For MBR decoding, we instead leverage a similarity measure 8(6; 6’) to choose a translation using the model’s probability distribution P(e| f), which has support over a set of possible translations E. The Viterbi derivation 6* is the mode of this distribution.

Given any similarity measure 8 and a k-best list E, the minimum Bayes risk translation can be found by computing the similarity between all pairs of sentences in E, as in Algorithm 1.

An example of a linear similarity measure is bag-of-words precision, which can be written as:

The Bayes optimal decoding objective is to minimize risk based on the similarity measure used for evaluation.

Unfortunately, with a nonlinear similarity measure like BLEU, we must resort to approximating the expected loss using a k-best list, which accounts for only a tiny fraction of a model’s full posterior distribution.

We show that if the similarity measure is linear in features of a sentence, then computing expected similarity for all k sentences requires only k similarity evaluations.

We present CoSimRank, a graph-theoretic similarity measure that is efficient because it can compute a single node similarity without having to compute the similarities of the entire graph.

Another advantage of CoSimRank is that it can be flexibly extended from basic node-node similarity to several other graph-theoretic similarity measures .

}raph-The0retic Similarity Measure

Apart from SimRank, many other similarity measures have been proposed.

(2006) introduce a similarity measure that is also based on the idea that nodes are similar when their neighbors are, but that is designed for bipartite graphs.

Another important similarity measure is cosine similarity of Personalized PageRank (PPR) vectors.

For each of these paraphrases, a DP is constructed and compared to that of the oov word using a similarity measure (Section 2.2).

where t is a phrase on the target side, 0 is the oov word or phrase, and s is a paraphrase of 0. p(s|0) is estimated using a similarity measure over DPs and p(t|s) is coming from the phrase-table.

2.3 Similarity Measures

In Section 2.2 and 2.3, different types of association measures and similarity measures have been explained to build and compare distributional profiles.

As the results show, the combination of PMI as association measure and cosine as DP similarity measure outperforms the other possible combinations.

Each phrase type represents a vertex in the graph and is connected to other vertices with a weight defined by a similarity measure between the two profiles (Section 2.3).

However based on the definition of the similarity measures using context, it is quite possible that an oov node and a labeled node which are connected to the same unlabeled node do not share any context words and hence are not directly connected.

In such a graph, the similarity of each pair of nodes is computed using one of the similarity measures discussed above.

In order to compare semantic signatures, we adopt the Cosine similarity measure as a baseline method.

The top-ranking participating systems in the SemEval-2012 task were generally supervised systems utilizing a variety of lexical resources and similarity measurement techniques.

3.3 Similarity Measure Analysis

In addition, we present in the table correlation scores for four other similarity measures reported by B'ar et al.

Different evaluation methods exist in the literature for evaluating the performance of a word-level semantic similarity measure ; we adopted two well-established benchmarks: synonym recognition and correlating word similarity judgments with those from human annotators.

We adopt this task as a way of evaluating our similarity measure at the sense level.

Within this context, we propose a new methodology that adapts the classical K -means algorithm to a third-order similarity measure initially developed for NLP tasks.

In this paper, we proposed a new PRC approach which (1) is based on the adaptation of the K -means algorithm to third-order similarity measures and (2) proposes a coherent stopping criterion.

In particular, p is the size of the word feature vectors representing both Web snippets and centroids (p = 2.5), K is the number of clusters to be found (K = 2..10) and 8(Wik, 14/31) is the collocation measure integrated in the InfoSimba similarity measure .

feature vectors are hard to define in small collections of short text fragments (Timonen, 2013), (2) existing second-order similarity measures such as the cosine are unadapted to capture the semantic similarity between small texts, (3) Latent Semantic Analysis has evidenced inconclusive results (Osinski and Weiss, 2005) and (4) the labeling process is a surprisingly hard extra task (Carpineto et al., 2009).

For that purpose, we propose a new methodology that adapts the classical K -means algorithm to a third-order similarity measure initially developed for Topic Segmentation (Dias et al., 2007).

