Original: I NTERPRETING K NOWLEDGE G RAPH R ELATION R EPRESENTATION FROM W ORD E MBEDDINGS
Revised: I NTERPRETING K NOWLEDGE G RAPH R ELATION R EPRESENTATION FROM W ORD E MBEDDINGS

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred.
Revised: Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred.

Original: To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-speciﬁc mapping.
Revised: To predict whether a relation holds between entities, embeddings are typically compared in the latent space following a relation-speciﬁc mapping.

Original: Whilst their predictive performance has steadily improved, how such models capture the underlying latent structure of semantic information remains unexplained.
Revised: Whilst their predictive performance has steadily improved, how such models capture the underlying latent structure of semantic information remains unexplained.

Original: Building on recent theoretical understanding of word embeddings, we categorise knowledge graph relations into three types and for each derive explicit requirements of their representations.
Revised: Building on recent theoretical understanding of word embeddings, we categorise knowledge graph relations into three types and for each derive explicit requirements of their representations.

Original: We show that empirical properties of relation representations and the relative performance of leading knowledge graph representation methods are justiﬁed by our analysis
Revised: We show that empirical properties of relation representations and the relative performance of leading knowledge graph representation methods are justiﬁed by our analysis

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Knowledge graphs are large repositories of binary relations between words (or entities) in the form of (subject, relation, object) triples.
Revised: Knowledge graphs are large repositories of binary relations between words (or entities) in the form of (subject, relation, object) triples.

Original: Many models for representing entities and relations have been developed, so that known facts can be recalled and previously unknown facts can be inferred, a task known as link prediction .
Revised: Many models for representing entities and relations have been developed, so that known facts can be recalled and previously unknown facts can be inferred, a task known as link prediction .

Original: Recent link prediction models (e.g. Bordes et al., 2013; Trouillon et al., 2016;
Revised: Recent link prediction models (e.g. Bordes et al., 2013; Trouillon et al., 2016;

Original: Balaževi ´ c et al., 2019b) learn entity representations, or embeddings , of far lower dimensionality than the number of entities, by capturing latent structure in the data.
Revised: Balaževi ´ c et al., 2019b) learn entity representations, or embeddings , of far lower dimensionality than the number of entities, by capturing latent structure in the data.

Original: Relations are typically represented as a mapping from the embedding of a subject entity to those of related object entities.
Revised: Relations are typically represented as a mapping from the embedding of a subject entity to those of related object entities.

Original: Although the performance of link prediction models has steadily improved for nearly a decade, relatively little is understood of the low-rank latent structure that underpins them, which we address in this work.
Revised: Although the performance of link prediction models has steadily improved for nearly a decade, relatively little is understood of the low-rank latent structure that underpins them, which we address in this work.

Original: The outcomes of our analysis can be used to aid and direct future knowledge graph model design.
Revised: The outcomes of our analysis can be used to aid and direct future knowledge graph model design.

Original: We start by drawing a parallel between the entity embeddings of knowledge graphs and context-free word embeddings, e.g. as learned by Word2Vec (W2V) (Mikolov et al., 2013a) and GloVe (Pennington et al., 2014).
Revised: We start by drawing a parallel between the entity embeddings of knowledge graphs and context-free word embeddings, e.g. as learned by Word2Vec (W2V) (Mikolov et al., 2013a) and GloVe (Pennington et al., 2014).

Original: Our motivating premise is that the same latent word features (e.g. meaning(s), tense, grammatical type) give rise to the patterns found in different data sources, i.e. manifesting in word cooccurrence statistics and determining which words relate to which.
Revised: Our motivating premise is that the same latent word features (e.g. meaning(s), tense, grammatical type) give rise to the patterns found in different data sources, i.e. manifesting in word cooccurrence statistics and determining which words relate to which.

