We propose a supervised method of extracting event causalities like conduct slash-and—barn agriculture—>exacerbate desertification from the web using semantic relation (between nouns), context, and association features.

3.2.1 Semantic Relation Features

We hypothesize that two nouns with some particular semantic relations are more likely to constitute event causality.

Below we describe the semantic relations that we believe are likely to constitute event causality.

slash-and—burn agriculture and desertification) that take some specific binary semantic relations (e.g.

Note that semantic relations are not restricted to those directly relevant to causality like A CAUSES B but can be those that might seem irrelevant to causality like A IS AN INGREDIENT FOR B (e.g.

Our underlying intuition is the observation that event causality tends to hold between two entities linked by semantic relations which roughly entail that one entity strongly affects the other.

Besides features similar to those described above, we propose semantic relation features3 that include those that are not obviously related to causality.

(2012) used semantic relations to generalize acquired causality instances.

In order to identify semantically-related mentions for collective inference, we detect meta path-based semantic relations through social networks.

semantIc relatedness ) SImIIarIty)

Principle 3 ( Semantic Relatedness ): Two highly semantically-related mentions are more likely to be linked to two highly semantically-related concepts.

The label assignment is obtained by our semi-supervised graph regularization framework based on a relational graph, which is constructed from local compatibility, coreference, and semantic relatedness relations.

In this subsection, we introduce the concept meta path which will be used to detect coreference (section 4.3) and semantic relatedness relations (section 4.4).

Each meta path represents one particular semantic relation .

4.4 Semantic Relatedness

First, compared to previous methods which solely employed opinion relations among words, our method constructs a heterogeneous graph to model two types of relations, including semantic relations and opinion relations.

We call such relations between homogeneous words as semantic relations .

Intuitively, besides opinion relations, semantic relations may provide additional rich clues for indicating opinion targets/words.

Solid curves and dotted lines respectively mean semantic relations and opinion relations between two candidates.

However, all aforementioned methods only employed opinion relations for the extraction, but ignore considering semantic relations among homogeneous candidates.

In terms of considering semantic relations among words, our method is related with several approaches based on topic model (Zhao et al., 2010; Moghaddam and Ester, 2011; Moghaddam and Ester, 2012a; Moghaddam and Ester, 2012b; Mukherjee and Liu, 2012).

Although these models could be used for our task according to the associations between candidates and topics, solely employing semantic relations is still one-sided and insufficient to obtain expected performance.

Different from traditional methods, besides opinion relations among words, we additionally capture semantic relations among homogeneous candidates.

E” C E represents the semantic relations between two opinion target candidates.

Such techniques are referred to as semantic relatedness (Strube and Ponzetto, 2006), collective disambiguation (Hoffart et al., 2011b), or joint disambiguation (Fahrni et al., 2012).

For example, if a NE describes a city name like Paris, it is more probable that the correct link for this city name designates Paris (France) rather than Paris (Texas) if a neighbor entity offers candidate links semantically related to Paris (France) like the Seine river or the Champs-Elyse’es.

The paper makes the following novel propositions: l) the ontology used to evaluate the relatedness of candidates is replaced by internal links and categories from the Wikipedia corpus; 2) the coherence of entities is improved prior to the calculation of semantic relatedness using a co-reference resolution algorithm, and a NE label correction method; 3) the proposed method is robust enough to improve the performance of existing entity linking annotation engines, which are capable of providing a set of ranked candidates for each annotation in a document.

A basic example of semantic relatedness that should be captured is explained hereafter.

The purpose of the MDP is to capture this semantic relatedness information contained in the graph of links extracted from Wikipedia pages related to each candidate annotation.

The calculation combines two scores that we called direct semantic relation score (dsr_score) and common semantic relation score (csr_score):

also introduced the notion of semantic relatedness .

While all these approaches focus on semantic relation between entities, their potential is limited by the separate mapping of candidate links for each mention.

Only some of these systems introduce the semantic relatedness in their methods like the AIDA (Hoffart et al., 2011b) system.

We first set up a human annotation of semantic links with or without contextual information to show the importance of the textual context in evaluating the relevance of semantic similarity, and to assess the prevalence of actual semantic relations between word tokens.