Within the context of PRC, the K -means algorithm needs to be adapted to integrate third-order similarity measures (Mihalcea et al., 2006; Dias et al., 2007).

Third-order similarity measures, also called weighted second-order similarity measures, do not rely on exact matches of word features as classical second-order similarity measures (6. g. the cosine metric), but rather evaluate similarity based on related matches.

In this paper, we propose to use the third-order similarity measure called InfoSimba introduced in (Dias et al., 2007) for Topic Segmentation and implement its simplified version 83 in Equation 2.

To apply our PSSS on IQAPS inference task, we use it as the sentiment similarity measure in the algorithm explained in Figure 4.

The second row of Table 4 show the results of using a popular semantic similarity measure , PMI, as the sentiment similarity (SS) measure in Figure 4.

Table 4 shows the effectiveness of our sentiment similarity measure .

Semantic similarity measures such as Latent Semantic Analysis (LSA) (Landauer et al., 1998) can effectively capture the similarity between semantically related words like "car" and "automobile", but they are less effective in relating words with similar sentiment orientation like "excellent" and "superior".

We show that our approach effectively outperforms the semantic similarity measures in two NLP tasks: Indirect yes/no Question Answer Pairs (IQAPs) Inference and Sentiment Orientation (S0) prediction that are described as follows:

Previous research utilized the semantic relations between words obtained from WordNet (Hassan and Radev, 2010) and semantic similarity measures (e.g.

They also utilized Latent Semantic Analysis (LSA) (Landauer et al., 1998) as another semantic similarity measure .

However, both PM and LSA are semantic similarity measure .

(2012), we used two semantic similarity measures (PMI and LSA) for the IQAP inference task.

As we discussed above, semantic similarity measures are less effective to infer sentiment similarity between word pairs.

where sim(v, v’) is a vector similarity measure .

We note that the general DIRT scheme may be used while employing other “base” vector similarity measures .

This issue has been addressed in a separate line of research which introduced directional similarity measures suitable for inference relations (Bhagat et al., 2007; Szpektor and Dagan, 2008; Kotlerman et al., 2010).

Our scheme can be applied on top of any context-insensitive “base” similarity measure for rule learning, which operates at the word level, such as Cosine or Lin (Lin, 1998).

We apply our two-level scheme over three state-of-the-art context-insensitive similarity measures .

Then we propose various novel similarity measurements including surface features, meta-path based semantic features and social correlation features and combine them in a learning-to-rank framework.

0 We propose two new similarity measures , as well as integrating temporal information into

the similarity measures to generate global semantic features.

We first extract surface features between the morph and the candidate based on measuring orthographic similarity measures which were commonly used in entity coreference resolution (e.g.

4.2.3 Meta-Path-Based Semantic Similarity Measurements

We then adopt meta-path-based similarity measures (Sun et al., 2011a; Sun et al., 2011b), which are defined over heterogeneous networks to extract semantic features.

Existing word similarity measures are not robust to data sparseness since they rely only on the point estimation of words’ context profiles obtained from a limited amount of data.

The method uses a distribution of context profiles obtained by Bayesian estimation and takes the expectation of a base similarity measure under that distribution.

For the task of word similarity estimation using a large amount of Web data in Japanese, we show that the proposed measure gives better accuracies than other well-known similarity measures .

The BC is also a similarity measure on probability distributions and is suitable for our purposes as we describe in the next section.

Although BC has not been explored well in the literature on distributional word similarities, it is also a good similarity measure as the experiments show.

A number of semantic similarity measures have been proposed based on this hypothesis (Hindle, 1990; Grefenstette, 1994; Dagan et al., 1994; Dagan et al., 1995; Lin, 1998; Dagan et al., 1999).

In general, most semantic similarity measures have the following form:

Our technical contribution in this paper is to show that in the case where the context profiles are multinomial distributions, the priors are Dirichlet, and the base similarity measure is the Bhattacharyya coefficient (Bhattacharyya, 1943), we can derive an analytical form for Eq.

