Identification Of Semantic Relations Research Articles

Multi task learning with general vector space for cross-lingual semantic relation detection

Semantic relation detection has an important role in natural language processing. In a supervised approach, the training process requires a sufficient amount of labeled data. However, in low-resource languages, labeled data are limited, whereas in rich-resource languages, labeled data are available in large quantities. In addition, various studies tend to model the single-task problem without considering the generalization with other tasks. Hence, a strategy that can utilize the availability of labeled data in rich-resource languages and generalize models to improve the identification of relations in a cross-lingual manner is needed. In this paper, we propose a framework to identify cross-lingual semantic relation using multi-task learning with a general vector space. The proposed method was designed to construct a general vector space and semantic relation identification. The experiments were conducted over three datasets: Indonesian–Arabic, English–Arabic, and English–Indonesia. The results show that the use of multi-task learning with a general vector space can overcome the problem of cross-lingual semantic relation identification. This is shown by the accuracy of the synonym and hypernym tasks that reached 84.9% and 84.8%, respectively.

The impact of semantic relations on grammatical alternation: an experimental study of proper name modifiers and determiner genitives

In this article we investigate the role of semantic relations in grammatical alternations. The specific alternation we look at is that between the proper name modifier construction, e.g.the Obama government, and the determiner genitive, e.g.Obama's government. Through the use of an experimental study in which participants were asked to rate the naturalness of the two constructions in 20 attested natural language contexts and provide paraphrases of the semantic relations in question, we tested when the two constructions alternate and whether either construction expresses semantic relations that block alternation. Our initial finding is that none of the relations we studied is categorically associated with only one of the constructions, but that certain relations – notably possession and name – are far more preferentially associated with determiner genitives and proper name modifiers respectively. Despite these ‘default’ associations, participants nevertheless identified a range of possible interpretations for many of the examples, meaning that our study simultaneously supports the opposing theoretical views of default relations and semantic underspecification. Further, our study validates the inclusion of semantic relations in genitive alternation studies as a major factor despite the notorious difficulties in their operationalisation. Animacy distinctions, although more straightforward to codify, appear to be of lesser importance. Methodologically, our study shows the value of an experimental approach as a corrective to researcher intuitions about the identification of semantic relations in context.

Problem-Oriented Automatic Summarization Method with Semantic Coherence Definition and Usage of Inclusion Measures for the Search of Answers to Questions in the Internet

The article deals with the relevant problem of automatic arbitrary text summary obtaining. Several works in this field are analyzed using comparison and classification methods. The problem of obtaining a text summary by means of an answer to a random question is emphasized. The identification of semantic relations between sentences using a set of rules based on syntax and semantics of a language is described. These rules are represented in the form of regular expressions – patterns that consist of characters and metacharacters and set search rules. Taking into account semantic coherence features, an improved method of sentences similarity calculation to identify the measure of inclusion of one sentence into another one is developed. This method helps to define more precisely logical stress on the words within automatic summarization and detect contradictions. A modified automatic summarization method, oriented at a specific problem is suggested. It is concluded that the proposed method is quiet effective in the process of automatic search for answers to questions in the Internet.

Semantic Relata for the Evaluation of Distributional Models in Mandarin Chinese

Distributional Semantic Models (DSMs) established themselves as a standard for the representation of word and sentence meaning. However, DSMs provide quantitative measurement of how strongly two linguistic expressions are related, without being able to automatically classify different semantic relations. Hence the notion of semantic similarity is underspecified in DSMs. We introduce Evalution-MAN in this paper as an effort to address this underspecification problem. Following the EVALution 1.0 dataset for English, we present a dataset for evaluating DSMs on the task of the identification of semantic relations in Mandarin Chinese. Moreover, we test different types of word vectors on the automatic learning of these semantic relations, and we evaluate them both in a unsupervised and in a supervised setting, finding that distributional models tend, in general, to assign higher similarity scores to synonyms and that deep learning classifiers are the best performing ones in the identification of semantic relations.

Left inferior parietal and posterior temporal cortices mediate the effect of action observation on semantic processing of objects: evidence from rTMS.

