Taxonomy Induction using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimumcost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms stateof- the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, no previous approaches have been empirically proven to manifest noise-robustness in the input vocabulary

Evaluation: Measurements of Differences between Semantic Spaces

The existing method to measure differences among semantic spaces is costly. The current study evaluates a low-cost method. Specifically, the current study uses three measurements of induced semantic structures (ISS) to measure the differences between vector-based semantic spaces. An ISS of a target word is that word\u27s ordered nearest neighbors. Our hypothesis, which was confirmed, is that the three measurements have the ability to measure the differences between spaces. In addition, the number of nearest neighbors used by measurements has an effect on the ability. Evaluation was conducted on five Touchstone Applied Science Associates (TASA) spaces. The measured differences between spaces were compared to the objective similar pattern of TASA spaces, which follow a well-defined hierarchy. The comparison indicateds that three measurements can capture the objective TASA pattern and that performance measures were better than a measurement which does not use ISS. It was concluded that the new method of measuring space differences in an apt complement to the existing method

Structured Learning for Taxonomy Induction with Belief Propagation

We present a structured learning approach to inducing hypernym taxonomies using a probabilistic graphical model formulation. Our model incorporates heterogeneous re-lational evidence about both hypernymy and siblinghood, captured by semantic features based on patterns and statistics from Web n-grams and Wikipedia ab-stracts. For efficient inference over tax-onomy structures, we use loopy belief propagation along with a directed span-ning tree algorithm for the core hyper-nymy factor. To train the system, we ex-tract sub-structures of WordNet and dis-criminatively learn to reproduce them, us-ing adaptive subgradient stochastic opti-mization. On the task of reproducing sub-hierarchies of WordNet, our approach achieves a 51 % error reduction over a chance baseline, including a 15 % error re-duction due to the non-hypernym-factored sibling features. On a comparison setup, we find up to 29 % relative error reduction over previous work on ancestor F1.

Hybrid fuzzy multi-objective particle swarm optimization for taxonomy extraction

Ontology learning refers to an automatic extraction of ontology to produce the ontology learning layer cake which consists of five kinds of output: terms, concepts, taxonomy relations, non-taxonomy relations and axioms. Term extraction is a prerequisite for all aspects of ontology learning. It is the automatic mining of complete terms from the input document. Another important part of ontology is taxonomy, or the hierarchy of concepts. It presents a tree view of the ontology and shows the inheritance between subconcepts and superconcepts. In this research, two methods were proposed for improving the performance of the extraction result. The first method uses particle swarm optimization in order to optimize the weights of features. The advantage of particle swarm optimization is that it can calculate and adjust the weight of each feature according to the appropriate value, and here it is used to improve the performance of term and taxonomy extraction. The second method uses a hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems that ensures that the membership functions and fuzzy system rule sets are optimized. The advantage of using a fuzzy system is that the imprecise and uncertain values of feature weights can be tolerated during the extraction process. This method is used to improve the performance of taxonomy extraction. In the term extraction experiment, five extracted features were used for each term from the document. These features were represented by feature vectors consisting of domain relevance, domain consensus, term cohesion, first occurrence and length of noun phrase. For taxonomy extraction, matching Hearst lexico-syntactic patterns in documents and the web, and hypernym information form WordNet were used as the features that represent each pair of terms from the texts. These two proposed methods are evaluated using a dataset that contains documents about tourism. For term extraction, the proposed method is compared with benchmark algorithms such as Term Frequency Inverse Document Frequency, Weirdness, Glossary Extraction and Term Extractor, using the precision performance evaluation measurement. For taxonomy extraction, the proposed methods are compared with benchmark methods of Feature-based and weighting by Support Vector Machine using the f-measure, precision and recall performance evaluation measurements. For the first method, the experiment results concluded that implementing particle swarm optimization in order to optimize the feature weights in terms and taxonomy extraction leads to improved accuracy of extraction result compared to the benchmark algorithms. For the second method, the results concluded that the hybrid technique that uses multi-objective particle swarm optimization and fuzzy systems leads to improved performance of taxonomy extraction results when compared to the benchmark methods, while adjusting the fuzzy membership function and keeping the number of fuzzy rules to a minimum number with a high degree of accuracy

Unsupervised Methods for Developing Taxonomies by Combining Syntactic and Statistical Information

This paper describes an unsupervised algorithm for placing unknown words into a taxonomy and evaluates its accuracy on a large and varied sample of words. The algorithm works by first using a large corpus to find semantic neighbors of the unknown word, which we accomplish by combining latent semantic analysis with part-of-speech information. We then place the unknown word in the part of the taxonomy where these neighbors are most concentrated, using a class-labelling algorithm developed especially for this task. This method is used to reconstruct parts of the existing WordNet database, obtaining results for common nouns, proper nouns and verbs. We evaluate the contribution made by part-of-speech tagging and show that automatic filtering using the class-labelling algorithm gives a fourfold improvement in accuracy

The Word-Space Model: using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces

The word-space model is a computational model of word meaning that utilizes the distributional patterns of words collected over large text data to represent semantic similarity between words in terms of spatial proximity. The model has been used for over a decade, and has demonstrated its mettle in numerous experiments and applications. It is now on the verge of moving from research environments to practical deployment in commercial systems. Although extensively used and intensively investigated, our theoretical understanding of the word-space model remains unclear. The question this dissertation attempts to answer is: what kind of semantic information does the word-space model acquire and represent? The answer is derived through an identification and discussion of the three main theoretical cornerstones of the word-space model: the geometric metaphor of meaning, the distributional methodology, and the structuralist meaning theory. It is argued that the word-space model acquires and represents two different types of relations between words – syntagmatic and paradigmatic relations – depending on how the distributional patterns of words are used to accumulate word spaces. The difference between syntagmatic and paradigmatic word spaces is empirically demonstrated in a number of experiments, including comparisons with thesaurus entries, association norms, a synonym test, a list of antonym pairs, and a record of part-of-speech assignments.För att köpa boken skicka en beställning till [email protected]/ To order the book send an e-mail to [email protected]