In this section, we show that if our base similarity measure is BC and the distributions under which we take the expectation are Dirichlet distributions, then Eq.

To put it all together, we can obtain a new Bayesian similarity measure on words, which can be calculated only from the hyperparameters for the Dirichlet prior, 04 and 6, and the observed counts C(wi, fk).

For each term t in the dictionary and each SMS token 3,, we define a similarity measure a(t, 3,) that measures how closely the term 75 matches the SMS token 3,.

Combining the similarity measure and the inverse document frequency (idf) of t in the corpus, we define a weight function to (t, 3,).

The similarity measure and the weight function are discussed in detail in Section 5.1.

The weight function is a combination of similarity measure between t and Si and Inverse Document Frequency (idf) of t. The next two subsections explain the calculation of the similarity measure and the idf in detail.

5.1.1 Similarity Measure

For term t E D and token 3%- of the SMS, the similarity measure a(t, 81) between them is

Our approach leverages a similarity measure that enables the structural comparison of senses across lexical resources, achieving state-of-the-art performance on the task of aligning WordNet to three different collaborative resources: Wikipedia, Wiktionary and OmegaWiki.

Our method leverages a novel similarity measure which enables a direct structural comparison of concepts across different lexical resources.

In future work, we plan to extend our concept similarity measure across different natural languages.

4.3 Similarity Measure Analysis

We explained in Section 2.1 that our concept similarity measure consists of two components: the definitional and the structural similarities.

structural similarity measure in comparison to the Dijkstra-WSA method, we carried out an experiment where our alignment system used only the structural similarity component, a variant of our system we refer to as SemAlignStr.

To do this, we apply our definitional similarity measure introduced in Section 2.1.

Figure 1 illustrates the procedure underlying our cross-resource concept similarity measurement technique.

The structural similarity component, instead, is a novel graph-based similarity measurement technique which calculates the similarity between a pair of concepts across the semantic networks of the two resources by leveraging the semantic

Our research goal was to develop a directional similarity measure suitable for learning asymmetric relations, focusing empirically on lexical expansion.

This paper investigates the nature of directional (asymmetric) similarity measures , which aim to quantify distributional feature inclusion.

Then, word vectors are compared by some vector similarity measure .

This paper advocates the use of directional similarity measures for lexical expansion, and potentially for other tasks, based on distributional inclusion of feature vectors.

We tested our similarity measure by evaluating its utility for lexical expansion, compared with baselines of the LIN, WeedsPrec and balPrec measures

Next, for each similarity measure , the terms found similar to any of the event’s seeds (‘u —> seed’) were taken as expansion terms.

Often, distributional similarity measures are used to identify expanding terms (e.g.

More generally, directional relations are abundant in NLP settings, making symmetric similarity measures less suitable for their identification.

Despite the need for directional similarity measures , their investigation counts, to the best of our knowledge, only few works (Weeds and Weir, 2003; Geffet and Dagan, 2005; Bhagat et al., 2007; Szpektor and Dagan, 2008; Michelbacher et al., 2007) and is utterly lacking.

As a consequence, improving such thesaurus is an important issue that is mainly tackled indirectly through the improvement of semantic similarity measures .

As in (Lin, 1998) or (Curran and Moens, 2002a), this building is based on the definition of a semantic similarity measure from a corpus.

For the extraction of distributional data and the characteristics of the distributional similarity measure , we adopted the options of (Ferret, 2010), resulting from a kind of grid search procedure performed with the extended TOEFL test proposed in (Freitag et al., 2005) as an optimization objective.

o similarity measure between contexts, for evaluating the semantic similarity of two words = Cosine measure.

Following work such as (Grefenstette, 1994), a widespread way to build a thesaurus from a corpus is to use a semantic similarity measure for extracting the semantic neighbors of the entries of the thesaurus.

Work based on WordNet-like lexical networks for building semantic similarity measures such as (Budanitsky and Hirst, 2006) or (Pedersen et al., 2004) falls into this category.

