Multiword expression

Abstract: Multiword expressions MWEs are a class of linguistic forms spanning conventional word boundaries that are both idiosyncratic and pervasive across different languages. The structure of linguistic processing that depends on the clear distinction between words and phrases has to be re-thought to accommodate MWEs. The issue of MWE handling is crucial for NLP applications, where it raises a number of challenges. The emergence of solutions in the absence of guiding principles motivates this survey, whose aim is not only to provide a focused review of MWE processing, but also to clarify the nature of interactions between MWE processing and downstream applications. We propose a conceptual framework within which challenges and research contributions can be positioned. It offers a shared understanding of what is meant by "MWE processing," distinguishing the subtasks of MWE discovery and identification. It also elucidates the interactions between MWE processing and two use cases: Parsing and machine translation. Many of the approaches in the literature can be differentiated according to how MWE processing is timed with respect to underlying use cases. We discuss how such orchestration choices affect the scope of MWE-aware systems. For each of the two MWE processing subtasks and for each of the two use cases, we conclude on open issues and research perspectives.

Abstract: The invention relates to a method and a computer system for enhanced part-of-speech (POS-) tagging as well as grammatically disambiguating a phrase. A phrase is usually a short multiword expression that may be ambiguous. By introducing grammatical constraints the invention supports POS-tagging as well as grammatically disambiguating the phrase. According to an identifier for the phrase, the phrase is supplemented with artificial context information. The supplemented phrase is then POS-tagged or grammatically disambiguated. Important applications are POS-tagging, Automatic Term Encoding, Headword Detection and Information Retrieval.

Abstract: We present a novel representation, evaluation measure, and supervised models for the task of identifying the multiword expressions (MWEs) in a sentence, resulting in a lexical semantic segmentation . Our approach generalizes a standard chunking representation to encode a subset of projective MWEs containing gaps, thereby enabling efficient sequence tagging algorithms for feature-rich discriminative models. Experiments on a new dataset of English web text offer the first linguistically-driven evaluation of MWE identification with truly heterogeneous expression types. Our statistical sequence model greatly outperforms a lookup-based segmentation procedure, achieving 60% F 1 for MWE identification.

Abstract: We conduct a pilot study for task-oriented evaluation of Multiword Expression (MWE) in Statistical Machine Translation (SMT). We propose two different integration strategies for MWE in SMT, which take advantage of different degrees of MWE semantic compositionality and yield complementary improvements in SMT quality on a large-scale translation task.