Previous studies showed that motor information related to tool use (i.e., functional actions) could affect processing of objects semantic properties, whereas motor information related to grasping or moving tool (i.e., structural actions) cannot. However, little is known about the neural correlates mediating such interaction between motor and semantic information. Here, healthy participants performed a semantic judgment task requiring identification of semantic relations among objects, after observing a functional, a structural or a pointing action prime. In a within-subject design, during prime presentation the participants underwent repetitive transcranial magnetic stimulation (rTMS) over the left supramarginal gyrus (SMG), the left posterior middle temporal gyrus (pMTG) or received sham stimulation. Results showed that in the sham condition observing functional actions (vs. structural and pointing actions) favoured processing of semantic relations based on function similarity (i.e., taxonomic relations), but not of relations based on co-occurrence within an event schema (i.e., thematic relations). Moreover, stimulation of both left SMG and pMTG abolished the effect of functional action primes worsening subsequent judgment about taxonomic relations, and this effect was greater after pMTG stimulation. rTMS did not affect processing of thematic semantic relations. We suggest that action observation triggers activation of functional motor information within left inferior parietal cortex, and that integration between functional motor and conceptual information in left temporal cortex could impact high-level semantic processing of tools.

Systematization and Identification of Semantic Relations in Ontologies for Scientific and Technical Subject Areas

This article presents a relations taxonomy constructed on the basis of a conceptual framework, as well as methods of identifying semantic relations that are used for automatically constructing ontologies of subject areas. A predicate lexicon that corresponds to relation types is established through automatically processing a collection of scientific and technical document texts. Patterns of linguistic constructions of semantic relations are constructed in a way that considers morphological features of words and morphemes. Statistics of types of relations and the use of patterns depending on the document type are studied.

Semantic relation identification for consecutive predicative constituents in Chinese

In this paper, we propose a general methodology for designing semantic role/relation system. Based on this methodology, we establish a succinct semantic relation system for consecutive predicative constituents for Chinese, which includes serial verb construction, discourse construction, and other constructions describing serial events. This semantic relation system has 13 middle-level classes and 24 fine-grained sub-classes in contrast to conventional complex classification schemes and meets the uniqueness and completeness criteria of semantic relation identification. We conduct experiments on our system by training four annotators in 1 h to label 200 sentences extracted from Sinica Treebank and HIT-CDTB. With the help of our predesigned feature-based decision tree and a connective markers checklist, the annotators attain a 73% consistency with the reference standard annotation and substantial agreement by Cohen’s kappa coefficient for middle-level labeling. By analyzing the labeling error types, we slightly revise our classification scheme and propose six methods to improve the classification and labeling system, hoping to achieve even better agreement in the future.

A Method for Automatic Construction of Ontological Knowledge Bases. II. Automatic Identification of Semantic Relations in Ontological Networks*

A semantic-syntactic model of natural language is presented. After the factorization of constructed tensors of the model, vectors representing the semantic-syntactic valence of words and describing the commutative behavior of words in a sentence are generated. A method is developed for computing vectors of the semantic-syntactic valence of concepts in an ontology that form an implicit description of their semantic relations. An algorithm is proposed for extracting explicit semantic relations between ontological concepts from vectors of their semantic-syntactic valence.

Acquisition of Turkish meronym based on classification of patterns

The identification of semantic relations from a raw text is an important problem in Natural Language Processing. This paper provides semi-automatic pattern-based extraction of part---whole relations. We utilized and adopted some lexico-syntactic patterns to disclose meronymy relation from a Turkish corpus. We applied two different approaches to prepare patterns; one is based on pre-defined patterns that are taken from the literature, second automatically produces patterns by means of bootstrapping method. While pre-defined patterns are directly applied to corpus, other patterns need to be discovered first by taking manually prepared unambiguous seeds. Then, word pairs are extracted by their occurrence in those patterns. In addition, we used statistical selection on global data that is obtaining from all results of entire patterns. It is a whole-by-part matrix on which several association metrics such as information gain, T-score, etc., are applied. We examined how all these approaches improve the system accuracy especially within corpus-based approach and distributional feature of words. Finally, we conducted a variety of experiments with a comparison analysis and showed advantage and disadvantage of the approaches with promising results.

