Composing meaning

构成意义

• 批准号: 138879-2009
• 负责人: Bergler, Sabine
• 金额: $ 1.75万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=503389
• 关键词: Composing; meaning

The work proposed here for the next few years builds on all of my previous research in spirit, and in particular sentiment analysis, causality, hedging, and (dream) emotion analysis. The unifying feature of all these topics is that they are concerned with (a) differentiating factual information from embedding meta-information in different text genres and (b) decoding this meta-information and making it accessible for automatic text analysis as an equal component in text meaning. That the factual content of a text contributes only one part of its meaning has been largely ignored in Natural Language Processing (NLP) systems to date. But recently, the possibility of commercial exploitation of sentiment and opinion-laden texts from abundant on-line sources such as blogs for product improvement has shifted attention to consider sentiment and even emotional content using shallow means. Now, valence shifters (language constructs that can invert the meaning of a clause, such as negation) are considered more widely. But still, the predominant technique for, for instance, sentiment analysis, is to simply count the number of positive vs. negative sentiment words in a sentence with a winner take all overall sentiment score. The limitations of the simplistic approach are obvious: a long string of positive statements about acting and directing in a movie review can be undone with the simple concluding statement: "And yet the film bombs.". Stochastic techniques fall short, because (a) the linguistically relevant features have not been identified and thus there are no common annotation schemes nor training corpora yet (b) their compositional behavior is too complex for simple annotation schemes. With better understanding of the nature and behavior of all of the different meta-propositional devices will come better annotation schemas that will lead to more refined stochastic systems. In considering the linguistics of non-factual aspects of sentence meaning and how they contribute to the meaning of the text, I propose to develop an annotation scheme and a compositional semantics for texts that treats different meaning components coherently.

这里提出的未来几年的工作建立在我之前在精神方面的所有研究基础上，特别是情感分析、因果关系、对冲和(梦)情绪分析。所有这些话题的共同特点是，它们涉及(A)区分事实信息和嵌入不同文本体裁的元信息，以及(B)解码这些元信息，并使其作为文本意义上的平等组成部分可用于自动文本分析。在自然语言处理(NLP)系统中，文本的事实内容只贡献了其意义的一部分，这一点在很大程度上被忽视了。但最近，从大量在线来源(如用于产品改进的博客)中商业利用情感和观点文本的可能性，已将注意力转移到使用肤浅的手段来考虑情感甚至情感内容。现在，价移位(可以颠倒小句意思的语言结构，如否定)被更广泛地考虑。但是，例如，情绪分析的主要技术是简单地计算一句话中积极情绪和负面情绪的数量，胜利者获得所有情绪总分。这种简单化的方法的局限性是显而易见的：在电影评论中，一长串关于表演和导演的积极言论，可以用一句简单的结束语来推翻：“然而，电影是炸弹。”随机技术的不足之处在于：(A)语言上相关的特征尚未被识别，因此没有通用的标注方案或训练语料库；(B)它们的组成行为对于简单的标注方案来说过于复杂。随着对所有不同元命题手段的性质和行为的更好理解，将产生更好的注释模式，从而导致更精细的随机系统。在考虑句子意义的非事实方面的语言学及其对文本意义的贡献时，我建议为文本开发一种注释方案和组合语义学，以连贯地对待不同的意义成分。