A part of these proposals focus on the weighting of the elements that are part of the contexts of words such as (Broda et al., 2009), in which the weights of context elements are turned into ranks, or (Zhitomirsky-Geffet and Dagan, 2009), followed and extended by (Yamamoto and Asakura, 2010), that proposes a bootstrapping method for modifying the weights of context elements according to the semantic neighbors found by an initial distributional similarity measure .

For instance, features such as ngrams of words or ngrams of parts of speech are not considered whereas they are widely used in tasks such as word sense disambiguation (WSD) for instance, probably because they would lead to very large models and because similarity measures such as the Cosine measure are not necessarily suitable for heterogeneous representations (Alexandrescu and Kirchhoff, 2007).

As a consequence, the improvement of such thesaurus is generally not directly addressed but is a possible consequence of the improvement of semantic similarity measures .

In this paper, we only tried Dice coefficient of n-grams and symmetrical sentence level BLEU as similarity measures .

In the future, we will explore other consensus features and other similarity measures , which may take document level information, or syntactic and semantic information into consideration.

Instead of using graph-based consensus confidence as features in the log-linear model, we perform structured label propagation (Struct-LP) to re-rank the n-best list directly, and the similarity measures for source sentences and translation candidates are symmetrical sentence level BLEU (equation (10)).

Tl(e,e') is the propagating probability in equation (8), with the similarity measure Sim(e,e') defined as the Dice coefficient over the set of all n-grams in e and those in e'.

defined in equation (3), takes symmetrical sentence level BLEU as similarity measure ]:

In theory we could use other similarity measures such as edit distance, string kernel.

wilj defines the weight of the edge, which is a similarity measure between nodes i and j.

Propagation probability TS (f, f ') is as defined in equation (3), and Tl(e,e') is defined given some similarity measure sim(e, 6') between labels 6 and

We show also how Thesaurus Rex supports a novel, generative similarity measure for WordNet.

more rounded similarity measures .

A similarity measure can draw on other

Their best similarity measure achieves a remarkable 0.93 correlation with human judgments on the Miller & Charles word-pair set.

Using WordNet, for instance, a similarity measure can vertically converge on a common superordinate category of both inputs, and generate a single numeric result based on their distance to, and the information content of, this common generalization.

To be as useful for creative tasks as they are for conventional tasks, we need to re-imagine our computational similarity measures as generative rather than selective, expansive rather than reductive, divergent as well as convergent and lateral as well as vertical.

Section 2 provides a brief overview of past work in the area of similarity measurement , before section 3 describes a simple bootstrapping loop for acquiring richly diverse perspectives from the web for a wide variety of familiar ideas.

In this work, we analyze various Japanese corpora using a number of collocation and word similarity measures to deduce and suggest the best collocations for Japanese second language learners.

In order to build a system that is more sensitive to constructions that are difficult for learners, we use word similarity measures that generate collocation candidates using a large Japanese language learner corpus.

similarity measures are used.

Our work follows the general approach, that is, uses similarity measures for generating the confusion set and association measures for ranking the best candidates.

Similarity measures are used to generate the collocation candidates that are later ranked using association measures.

Instead of utilizing simple similarity measures and their disjoint combinations, our method directly optimizes document and entity representations for a given similarity measure .

A supervised fine-tuning stage follows to optimize the representation towards the similarity measure .

We propose a deep learning approach that automatically learns context-entity similarity measure for entity disambiguation.

When embedding our similarity measure sim(d, 6) into (Han et al., 2011), we achieve the best results on AIDA.

ument and entity representations for a fixed similarity measure .

In fact, the underlying representations for computing similarity measure add internal structure to the given similarity measure .

The learned similarity measure can be readily incorporated into any existing collective approaches, which further boosts performance.

The best results are obtained with a novel second-order distributional similarity measure , and the positive effect is specially relevant for out-of-domain data.

We have empirically shown how automatically generated selectional preferences, using WordNet and distributional similarity measures , are able to effectively generalize lexical features and, thus, improve classification performance in a large-scale argument classification task on the CoNLL-2005 dataset.

