We also show that the monolingual translation probabilities obtained (i) are comparable to traditional semantic relatedness measures and (ii) significantly improve the results over the query likelihood and the vector-space model for answer finding.

To do so, we compare translation probabilities with concept vector based semantic relatedness measures with respect to human relatedness rankings for reference word pairs.

Section 2 discusses related work on semantic relatedness and statistical translation models for retrieval.

Semantic relatedness experiments are detailed in Section 4.

the different kinds of data encode different types of information, including semantic relatedness and similarity, as well as morphological relatedness.

2.2 Semantic Relatedness

While classical measures of semantic relatedness have been extensively studied and compared, based on comparisons with human relatedness judgements or word-choice problems, there is no comparable intrinsic study of the relatedness measures obtained through word translation probabilities.

In this study, we use the correlation with human rankings for reference word pairs to investigate how word translation probabilities compare with traditional semantic relatedness measures.

The aim of this first experiment is to perform an intrinsic evaluation of the word translation probabilities obtained by comparing them to traditional semantic relatedness measures on the task of ranking word pairs.

Human judgements of semantic relatedness can be used to evaluate how well semantic relatedness measures reflect human rankings by correlating their ranking results with Spearman’s rank correlation coefficient.

A common assumption is that, when investigating the semantics in articles such as those in Wikipedia (e. g. semantic Wikipedia (Volkel et al., 2006)), key information related to a concept described on a page p lies within the set of links l(p) on that page; particularly, it is likely that a salient semantic relation 7“ exists between p and a related page 19’ E l(p).

To discover a range of semantic relations from a large corpus, we present an unsupervised relation extraction method using deep linguistic information to alleviate surface and noisy surface patterns generated from a large corpus, and use Web frequency information to ease the sparseness of linguistic information.

A salient challenge and research interest for frequent pattern mining is abstraction away from different surface realizations of semantic relations to discover discriminative patterns efficiently.

Linguistic analysis is another effective technology for semantic relation extraction, as described in many reports such as (Kambhatla, 2004); (Bunescu and Mooney, 2005); (Harabagiu et al., 2005); (Nguyen et al., 2007).

Currently, linguistic approaches for semantic relation extraction are mostly supervised, relying on pre-specification of the desired relation or initial seed words or patterns from hand-coding.

Given a concept described in a Wikipedia article, our idea of preprocessing executes initial consideration of all anchor-text concepts linking to other Wikipedia articles in the article as related concepts that might share a semantic relation with the entitled concept.

Querying a concept pair using a search engine (Google), we characterize the semantic relation between the pair by leveraging the vast size of the Web.

A salient difficulty posed by dependency pattern clustering is that concept pairs of the same semantic relation cannot be merged if they are expressed in different dependency structures.

(Turney, 2006) presented an unsupervised algorithm for mining the Web for patterns expressing implicit semantic relations .

In addition, to obtain semantic information for concept pairs, we generate dependency patterns to abstract away from different surface realizations of semantic relations .

This paper proposes a novel framework for a large-scale, accurate acquisition method for monolingual semantic knowledge, especially for semantic relations between nominals such as hyponymy and meronymy.

The acquisition of semantic relations between nominals can be seen as a classification task of semantic relations — to determine whether two nominals hold a particular semantic relation (Girju et al., 2007).

Recently, there has been increased interest in semantic relation acquisition from corpora.

Some regarded Wikipedia as the corpora and applied handcrafted or machine-learned rules to acquire semantic relations (Herbelot and Copestake, 2006; Kazama and Torisawa, 2007; Ruiz-casado et al., 2005; Nastase and Strube, 2008; Sumida et al., 2008; Suchanek et al., 2007).

Several researchers who participated in SemEval-07 (Girju et al., 2007) proposed methods for the classification of semantic relations between simple nominals in English sentences.

Also note that the only two features predictive of the comparison class (indicated by * in Table l): the-it and to-it, contain only function words rather than semantically related non-function words.

We show that the features in fact do not capture semantic relation but rather give information about function word co-occurrences.

Semantic relations vs. function word pairs If the hypothesis for word pair triggers of discourse relations were true, the analysis of unambiguous relations can be used to discover pairs of words with causal or contrastive relations holding between them.

One approach for reducing the number of features follows the hypothesis of semantic relations between words.

Many works on machine readable dictionaries utilized definitions to identify semantic relations between words (Ide and Jean, 1993).

Ponzetto and Strube (2007) identified the subsumption (ISA) relation from Wikipedia’s category tags, while in Yago (Suchanek et al., 2007) these tags, redirect links and WordNet were used to identify instances of 14 predefined specific semantic relations .

However this is a rather loose notion, which only indicates that terms are semantically “related” and are likely to co-occur with each other.

An examination of the paths in All-N reveals, beyond standard hyponymy and synonymy, various semantic relations that satisfy lexical reference, such as Location, Occupation and Creation, as illustrated in Table 3.

We have shown that this model is competitive for determining whether there exists a semantic relationship between them, and can be improved by principled combination with more standard lexical overlap approaches.

WordNet relation(s) The model next chooses a lexical semantics relation between 3360-) and the yet-to-be-chosen word ti (line 12).

Word Finally, the target word is randomly chosen from among the set of words that bear the lexical semantic relationship just chosen (line 13).

All of them are indeed semantically related to the domain.

We find semantic relatedness of each noun in the post with the two main topics of the debate by calculating the Pointwise Mutual Information (PMI) between the term and each topic over the entire web corpus.

We use the API provided by the Measures of Semantic Relatedness (MSR)4 engine for this purpose.

Existing work on automatic taxonomy induction has been conducted under a variety of names, such as ontology learning, semantic class learning, semantic relation classification, and relation extraction.

They have been applied to extract various types of lexical and semantic relations , including isa, part-of, sibling, synonym, causal, and many others.

The features used in this work are indicators of semantic relations between terms.