This helps cover non classical semantic relations which are hard to evaluate with classical resources.

In other words, is there a semantic relation between them, either classical (synonymy, hypernymy, co-hyponymy, meronymy, co-meronymy) or not (the relation can be paraphrased but does not belong to the previous cases) ?”

They are not suitable for the evaluation of the whole range of semantic relatedness that is exhibited by distributional similarities, which exceeds the limits of classical lexical relations, even though researchers have tried to collect equivalent resources manually, to be used as a gold standard (Weeds, 2003; Bordag, 2008; Anguiano et al., 2011).

One advantage of distributional similarities is to exhibit a lot of different semantic relations , not necessarily standard lexical relations.

spective, to cover what (Morris and Hirst, 2004) call “non classical lexical semantic relations” .

We differ from all these evaluation procedures as we do not focus on an essential view of the relatedness of two lexical items, but evaluate the link in a context Where the relevance of the link is in question, an “existential” view of semantic relatedness .

This paper proposes a novel and effective method for the construction of semantic hierarchies based on word embeddings, which can be used to measure the semantic relationship between words.

In this paper, we aim to identify hypemym—hyponym relations using word embeddings, which have been shown to preserve good properties for capturing semantic relationship between words.

have attempted to automatically extract semantic relations or to construct taxonomies.

Word embeddings have been empirically shown to preserve linguistic regularities, such as the semantic relationship between words (Mikolov et al., 2013b).

Additionally, their experiment results have shown that the Skip-gram model performs best in identifying semantic relationship among words.

Looking at the well-known example: v(king) — v(queen) % v(man) —v(woman), it indicates that the embedding offsets indeed represent the shared semantic relation between the two word pairs.

The reasons are twofold: (l) Mikolov’s work has shown that the vector offsets imply a certain level of semantic relationship .

(2013b) further observe that the semantic relationship of words can be induced by performing simple algebraic operations with word vectors.

As mentioned in Section 2.1.1, we build the WN graph by including all the synsets and semantic relations defined in WordNet (e.g., hypernymy and meronymy) and further populate the relation set by connecting a synset to all the other synsets that appear in its disambiguated gloss.

The other two resources, i.e., WT and OW, do not provide a reliable network of semantic relations , therefore we used our ontologization approach to construct their corresponding semantic graphs.

However, not all lexical resources provide explicit semantic relations between concepts and, hence, machine-readable dictionaries like Wiktionary have first to be transformed into semantic graphs before such graph-based approaches can be applied to them.

Our ontologization algorithm takes as input a lexicon L and outputs a semantic graph G = (V, E) where, as already defined in Section 2, V is the set of concepts in L and E is the set of semantic relations between these concepts.

usually the case with machine-readable dictionaries, where structuring the resource involves the arduous task of connecting lexicographic senses by means of semantic relations .

Therefore, we assume that a lexical resource L can be represented as an undirected graph G = (V, E) where V is the set of nodes, i.e., the concepts defined in the resource, and E is the set of undirected edges, i.e., semantic relations between concepts.

However, other resources such as Wiktionary do not provide semantic relations between concepts and, therefore, have first to be transformed into semantic networks before they can be aligned using our alignment algorithm.

o to winden the scope (i.e., including Kurmanji synsets), the coverage (i.e., going beyond Base Concepts) , and richness (supporting additional semantic relations ) of the current version.

3.3 Limited Support for Semantic Relation Types

As shown in Table 2, there are several WordNet semantic relations for each syntactic categories.

The most important semantic relation in WordNet is Hyponymy and this relation is the only one support in KurdNet (Aliabadi et al., 2014).

0 Expand: in this model, the synsets are built in correspondence with the WordNet synsets and the semantic relations are directly imported.

Finding Semantic Relations Creating Graphical User Interface

Srikumar and Roth (2013) broadened this perspective by considering a class-based approach by collapsing semantically-related senses across prepositions, thereby deriving a semantic relation inventory.

While their emphasis was on modeling semantic relations , they achieved an accuracy of 83.53 percent for preposition disambiguation.

As mentioned above, PDEP has a field for the Srikumar semantic relation , initially populated for the SemEval prepositions, and being extended to cover all other prepositions.