Pantel and Lin (2000) obtained very good results using the distributional similarity measure defined by Lin (1998).

The second-order distributional similarity measures perform best overall, both in precision and recall.

We will refer to this similarity measure as simg‘n.

We will refer to these similarity measures as simE-ZE and simi’gg hereinafter.

A number of word similarity measures are proposed for clustering words for the Named Entity Recognition task.

A number of word similarity measures are used for clustering.

Among the various similarity measures of clustering, improved results are obtained using the clus-

ters which uses the similarity measurement based on proximity of the words to NE categories (defined in Section 3.3).

The Euclidean distance is used to find the similarity between the above word vectors as a similarity measure .

Using the above similarity measures we have used the k-means algorithm.

We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation.

In the final stage (Section 3.4), we produce an overall grade based upon the alignment scores found in the previous stage as well as the results of several semantic BOW similarity measures (Section 3.3).

All eight WordNet-based similarity measures listed in Section 3.3 plus the LSA model are used to produce these features.

In order to address this, we combine the graph alignment scores, which encode syntactic knowledge, with the scores obtained from semantic similarity measures .

One surprise while building this system was the consistency with which the novel technique of question demoting improved scores for the BOW similarity measures .

Our method was evaluated for each (P1, P2, P3) combination and similarity measures J0 and 197,, separately.

In this paper, we propose a novel unsupervised approach that compares the major senses of a MWE and its semantic head using distributional similarity measures to test the compositionality of the MWE.

Lee (1999) shows that J performs better than other symmetric similarity measures such as cosine, Jensen-Shannon divergence, etc.

Given the major uses of a MWE and its semantic head, the MWE is considered as compositional, when the corresponding distributional similarity measure (Jc or 197,) value is above a parameter threshold, sim.

The best performing distributional similarity measure is an.

One of the most effective similarity measures is the cosine similarity, which is a normalized dot product.

In order to appreciate the effect of these advantages, we perform an experiment that takes H to be the set of all LCs of size 1, and uses a single similarity measure .

where sim is some vector similarity measure .

We use two common similarity measures: the vector cosine metric, and the BInc (Szpektor and Dagan, 2008) similarity measure .

To do so, we use point-wise mutual information, and the conditional probabilities P(hf|hf) and POLE Similar measures have often been used for the unsupervised detection of MWEs (Villavicencio et al., 2007; Fazly and Stevenson, 2006).

Local algorithms We described 12 distributional similarity measures computed over our corpus (Section 5.1).

In addition, we obtained similarity lists learned by Lin and Pantel (2001), and replicated 3 similarity measures learned by Szpektor and Dagan (2008), over the RCV1 corpus7.

For each distributional similarity measure (altogether 16 measures), we learned a graph by inserting any edge (u, v) , when u is in the top K templates most similar to 2).

Similarity function We consider two similarity functions: The Lin (2001) similarity measure, and the Balanced Inclusion (BInc) similarity measure (Szpektor and Dagan, 2008).

They align senses in WordNet to Wikipedia entries in a supervised setting using semantic similarity measures .

Niemann and Gurevych (2011) combine two different types of similarity (i) cosine similarity on bag-of-words vectors (COS) and (ii) a personalized PageRank—based similarity measure (PPR).

For each similarity measure , Niemann and Gurevych (2011) determine a threshold (tppr and

The similarity measure is based on stem overlap of the candidates’ glosses expanded by WordNet domains, the WordNet synset, and the set of senses for a FrameNet frame.

Because the key problem of named entity disambiguation is to measure the similarity between name observations, we integrate the structural semantic relatedness in the similarity measure , so that it can better reflect the actual similarity between name observations.

The lexical relatedness lr between two WordNet concepts are measured using the Lin (l998)’s WordNet semantic similarity measure .

The problem of quantifying the relatedness between nodes in a graph is not a new problem, e.g., the structural equivalence and structural similarity (the SimRank in Jeh and Widom (2002) and the similarity measure in Leicht et al.

