There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47 on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56 on the 374 analogy questions, statistically equivalent to the average human score of 57 . On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.
A semantic frame for an event such as judgement contains semantic roles such as judge , evaluee , and reason , whereas an event such as statement contains roles such as speaker , addressee , and message @cite_23 . The task of identifying semantic roles is to label the parts of a sentence according to their semantic roles. We believe that it may be helpful to view semantic frames and their semantic roles as sets of semantic relations; thus a measure of relational similarity should help us to identify semantic roles. moldovan04 argue that semantic roles are merely a special case of semantic relations (), since semantic roles always involve verbs or predicates, but semantic relations can involve words of any part of speech.
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We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Given an input sentence and a target word and frame, the system labels constituents with either abstract semantic roles, such as AGENT or PATIENT, or more domain-specific semantic roles, such as SPEAKER, MESSAGE, and TOPIC.The system is based on statistical classifiers trained on roughly 50,000 sentences that were hand-annotated with semantic roles by the FrameNet semantic labeling project. We then parsed each training sentence into a syntactic tree and extracted various lexical and syntactic features, including the phrase type of each constituent, its grammatical function, and its position in the sentence. These features were combined with knowledge of the predicate verb, noun, or adjective, as well as information such as the prior probabilities of various combinations of semantic roles. We used various lexical clustering algorithms to generalize across possible fillers of roles. Test sentences were parsed, were annotated with these features, and were then passed through the classifiers.Our system achieves 82 accuracy in identifying the semantic role of presegmented constituents. At the more difficult task of simultaneously segmenting constituents and identifying their semantic role, the system achieved 65 precision and 61 recall.Our study also allowed us to compare the usefulness of different features and feature combination methods in the semantic role labeling task. We also explore the integration of role labeling with statistical syntactic parsing and attempt to generalize to predicates unseen in the training data. We describe an unsupervised system for learning narrative schemas, coherent sequences or sets of events (arrested(POLICE, SUSPECT), convicted(JUDGE, SUSPECT)) whose arguments are filled with participant semantic roles defined over words (Judge = judge, jury, court , Police = police, agent, authorities ). Unlike most previous work in event structure or semantic role learning, our system does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles. Our unsupervised learning algorithm uses coreferring arguments in chains of verbs to learn both rich narrative event structure and argument roles. By jointly addressing both tasks, we improve on previous results in narrative frame learning and induce rich frame-specific semantic roles. Technologies for Industry-4.0 are evolving rapidly, and the term semantics is widely used. Various standardization groups claim that they provide mechanisms to express semantics or allow the integration of semantic information. The emerging data format AutomationML (as IEC 62714) proposes a role-based approach to encode semantics in engineering models and has already standardized fundamental engineering concepts as role classes. However, for concrete data processing tasks the currently standardized role classes are not sufficient to unambiguously express the meaning of various vendor or application specific concepts, while user-defined role classes rely on hand wired semantics encoded in dedicated software, e.g. importer exporters. Yet AutomationML system unit classes represent reusable engineering objects as relational models of AutomationML roles, attributes, interfaces, internal elements and links, which can be used to describe complex user-specific concepts. To enable an automatic machine interpretation of these unstandardized relational models, we transform AutomationML data to a formal and declarative semantic representation using the Web Ontology Language (OWL), and propose a rule mining approach to learn the intended meaning of user selected system unit classes, i.e. to identify the common relational structure shared by the selected engineering objects. This demonstration presents a high-performance syntactic and semantic dependency parser. The system consists of a pipeline of modules that carry out the to-kenization, lemmatization, part-of-speech tagging, dependency parsing, and semantic role labeling of a sentence. The system's two main components draw on improved versions of a state-of-the-art dependency parser (Bohnet, 2009) and semantic role labeler (, 2009) developed independently by the authors. The system takes a sentence as input and produces a syntactic and semantic annotation using the CoNLL 2009 format. The processing time needed for a sentence typically ranges from 10 to 1000 milliseconds. The predicate--argument structures in the final output are visualized in the form of segments, which are more intuitive for a user.