This opinion paper discusses subjective natural language problems in terms of their motivations, applications, characterizations, and implications.

Subjective natural language problems extend well beyond sentiment and opinion analysis.

Affective semantics is difficult for many automatic techniques to capture because rather than simple text-derived ‘surface’ features, it requires sophisticated, ‘deep’ natural language understanding that draws on subjective human knowledge, interpretation, and experience.

0 Nontraditional intersubjectivity Subjective natural language processing problems are generally problems of meaning or communication where so-called intersubjective agreement does not apply in the same way as in traditional tasks.

In computational linguistics and natural language processing (NLP), current efforts on subjective natural language problems are concentrated on the vibrant field of opinion mining and sentiment analysis (Liu, 2010; Tackstrom, 2009), and ACL-HLT 2011 lists Sentiment Analysis, Opinion Mining and Text Classification as a subject area.

The purpose of this opinion paper is not to provide a survey of subjective natural language prob-

Rather, it intends to launch discussions about how subjective natural language problems have a vital role to play in computational linguistics and in shaping fundamental questions in the field for the future.

Subjective natural language processing problems represent exciting frontier areas that directly relate to advances in artificial natural language behavior, improved intelligent access to information, and more agreeable and comfortable language-based human-computer interaction.

From a practical, application-oriented point of View, dedicating more resources and efforts to subjective natural language problems is a natural step, given the wealth of available written, spoken or multimodal texts and information associated with creativity, socializing, and subtle interpretation.

Experimental results verify the effectiveness of our approach for both short keyword queries, and verbose natural language queries.

Automatic markup of textual documents with linguistic annotations such as part-of-speech tags, sentence constituents, named entities, or semantic roles is a common practice in natural language processing (NLP).

Instead of just focusing our attention on keyword queries, as is often done in previous work (Barr et al., 2008; Bergsma and Wang, 2007; Tan and Peng, 2008; Guo et al., 2008), we also explore the performance of our annotations with more complex natural language search queries such as verbal phrases and wh-questions, which often pose a challenge for IR applications (Bendersky et al., 2010; Kumaran and Allan, 2007; Kumaran and Carvalho, 2009; Lease, 2007).

Instead, we are interested in annotation of queries of different types, including verbose natural language queries.

An additional research area which is relevant to this paper is the work on joint structure modeling (Finkel and Manning, 2009; Toutanova et al., 2008) and stacked classification (Nivre and McDonald, 2008; Martins et al., 2008) in natural language processing.

We built a system that interprets natural language utterances much more accurately than existing systems, despite using no annotated logical forms.

Think of DCS as a higher-level programming language tailored to natural language , which results in programs (DCS trees) which are much simpler than the logically-equivalent lambda calculus formulae.

The integration of natural language with denotations computed against a world (grounding) is becoming increasingly popular.

We now turn to the task of mapping natural language utterances to DCS trees.

Bayesian inference methods have become popular in natural language processing (Goldwater and Grif-fiths, 2007; Finkel et al., 2005; Blunsom et al., 2009; Chiang et al., 2010).

A common phenomenon observed while modeling natural language problems is sparsity.

We use natural language processing techniques to attack letter substitution ciphers.

In a letter substitution cipher, every letter p in the natural language (plaintext) sequence is replaced by a cipher token 0, according to some substitution key.

Understanding the meaning of text is a long term goal in the natural language processing community.

Within natural language processing, negation has drawn attention mainly in sentiment analysis (Wilson et al., 2009; Wiegand et al., 2010) and the biomedical domain.

None of the above references aim at detecting or annotating the focus of negation in natural language .

This phenomenon has been observed in several natural language synthesis tasks such as generation and summarization, in which a single gold standard is inadequate to fully assess performance.

This notion of preferential ordering of discourse relations is observed in natural language in general,

This task, discourse parsing, has been a recent focus of study in the natural language processing (NLP) community, largely enabled by the availability of large-scale discourse annotated corpora (Wellner and Pustejovsky, 2007; Elwell and Baldridge, 2008; Lin et al., 2009; Pitler et al., 2009; Pitler and Nenkova, 2009; Lin et al., 2010; Wang et al., 2010).

This means that researchers do not have a common development and test set for natural language processing of learner English such as for grammatical error detection.

The availability of learner corpora is still somewhat limited despite the obvious usefulness of such data in conducting research on natural language processing of learner English in recent years.

This is one of the most active research areas in natural language processing of learner English.