However, these similarity measures are not suitable for our task, because all of them assume that the edges are uniform so that they cannot take edge weight into consideration.

For both POS tagging and sentiment classification, we experimented with several alternative approaches for feature weighting, representation, and similarity measures using development data, which we randomly selected from the training instances from the datasets described in Section 5.

With respect to similarity measures, we experimented with cosine similarity and the similarity measure proposed by Lin (1998); cosine similarity performed consistently well over all the experimental settings.

The feature representation was held fixed during these similarity measure comparisons.

As an example of the distribution prediction method, in Table 3 we show the top 3 similar distributional features u in the books (source) domain, predicted for the electronics (target) domain word 21) = lightweight, by different similarity measures .

Moreover, our approach can combine two similarity measures in a hybrid hashing scheme, which is beneficial to comprehensively modeling the document similarity.

Given a query document vector q, we use the Cosine similarity measure to evaluate the similarity between q and a document a: in a dataset:

Enable a hybrid hashing scheme combining two similarity measures .

Furthermore, we make the hashing framework applicable to combine different similarity measures in NNS.

Figure 3: Precision and recall on relevant links with respect to a threshold on the similarity measure (Lin’s score)

A straightforward parameter to include to predict the relevance of a link is of course the similarity measure itself, here Lin’s information measure.

This is already a big improvement on the use of the similarity measure alone (24%).

A distributional thesaurus is a lexical network that lists semantic neighbours, computed from a corpus and a similarity measure between lexical items, which generally captures the similarity of contexts in which the items occur.

sim(h, h’) is a similarity measure between argument heads.

The similarity measure we use is based on the slot distributions of the arguments.

The similarity measure between two head words is then defined as the cosine measure of their vectors.

Siml and Sim2 respectively mean Formula 3.1 and Formula 3.2 are used in postprocessing as the similarity measure between

Sim2 achieves more performance improvement than Siml, which demonstrates the effectiveness of the similarity measure in Formula 3.2.

One simple and straightforward similarity measure is the J accard

5.2 Fractional Similarity Measures

In contrast, the linear-programming based TESLA metric allows fractional similarity measures between 0 (completely unrelated) and l (exact synonyms).

Supporting fractional similarity measures is nontrivial in the TESLA-CELAB framework.

We use several string similarity measures as features, including SoftTFIDF (Cohen et al., 2003), Level 2 JaroWinkler (Cohen et al., 2003), Fellegi-Sunter (Fellegi and Sunter, 1969), and Monge-Elkan (Monge and Elkan, 1996).

Like other approaches (Basu et al., 2004; Xing et al., 2003; Klein et al., 2002), we learn a similarity measure for clustering based on a set of must-link and cannot-link constraints.

Unlike prior work, our algorithm exploits multiple views of the data to improve the similarity measure .

We can derive such distance measures from the genre hierarchy in a way similar to word similarity measures that were invented for lexical hierarchies such as WordNet (see (Pedersen et al., 2007) for an overview).

The Leaeoek & Chodorow similarity measure (Leacock and Chodorow, 1998) normalizes the path length measure (6) by the maximum number of nodes D when traversing down from the root.

Several other similarity measures have been proposed based on the Resnik similarity such as the one by (Lin, 1998):

They are used as matching sequences to locate corresponding candidate entries in the KB, and then to disambiguate those candidates using similarity measures .

This is usually done using similarity measures (such as cosine similarity, weighted J accard distance, KL divergence...) that evaluate the distance between a bag of words related to a candidate annotation, and the words surrounding the entity to annotate in the text.

It proposes a disambiguation method that combines popularity-based priors, similarity measures , and coherence.

The Levenshtein similarity measure , on the other hand, is too restrictive and thus results in comparatively high precisions, but very low recall.

On the basis of this pseudo-parse, we compute the similarity measure sim:

The semantic constraints check whether the event descriptions of the merged node would be sufficiently consistent according to the similarity measure from Section 5.2.