Composing meaning

点击翻译按钮获取中文摘要

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.