We describe a method of normalizing numerical expressions referring to the same amount in text into a unified semantic representation .

For instance, the context of 319 people in the sentence 319 people face a water shortage is “face” and “water shortage.” In order to extract and aggregate numerical expressions in various documents, we converted the numerical expressions into semantic representations (to be described in Section 4.1), and extracted their context (to be described in Section 4.2).

Numerical Semantic representation Expression Value | Unit ‘ Mod.

The first step for collecting numerical expressions is to recognize when a numerical expression is mentioned and then to normalize it into a semantic representation .

1, each category is associated with a lambda term of semantic representations , and each combinatory rule is associated with rules for semantic composition.

Since these rules are universal, we can obtain different semantic representations by switching the semantic representations of lexical categories.

coordination and semantic representation in particular.

12) must be used to construct the semantic representation , namely the PAS.

We evaluated the effectiveness of our attribute classifiers by integrating their predictions with traditional text-only models of semantic representation .

(2004)) to learn a joint semantic representation from the textual and visual modalities.

Visual input represents a major source of data from which humans can learn semantic representations of linguistic and nonlinguistic communicative actions (Regier, 1996).

Grounding semantic representations with visual information is an instance of multimodal leam-ing.

On average, each concept was annotated with 19 attributes; approximately 14.5 of these were not part of the semantic representation created by McRae et al.’s (2005) participants for that concept even though they figured in the representations of other concepts.

We present UCCA, a novel multilayered framework for semantic representation that aims to accommodate the semantic distinctions expressed through linguistic utterances.

This paper presented Universal Conceptual Cognitive Annotation (UCCA), a novel framework for semantic representation .

An extensive comparison of UCCA to existing approaches to syntactic and semantic representation , focusing on the major resources available for English, is found in Section 5.

Several annotated corpora offer a joint syntactic and semantic representation .

In the future, we plan to investigate how to adapt smatch to other semantic representations .

Related work on directly measuring the semantic representation includes the method in (Dri-dan and Oepen, 2011), which evaluates semantic parser output directly by comparing semantic substructures, though they require an alignment between sentence spans and semantic substructures.

Following (Langkilde and Knight, 1998) and (Langkilde-Geary, 2002), we refer to this semantic representation as AMR (Abstract Meaning Representation).

3.1 Tree-based Semantic Representations

In this way, each nonterminal symbol has a semantic representation and an associated parse category.

We have presented a novel technique for surface realisation that treats generation as a sequence labelling task by combining a CRF with tree-based semantic representations .

Semantic representations constructed in this way beat a strong baseline and can be of higher quality than representations directly constructed from corpus data.

A natural extension of our research is to address morpheme composition and morphological induction jointly, trying to model the intuition that good candidate morphemes should have coherent semantic representations .

Our goal is to automatically construct, given distributional representations of stems and affixes, semantic representations for the derived words containing those stems and affixes.

We demonstrate that our semantic representation achieves state-of-the-art performance in three experiments using semantic similarity at different lexical levels (i.e., sense, word, and text), surpassing the performance of previous similarity measures that are often specifically targeted for each level.

Despite the potential advantages, few approaches to semantic similarity operate at the sense level due to the challenge in sense-tagging text (Navigli, 2009); for example, none of the top four systems in the recent SemEval-2012 task on textual similarity compared semantic representations that incorporated sense information (Agirre et al., 2012).

(2009) used a similar semantic representation of short texts from random walks on WordNet, which was applied to paraphrase recognition and textual entailment.