TAILIEUCHUNG - Báo cáo khoa học: "Detecting Compositionality in Multi-Word Expressions"

Identifying whether a multi-word expression (MWE) is compositional or not is important for numerous NLP applications. Sense induction can partition the context of MWEs into semantic uses and therefore aid in deciding compositionality. We propose an unsupervised system to explore this hypothesis on compound nominals, proper names and adjective-noun constructions, and evaluate the contribution of sense induction. The evaluation set is derived from WordNet in a semisupervised way. Graph connectivity measures are employed for unsupervised parameter tuning. . | Detecting Compositionality in Multi-Word Expressions loannis Korkontzelos Department of Computer Science The University of York Heslington York Yo10 5NG UK johnkork@ Suresh Manandhar Department of Computer Science The University of York Heslington York Yo10 5NG UK suresh@ Abstract Identifying whether a multi-word expression MWE is compositional or not is important for numerous NLP applications. Sense induction can partition the context of MWEs into semantic uses and therefore aid in deciding compositionality. We propose an unsupervised system to explore this hypothesis on compound nom-inals proper names and adjective-noun constructions and evaluate the contribution of sense induction. The evaluation set is derived from WordNet in a semisupervised way. Graph connectivity measures are employed for unsupervised parameter tuning. 1 Introduction and related work Multi-word expressions MWEs are sequences of words that tend to cooccur more frequently than chance and are either idiosyncratic or decomposable into multiple simple words Baldwin 2006 . Deciding idiomaticity of MWEs is highly important for machine translation information retrieval question answering lexical acquisition parsing and language generation. Compositionality refers to the degree to which the meaning of a MWE can be predicted by combining the meanings of its components. Unlike syntactic compositionality . by and large semantic compositionality is continuous Baldwin 2006 . In this paper we propose a novel unsupervised approach that compares the major senses of a MWE and its semantic head using distributional similarity measures to test the compositionality of the MWE. These senses are induced by a graph based sense induction system whose parameters are estimated in an unsupervised manner exploiting a number of graph connectivity measures Ko-rkontzelos et al. 2009 . Our method partitions the context space and only uses the major senses filtering out minor senses. In our .

• Báo cáo khoa học
• Detecting Compositionality
• Multi Word Expressions
• Ioannis Korkontzelos
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học