Dependency-based Compositional Semantics (DCS) is a framework of natural language semantics with easy-to-process structures as well as strict semantics.

The pursue of a logic more suitable for natural language inference is not new.

Much work has been done in mapping natural language into database queries (Cai and Yates, 2013; Kwiatkowski et al., 2013; Poon, 2013).

can thus be considered as an attempt to characterize a fragment of FOL that is suited for both natural language inference and transparent syntax-semantics mapping, through the choice of operations and relations on sets.

It is expressive enough to represent complex natural language queries on a relational database, yet simple enough to be latently learned from question-answer pairs.

In this section we describe the idea of representing natural language semantics by DCS trees, and achieving inference by computing logical relations among the corresponding abstract denotations.

DCS trees has been proposed to represent natural language semantics with a structure similar to dependency trees (Liang et al., 2011) (Figure 1).

2.4.1 Natural language to DCS trees

Given an input utterance, we first use a simple method to deterministically generate a set of candidate logical forms with a canonical realization in natural language for each.

Utterance generation While mapping general language utterances to logical forms is hard, we observe that it is much easier to generate a canonical natural language utterances of our choice given a logical form.

use a KB over natural language extractions rather than a formal KB and so querying the KB does not require a generation step — they paraphrase questions to KB entries directly.

We consider the semantic parsing problem of mapping natural language utterances into logical forms to be executed on a knowledge base (KB) (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2005; Wong and Mooney, 2007; Kwiatkowski et al., 2010).

Semantic parsers need to somehow associate natural language phrases with logical predicates, e.g., they must learn that the constructions “What

To learn these map-tings, traditional semantic parsers use data which airs natural language with the KB.

date logical forms Zac, and then for each 2 E 236 generate a small set of canonical natural language utterances Cz.

Second, natural language utterances often do not express predicates explicitly, e. g., the question “What is Italy’s money?” expresses the binary predicate CurrencyOf with a possessive construction.

Our framework accommodates any paraphrasing method, and in this paper we propose an association model that learns to associate natural language phrases that co-occur frequently in a monolingual parallel corpus, combined with a vector space model, which learns to score the similarity between vector representations of natural language utterances (Section 5).

In this paper, we consider a new zero-shot learning task of extracting entities specified by a natural language query (in place of seeds) given only a single web page.

In our case, we only have the natural language query, which presents the more difficult problem of associating the entity class in the query (e.g., hiking trails) to concrete entities (e.g., Avalon Super Loop).

Another related line of work is information extraction from text, which relies on natural language patterns to extract categories and relations of entities.

In future work, we would like to explore the issue of compositionality in queries by aligning linguistic structures in natural language with the relative position of entities on web pages.

In this paper, we propose a novel task, zero-shot entity extraction, where the specification of the desired entities is provided as a natural language query.

In our setting, we take as input a natural language query and extract entities from a single web page.

For evaluation, we created the OPENWEB dataset comprising natural language queries from the Google Suggest API and diverse web pages returned from web search.

We define the zero-shot entity extraction task as follows: let at be a natural language query (e.g., hiking trails near Baltimore), and to be a web page.

Answering natural language questions using the Freebase knowledge base has recently been explored as a platform for advancing the state of the art in open domain semantic parsing.

One challenge for natural language querying against a KB is the relative informality of queries as compared to the grammar of a KB.

To compensate for the problem of domain mismatch or overfitting, we exploited ClueWeb, mined mappings between KB relations and natural language text, and showed that it helped both relation prediction and answer extraction.

However, most Freebase relations are framed in a way that is not commonly addressed in natural language questions.

Question answering (QA) from a knowledge base (KB) has a long history within natural language processing, going back to the 1960s and 1970s, with systems such as Baseball (Green Jr et al., 1961) and Lunar (Woods, 1977).

These systems were limited to closed-domains due to a lack of knowledge resources, computing power, and ability to robustly understand natural language .

Eventually, we hope to extend the techniques to synthesize even more complex structures, such as computer programs, from natural language .

As the questions are posted to a web forum, the posts often contained additional comments which were not part of the word problems and the solutions are embedded in long freeform natural language descriptions.

This allows for a tighter mapping between the natural language and the system template, where the words aligned to the first equation in the template come from the first two sentences, and the words aligned to the second equation come from the third.

Document level features Oftentimes the natural language in ac will contain words or phrases which are indicative of a certain template, but are not associated with any of the words aligned to slots in the template.

Single Slot Features The natural language cc always contains one or more questions or commands indicating the queried quantities.

Situated Semantic Interpretation There is a large body of research on learning to map natural language to formal meaning representations, given varied forms of supervision.

Second, the measure makes sense theoretically, both from algorithmic and native language acquisition points of view.

We also make an interesting observation that the impressionistic evaluation of syntactic complexity is better approximated by the presence or absence of grammar and usage patterns (and not by their frequency of occurrence), an idea supported by studies in native language acquisition.

Studies in native language acquisition, have considered multiple grammatical developmental indices that represent the grammatical levels reached at various stages of language acquisition.

0 In the domain of native language acquisition, the presence or absence of a grammatical structure indicates grammatical development.

The inductive classifier we use here is the maximum-entropy model (MaxEnt) which has been used to solve several statistical natural language processing problems with much success (Berger et al., 1996; Borthwick et al., 1998; Borthwick, 1999; Pang et al., 2002; Klein et al., 2003; Rosenfeld, 2005).