Original: Different embedding approaches may capture such structure in different ways, but if it is fundamentally the same, an understanding gained from one embedding task (e.g. word embedding) may beneﬁt another (e.g. knowledge graph representation).
Revised: Different embedding approaches may capture such structure in different ways, but if it is fundamentally the same, an understanding gained from one embedding task (e.g. word embedding) may beneﬁt another (e.g. knowledge graph representation).

Original: Furthermore, the relatively limited but accurate data used in knowledge graph representation differs materially from the highly abundant but statistically noisy text data used for word embeddings.
Revised: Furthermore, the relatively limited but accurate data used in knowledge graph representation differs materially from the highly abundant but statistically noisy text data used for word embeddings.

Original: As such, theoretically reconciling the two embedding methods may lead to uniﬁed and improved embeddings learned jointly from both data sources.
Revised: As such, theoretically reconciling the two embedding methods may lead to uniﬁed and improved embeddings learned jointly from both data sources.

Original: Recent work (Allen & Hospedales, 2019; Allen et al., 2019) theoretically explains how semantic properties are encoded in word embeddings that (approximately) factorise a matrix of pointwise mutual information (PMI) from word co-occurrence statistics, as known for W2V (Levy & Goldberg, 2014).
Revised: Recent work (Allen & Hospedales, 2019; Allen et al., 2019) theoretically explains how semantic properties are encoded in word embeddings that (approximately) factorise a matrix of pointwise mutual information (PMI) from word co-occurrence statistics, as known for W2V (Levy & Goldberg, 2014).

Original: Semantic relationships between words, speciﬁcally similarity, relatedness, paraphrase and analogy, are proven to manifest as linear geometric relationships between rows of the PMI matrix (subject to known error terms), of which word embeddings can be considered low-rank projections.
Revised: Semantic relationships between words, speciﬁcally similarity, relatedness, paraphrase and analogy, are proven to manifest as linear geometric relationships between rows of the PMI matrix (subject to known error terms), of which word embeddings can be considered low-rank projections.

Original: This explains, for example, the observations that similar words have similar embeddings and that embeddings of analogous word pairs share a common “vector offset” (e.g. Mikolov et al., 2013b).
Revised: This explains, for example, the observations that similar words have similar embeddings and that embeddings of analogous word pairs share a common “vector offset” (e.g. Mikolov et al., 2013b).

Original: We extend this insight to identify geometric relationships between PMI-based word embeddings that correspond to other relations, i.e. those of knowledge graphs.
Revised: We extend this insight to identify geometric relationships between PMI-based word embeddings that correspond to other relations, i.e. those of knowledge graphs.

Original: Such relation conditions deﬁne
Revised: Such relation conditions deﬁne

Original: [Table]
Revised: [Table]

Original: relation-speciﬁc mappings between entity embeddings (i
Revised: relation-speciﬁc mappings between entity embeddings (i

Original: [Table]
Revised: [Table]

Original: e. relation representations ) and so provide a “blue-print” for knowledge graph representation models.
Revised: e. relation representations ) and so provide a “blue-print” for knowledge graph representation models.

Original: Analysing the relation representations of leading knowledge graph representation models, we ﬁnd that various properties, including their relative link prediction performance, accord with predictions based on these relation conditions, supporting the premise that a common latent structure is learned by word and knowledge graph embedding models, despite the signiﬁcant differences between their training data and methodology.
Revised: Analysing the relation representations of leading knowledge graph representation models, we ﬁnd that various properties, including their relative link prediction performance, accord with predictions based on these relation conditions, supporting the premise that a common latent structure is learned by word and knowledge graph embedding models, despite the signiﬁcant differences between their training data and methodology.

