For NLP applications that require some sort of semantic interpretation it would be helpful to know what expressions exhibit an idiomatic meaning and what expressions exhibit a literal meaning. We investigate whether automatic word-alignment in existing parallel corpora facilitates the classification of candidate expressions along a continuum ranging from literal and transparent expressions to i...

We propose a method for semantic structure analysis of noun phrases using Abstract Meaning Representation (AMR). AMR is a graph representation for the meaning of a sentence, in which noun phrases (NPs) are manually annotated with internal structure and semantic relations. We extract NPs from the AMR corpus and construct a data set of NP semantic structures. We also propose a transition-based al...

Semantic parsers map natural language sentences to formal representations of their underlying meaning. Building accurate semantic parsers without prohibitive engineering costs is a longstanding, open research problem. The tutorial will describe general principles for building semantic parsers. The presentation will be divided into two main parts: learning and modeling. In the learning part, we ...

Semantic parsing offers many opportunities to improve natural language understanding. We present a semantically annotated parallel corpus for English, German, Italian, and Dutch where sentences are aligned with scoped meaning representations in order to capture the semantics of negation, modals, quantification, and presupposition triggers. The semantic formalism is based on Discourse Representa...

The paper addresses with the development of an environmental thesaurus in order to obtain an advanced tool for semantic control and knowledge organization to be applied to environmental information management. EARTh (Environmental Application Reference Thesaurus) was designed according to an approach to meaning representation that regards lexical meaning as a unitas multiplex, a unity that aggr...

Business rules are generally expressed in natural language, although some rules are at times illustrated graphically. SBVR is not a logic language for restating business rules in some other language that business people don’t use. Rather, SBVR provides a means for describing the structure of the meaning of rules expressed in the natural language that business people use. SBVR calls this “semant...

This paper presents a new statistical method for detecting and tracking changes in word meaning, based on Latent Semantic Analysis. By comparing the density of semantic vector clusters this method allows researchers to make statistical inferences on questions such as whether the meaning of a word changed across time or if a phonetic cluster is associated with a specific meaning. Possible applic...

A three layer back propagation neural net is set up to study the functional dependency between the semantic class of a bisyllabic Chinese word and that of its two constituent Chinese characters. Simulations were performed using a three-layer back-propagation neural net with various combination of inputs. The inputs are (1) semantic classes of the constituent characters, (2) Entropy of the chara...

We propose in this paper a greedy method to the problem of measuring semantic similarity between short texts. Our method is based on the principle of compositionality which states that the overall meaning of a sentence can be captured by summing up the meaning of its parts, i.e. the meanings of words in our case. Based on this principle, we extend wordto-word semantic similarity metrics to quan...

The development of larger scale natural language systems has been hampered by the need to manually create mappings from syntactic structures into meaning representations. A new approach to semantic interpretation is proposed, which uses partial syntactic structures as the main unit of abstraction for interpretation rules. This approach can work for a variety of syntactic representations corresp...