We will apply our proposed R2NN to other tree structure learning tasks, such as natural language parsing.

Applying DNN to natural language processing (NLP), representation or embedding of words is usually learnt first.

Recursive neural networks, which have the ability to generate a tree structured output, are applied to natural language parsing (Socher et al., 2011), and they are extended to recursive neural tensor networks to explore the compositional aspect of semantics (Socher et al., 2013).

To generate a tree structure, recursive neural networks are introduced for natural language parsing (Socher et al., 2011).

For example, for nature language parsing, sum] is the representation of the parent node, which could be a NP or VP node, and it is also the representation of the whole subtree covering from Z to n .

We have proposed STRUCT, a general-purpose natural language generation system which is comparable to current state-of-the-art generators.

Natural language generation (NLG) develops techniques to extend similar capabilities to automated systems.

to Natural Language Generation

This is then used by a surface realization module which encodes the enriched semantic representation into natural language .

In the MDP we use for NLG, we must define each element of the tuple in such a way that a plan in the MDP becomes a sentence in a natural language .

Negative expressions are common in natural language text and play a critical role in information extraction.

Negation expressions are common in natural language text.

Horn, 1989; van der Wouden, 1997), and there were only a few in natural language processing with focus on negation recognition in the biomedical domain.

Due to the increasing demand on deep understanding of natural language text, negation recognition has been drawing more and more attention in recent years, with a series of shared tasks and workshops, however, with focus on cue detection and scope resolution, such as the BioNLP 2009 shared task for negative event detection (Kim et al., 2009) and the ACL 2010 Workshop for scope resolution of negation and speculation (Morante and Sporleder, 2010), followed by a special issue of Computational Linguistics (Morante and Sporleder, 2012) for modality and negation.

As our input consists of natural language attributes, the model would infer textual attributes given visual attributes and vice versa.

The two modalities are encoded as vectors of natural language attributes and are obtained automatically from decoupled text and image data.

Recent years have seen a surge of interest in single word vector spaces (Turney and Pantel, 2010; Collobert et al., 2011; Mikolov et al., 2013) and their successful use in many natural language applications.

The visual and textual modalities on which our model is trained are decoupled in that they are not derived from the same corpus (we would expect co-occurring images and text to correlate to some extent) but unified in their representation by natural language attributes.

Natural language is diverse.

Information extraction projects aim to distill relational facts from natural language text (Auer et al., 2007; Bollacker et al,2008;(knlynietal,2010;Faderetal,2011; Nakashole et al., 2011; Del Corro and Gemulla, 2013).

However, such scores are often tied to the extractor’s ability to read and understand natural language text.

The focus is on understanding natural language , including the use of negation.

Semantic parsing is the problem of mapping natural language strings into meaning representations.

Although it does not encode quantifiers, tense, or modality, the set of semantic phenomena included in AMR were selected with natural language applications—in particular, machine translation—in mind.

While all semantic parsers aim to transform natural language text to a formal representation of its meaning, there is wide variation in the meaning representations and parsing techniques used.

Natural language interfaces for querying databases have served as another driving application (Zelle and Mooney, 1996; Kate et al., 2005; Liang et al., 2011, inter alia).

This underlying intuition, adopted from formal semantics of natural language , motivated the creation of the lexical function model of composition (lf) (Baroni and Zamparelli, 2010; Co-ecke et al., 2010).

The lf model can be seen as a projection of the symbolic Montagovian approach to semantic composition in natural language onto the domain of vector spaces and linear operations on them (Baroni et al., 2013).

The full range of semantic types required for natural language processing, including those of adverbs and transitive verbs, has to include, however, tensors of greater rank.

Given a pair of entities (A,B) in S, the first step is to express the relation between A and B with some feature representation using a feature extraction scheme x. Lexical or syntactic patterns have been successfully used in numerous natural language processing tasks, including relation extraction.

Each node is augmented with relevant part-of-speech (POS) using the Python Natural Language Processing Tool Kit.

Because not-a-relation is a background or default relation type in the relation classification task, and because it has rather high variation when manifested in natural language , we have found it difficult to obtain a distance metric W that allows the not-a-relation samples to form clusters naturally using transductive inference.

Two of the fundamental components of a natural language communication are word sense discovery (Jones, 1986) and word sense disambiguation (Ide and Veronis, 1998).

two aspects are not only important from the perspective of developing computer applications for natural languages but also form the key components of language evolution and change.

Word sense disambiguation as well as word sense discovery have both remained key areas of research right from the very early initiatives in natural language processing research.

We describe a system capable of translating native language (L1) fragments to foreign language (L2) fragments in an L2 context.

The type of translation assistance system under investigation here encourages language learners to write in their target language while allowing them to fall back to their native language in case the correct word or expression is not known.

Whereas machine translation generally concerns the translation of whole sentences or texts from one language to the other, this study focusses on the translation of native language (henceforth L1) words and phrases, i.e.

In all these approaches, grammar and lexicon are developed manually and it is assumed that the lexicon associates semantic sub-formulae with natural language expressions.

As discussed in (Power and Third, 2010), one important limitation of these approaches is that they assume a simple deterministic mapping between knowledge representation languages and some controlled natural language (CNL).

(Lu and Ng, 2011) focuses on generating natural language sentences from logical form (i.e., lambda terms) using a synchronous context-free grammar.