A knowledge-rich approach to identifying semantic relations between nominals

This paper describes a state-of-the-art supervised, knowledge-intensive approach to the automatic identification of semantic relations between nominals in English sentences. The system employs a combination of rich and varied sets of new and previously used lexical, syntactic, and semantic features extracted from various knowledge sources such as WordNet and additional annotated corpora. The system ranked first at the third most popular SemEval 2007 Task – Classification of Semantic Relations between Nominals and achieved an F-measure of 72.4% and an accuracy of 76.3%. We also show that some semantic relations are better suited for WordNet-based models than other relations. Additionally, we make a distinction between out-of-context (regular) examples and those that require sentence context for relation identification and show that contextual data are important for the performance of a noun–noun semantic parser. Finally, learning curves show that the task difficulty varies across relations and that our learned WordNet-based representation is highly accurate so the performance results suggest the upper bound on what this representation can do.

Extraction of semantic biomedical relations from text using conditional random fields.

BackgroundThe increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.ResultsWe benchmark our approach on two different tasks. The first task is the identification of semantic relations between diseases and treatments. The available data set consists of manually annotated PubMed abstracts. The second task is the identification of relations between genes and diseases from a set of concise phrases, so-called GeneRIF (Gene Reference Into Function) phrases. In our experimental setting, we do not assume that the entities are given, as is often the case in previous relation extraction work. Rather the extraction of the entities is solved as a subproblem. Compared with other state-of-the-art approaches, we achieve very competitive results on both data sets. To demonstrate the scalability of our solution, we apply our approach to the complete human GeneRIF database. The resulting gene-disease network contains 34758 semantic associations between 4939 genes and 1745 diseases. The gene-disease network is publicly available as a machine-readable RDF graph.ConclusionWe extend the framework of Conditional Random Fields towards the annotation of semantic relations from text and apply it to the biomedical domain. Our approach is based on a rich set of textual features and achieves a performance that is competitive to leading approaches. The model is quite general and can be extended to handle arbitrary biological entities and relation types. The resulting gene-disease network shows that the GeneRIF database provides a rich knowledge source for text mining. Current work is focused on improving the accuracy of detection of entities as well as entity boundaries, which will also greatly improve the relation extraction performance.

Mining defining contexts to help structuring differential ontologies

In this paper, we present an experiment dealing with corpus-based construction of “differential ontologies”, which are organised according to semantic similarity and differential features. We argue that knowledge-rich defining contexts can be useful to help an ontology modeller in his task. We present a method, based on lexico-syntactic patterns, to spot such contexts in a corpus, then identify the terms they relate (definiendum and genus or “characteristics”) and the semantic relation that links them. We also show how potential co-hyponyms can be detected on the basis of shared words in their definiens. We evaluate the extracted defining sentences, semantic relations and co-hyponyms on a test corpus focusing on childhood and on an evaluation corpus about dietetics (both corpora are French). Definition extraction obtains 50% precision and recall of approximately 40%. Semantic relation identification reaches an average of 48% precision, and co-hyponyms 23.5%. We discuss the results of these experiments and conclude on perspectives for future work.

Automatic Extraction of Ontology Relations From Medical Abstracts

Ontologies play an important role in the Semantic Web as well as in knowledge management. This project seeks to develop an automatic method to build ontologies, especially in a medical domain. The initial study investigates an approach of identifying pairs of related concepts using association rule induction and identifying semantic relations between concepts using an existing medical knowledge base, the UMLS (Unified Medical Language System) semantic net. This is evaluated by comparing the result with manually assigned semantic relations based on an analysis of medical abstracts containing each pair of concepts. Our initial finding shows that the automatic process is promising, achieving a 68% coverage compared to manual tagging. We also discuss about probable approaches for the improvement of the identification of semantic relations by employing natural language processing techniques.

Effects of computer programming experience on network representations of abstract programming concepts

The cognitive organization of a set of abstract programming concepts was investigated in subjects who varied in degree of computer programming experience. Relatedness ratings on pairs of the concepts were collected from naive, novice, intermediate, and advanced programmers. Both individual and group network representations of memory structure were derived using the Pathfinder network scaling algorithm. Not only did the four group networks differ, but they varied systematically with experience, providing support for the psychological meaningful-ness of the structures. Additionally, an analysis at the conceptual level revealed that the four groups differed in the way concepts were represented. Furthermore, this analysis was used to classify concepts in the naive, novice, and intermediate networks as well-defined or misdefined. The identification of semantic relations corresponding to some of the links in the networks provided further information concerning differences in programmer knowledge at different levels of experience. Applications of this work to programmer education and knowledge engineering are discussed.