Composing meaning

接下来几年的工作将建立在我之前对精神的所有研究之上，特别是情感分析、因果关系、对冲和（梦）情绪分析。所有这些主题的统一特征是，它们关注（a）区分事实信息与嵌入不同文本类型的元信息，以及（B）解码这些元信息，并使其作为文本意义的平等组成部分可用于自动文本分析。文本的事实内容只贡献其意义的一部分，这在自然语言处理（NLP）系统中一直被忽视。但最近，商业利用来自丰富的在线资源（如博客）的情感和充满观点的文本进行产品改进的可能性已经将注意力转移到使用浅层手段来考虑情感甚至情感内容。现在，配价转换器（可以颠倒从句含义的语言结构，如否定）被更广泛地考虑。但是，例如，情感分析的主要技术是简单地计算一个句子中积极与消极情感词的数量，赢家获得所有的整体情感得分。简单化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陈述可以用简单的结论来撤销：“然而，电影炸弹。".随机技术的不足之处在于：（a）语言相关特征尚未被识别，因此还没有通用的注释方案，也没有训练语料库（B）它们的组合行为对于简单的注释方案来说太复杂了。随着对所有不同元命题设备的性质和行为的更好理解，将产生更好的注释模式，这将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，以及它们如何有助于文本的意义，我建议开发一个注释方案和组成语义的文本，连贯地对待不同的意义组件。

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.