Section 5 describes how we can use PDEP for the analysis of semantic role and semantic relation inventories.

A key element of Srikumar and Roth was the use of these classes to model semantic relations across prepositions (e.g., grouping all the Temporal senses of the SemEval prepositions).

In TPP, each sense was characterized with its complement and attachment (or governor) properties, its class and semantic relation , substitutable prepositions, its syntactic positions, and any FrameNet frame and frame element usages (where available).

However, the traditional syntactic tree representation is often too coarse or ambiguous to accurately capture the semantic relation information between two entities.

1) The syntactic tree focuses on representing syntactic relation/structure, which is often too coarse or ambiguous to capture the semantic relation information.

For example, all the three trees in Figure 1 share the same possessive syntactic structure, but express quite different semantic relations : where “Mary’s brothers” expresses PER-SOC Family relation, “Mary ’s toys” expresses Possession relation, and “New York’s airports” expresses PH YS-Located relation.

better capture the semantic relation information between two entities.

Two experiments were performed with Infection and TKT: (1) an evaluation of players’ ability to play accurately and to validate semantic relations and image associations and (2) a comprehensive cost comparison.

Semantic knowledge bases such as WordNet (Fellbaum, 1998), YAGO (Suchanek et al., 2007), and BabelNet (Navigli and Ponzetto, 2010) provide ontological structure that enables a wide range of tasks, such as measuring semantic relatedness (Budanitsky and Hirst, 2006) and similarity (Pilehvar et al., 2013), paraphrasing (Kauchak and Barzilay, 2006), and word sense disambiguation (Navigli and Ponzetto, 2012; Moro et al., 2014).

semantic networks, using two games that operate on complementary sources of information: semantic relations and sense-image mappings.

Second, BabelNet contains the semantic relations from both WordNet and hyperlinks in Wikipedia; these relations are again an ideal case of validation, as not all hyperlinks connect semantically-related pages in Wikipedia.

While the aforementioned external resource-based features attempt to encode how salient a candidate keyphrase is, Turney (2003) proposes features that encode the semantic relatedness between two candidate keyphrases.

Noting that candidate keyphrases that are not semantically related to the predicted keyphrases are unlikely to be keyphrases in technical reports, Turney employs coherence features to identify such candidate keyphrases.

Semantic relatedness is encoded in the coherence features as two candidate keyphrases’ pointwise mutual information, which Turney computes by using the Web as a corpus.

To extract semantic relations from text, three types of approaches have been applied.

Using question answering as an example (Wang et al., 2012): in question analysis, the semantic relations between the question focus and each term in the clue can be used to identify the weight of each term so that better search queries can be generated.

In candidate answer scoring, relation-based matching algorithms can go beyond explicit lexical and syntactic information to detect implicit semantic relations shared across the question and passages.

HIER NE, semantic relations

Small filters at higher layers can capture syntactic or semantic relations between noncontinuous phrases that are far apart in the input sentence.

Likewise, the induced graph structure in a DCNN is more general than a parse tree in that it is not limited to syntactically dictated phrases; the graph structure can capture short or long-range semantic relations between words that do not necessarily correspond to the syntactic relations in a parse tree.

The label on the edge indicates the type of semantic relationship .

Because each word in a sentence may be in a semantic relationship with any other word (including itself), a sentence of length n has n2 possible edges.

In this way, we jointly perform identification (determining whether a semantic relationship exists) and classification (determining the semantic label).

We further employ the Generalized Plya urn (GPU) model (Mahmoud, 2008) which was shown to be effective in leveraging semantically related words (Chen et al., 2013a, Chen et al., 2013b, Mimno et al., 2011).

Each cluster contains semantically related topics likely to indicate the same real-world aspect.

Using two terms in a set is sufficient to cover the semantic relationship of the terms belonging to the same aspect.

To give just one last example, distributional semanticists have looked at whether certain properties of vectors reflect semantic relations in the expected way: e.g., whether the vectors of hypemyms “distribution-ally include” the vectors of hyponyms in some mathematical precise sense.

Does the structure of predict vectors mimic meaningful semantic relations ?

Semantic relatedness A first set of semantic benchmarks was constructed by asking human subjects to rate the degree of semantic similarity or relatedness between two words on a numerical scale.