i.e., words, 115K) and (ii) synsets (63K), and four types of edges; (t1) predicate-argument (179K), (t2) argument-argument (144K), (t3) argument-synset (126K), and (t4) synset-synset (3.4K) edges. |
Network of Words and Senses | The argument-synset edges capture the synonymy between argument nodes through the corresponding synsets . |
Network of Words and Senses | Finally, the synset-synset edges depict the antonym relations between synset pairs. |
Pairwise Markov Random Fields and Loopy Belief Propagation | More formally, we denote the connotation graph GWORDJ'SEI‘ISE by G = (V, E), in which a total of n word and synset nodes V = {211, . |
Pairwise Markov Random Fields and Loopy Belief Propagation | and synsets connected with typed edges, - prior knowledge (i.e., probabilities) of (some or all) nodes belonging to each class, |
Abstract | As a first step to automatically construct full Wordnets, we propose approaches to generate Wordnet synsets for languages both resource-rich and resource-poor, using publicly available Wordnets, a machine translator and/or a single bilingual dictionary. |
Abstract | Our algorithms translate synsets of existing Wordnets to a target language T, then apply a ranking method on the translation candidates to find best translations in T. Our approaches are applicable to any language which has at least one existing bilingual dictionary translating from English to it. |
Introduction | One of our goals is to automatically generate high quality synsets , each of which is a set of cognitive synonyms, for Wordnets having the same structure as the PWN in several languages. |
Introduction | In particular, given public Wordnets aligned to the PWN ( such as the FinnWordNet (FWN) (Linden, 2010) and the J apaneseWordNet (J WN) (Isahara et al., 2008) ) and the Microsoft Translator, we build Wordnet synsets for arb, asm, dis, ajz and vie. |
Proposed approaches | In this section, we propose approaches to create Wordnet synsets for a target languages T using existing Wordnets and the MT and/or a single bilingual dictionary. |
Proposed approaches | We take advantage of the fact that every synset in PWN has a unique oflset-POS, referring to the offset for a synset with a particular part-of—speech (POS) from the beginning of its data file. |
Proposed approaches | Each synset may have one or more words, each of which may be in one or more synsets . |
Evaluation framework | The aligner constructs a WordNet dictionary for the purpose of synset alignment. |
Evaluation framework | The CW cluster is then aligned to WordNet synsets by comparing the clusters with WordNet graph and the synset with the maximum alignment score is returned as the output. |
Evaluation framework | In summary, the aligner tool takes as input the CW cluster and returns a WordNet synset id that corresponds to the cluster words. |
Related work | A few approaches suggested by (Bond et al., 2009; Paakko' and Linden, 2012) attempt to augment WordNet synsets primarily using methods of annotation. |
Experiments | To enable a comparison with the state of the art, we followed Matuschek and Gurevych (2013) and performed an alignment of WordNet synsets (WN) to three different collaboratively-constructed resources: Wikipedia |
Experiments | As mentioned in Section 2.1.1, we build the WN graph by including all the synsets and semantic relations defined in WordNet (e.g., hypernymy and meronymy) and further populate the relation set by connecting a synset to all the other synsets that appear in its disambiguated gloss. |
Resource Alignment | For instance, WordNet can be readily represented as an undirected graph G whose nodes are synsets and edges are modeled after the relations between synsets defined in WordNet (e. g., hypernymy, meronymy, etc. |
Resource Alignment | ), and LG is the mapping between each synset node and the set of synonyms which express the concept. |
Resource Alignment | 3'For instance, we calculated that more than 80% of the words in WordNet are monosemous, with over 60% of all the synsets containing at least one of them. |
Model and Feature Extraction | A lexical item can belong to several synsets , which are associated with different supersenses. |
Model and Feature Extraction | For example, the word head (when used as a noun) participates in 33 synsets , three of which are related to the supersense noan.b0dy. |
Model and Feature Extraction | Hence, we select all the synsets of the nouns head and brain. |
Experiments | A task begins with a description of a target synset and its textual definition; following, ten annotation questions are shown. |
Video Game with a Purpose Design | First, by connecting WordNet synsets to Wikipedia pages, most synsets are associated with a set of pictures; while often noisy, these pictures sometimes illustrate the target concept and are an ideal case for validation. |
Video Game with a Purpose Design | Data We created a common set of concepts, 0, used in both games, containing sixty synsets selected from all BabelNet synsets with at least fifty associated images. |
Video Game with a Purpose Design | Using the same set of synsets , separate datasets were created for the two validation tasks. |
Experiments | To project WordNet synsets to terms, we used the first (most frequent) term in each synset . |
Experiments | A few WordNet synsets have multiple parents so we only keep the first of each such pair of overlapping trees. |
Experiments | We also discard a few trees with duplicate terms because this is mostly due to the projection of different synsets to the same term, and theoretically makes the tree a graph. |
Experimental Framework | WordNet is organized into sets of synonyms, called synsets (SS). |
Experimental Framework | Each synset in turn belongs to a unique semantic file (SF). |
Experimental Framework | As an example, knife in its tool sense is in the EDGE TOOL USED AS A CUTTING INSTRUMENT singleton synset , and also in the ARTIFACT SF along with thousands of words including cutter. |
Comparative Evaluation | As regards recall, we note that in two cases (i.e., DBpedia returning page super-types from its upper taxonomy, YAGO linking categories to WordNet synsets) the generalizations are neither pages nor categories and that MENTA returns heterogeneous hypernyms as mixed sets of WordNet synsets , Wikipedia pages and categories. |
Comparative Evaluation | MENTA seems to be the closest resource to ours, however, we remark that the hypernyms output by MENTA are very heterogeneous: 48% of answers are represented by a WordNet synset , 37% by Wikipedia categories and 15% are Wikipedia pages. |
Introduction | However, unlike the case with smaller manually-curated resources such as WordNet (Fellbaum, 1998), in many large automatically-created resources the taxonomical information is either missing, mixed across resources, e.g., linking Wikipedia categories to WordNet synsets as in YAGO, or coarse-grained, as in DBpedia whose hypernyms link to a small upper taxonomy. |
Assessment of Lexical Resources | This includes the WordNet lexicographer’s file name (e.g., noun.time), synsets , and hypernyms. |
Assessment of Lexical Resources | We make extensive use of the file name, but less so from the synsets and hypernyms. |
Assessment of Lexical Resources | However, in general, we find that the file names are too coarse-grained and the synsets and hypernyms too fine-grained for generalizations on the selectors for the complements and the governors. |