Original: In summary
Revised: In summary

Original: [Table]
Revised: [Table]

Original: the key contributions of this work are:
Revised: the key contributions of this work are:

Original: • to use recent understanding of PMI-based word embeddings to derive geometric attributes of a relation representation for it to map subject word embeddings to all related object word embeddings ( relation conditions ), which partition relations into three types ; • to show that properties of knowledge graph representation models ﬁt predictions based on relation conditions, e.g. the strength of a relation’s relatedness aspect is reﬂected in the eigenvalues of its relation matrix; and • to show that both per-relation ranking as well as classiﬁcation performance of leading link prediction models corresponds to the model satisfying the appropriate relation conditions, i.e. how 
Revised: • to use recent understanding of PMI-based word embeddings to derive geometric attributes of a relation representation for it to map subject word embeddings to all related object word embeddings ( relation conditions ), which partition relations into three types ( § 3); • to show that both per-relation ranking as well as classiﬁcation performance of leading link prediction models corresponds to the model satisfying the appropriate relation conditions, i.e. how closely its relation representations match the geometric form derived theoretically ( § 4.1); and • to show that properties of knowledge graph representation models ﬁt predictions based on relation conditions, e.g. the strength of a relation’s

Original: closely its relation representations match the geometric form derived theoretically.
Revised: relatedness aspect is reﬂected in the eigenvalues of its relation matrix ( § 4.2).

Original:  B ACKGROUND
Revised:  B ACKGROUND

Original: Knowledge graph representation:
Revised: Knowledge graph representation:

Original: Recent knowledge graph models typically represent entities e s , e o as vectors e s , e o ∈ R d e , and relations as transformations in the latent space from subject to object entity embedding, where the dimension d e is far lower (e.g. 200 ) than the number of entities n e (e.g. > 10 4 ).
Revised: Recent knowledge graph models typically represent entities e s , e o as vectors e s , e o ∈ R d e , and relations as transformations in the latent space from subject to object entity embedding, where the dimension d e is far lower (e.g. 200 ) than the number of entities n e (e.g. > 10 4 ).

Original: Such models are distinguished by their score function , which deﬁnes (i) the form of the relation transformation, e.g. matrix multiplication and/or vector addition; and (ii) the measure of proximity between a transformed subject embedding and an object embedding, e.g. dot product or Euclidean distance.
Revised: Such models are distinguished by their score function , which deﬁnes (i) the form of the relation transformation, e.g. matrix multiplication and/or vector addition; and (ii) the measure of proximity between a transformed subject embedding and an object embedding, e.g. dot product or Euclidean distance.

Original: Score functions can be non-linear (e.g. Dettmers et al., 2018), or linear and sub-categorised as additive , multiplicative or both.
Revised: Score functions can be non-linear (e.g. Dettmers et al., 2018), or linear and sub-categorised as additive , multiplicative or both.

Original: We focus on linear models due to their simplicity and strong performance at link prediction (including state-of-the-art).
Revised: We focus on linear models due to their simplicity and strong performance at link prediction (including state-of-the-art).

Original: Table 1 shows the score functions of competitive linear knowledge graph embedding models spanning the sub-categories:
Revised: Table 1 shows the score functions of competitive linear knowledge graph embedding models spanning the sub-categories:

Original: TransE (Bordes et al., 2013), DistMult (Yang et al., 2015), TuckER (Balaževi ´ c et al., 2019b) and
Revised: TransE (Bordes et al., 2013), DistMult (Yang et al., 2015), TuckER (Balaževi ´ c et al., 2019b) and

Original: MuRE (Balaževi´c et al., 2019a).
Revised: MuRE (Balaževi´c et al., 2019a).

Original: Additive models apply a relation-speciﬁc translation to a subject entity embedding and typically use
Revised: Additive models apply a relation-speciﬁc translation to a subject entity embedding and typically use

Original: Euclidean distance to evaluate proximity to object embeddings.
Revised: Euclidean distance to evaluate proximity to object embeddings.

Original: A generic additive score function is given by φ ( e s , r, e o )= −(cid:107)
Revised: A generic additive score function is given by φ ( e s , r, e o )= −(cid:107)

Original: e s + r − e o (cid:107) 

Revised: e s + r − e o (cid:107)

Original: + b s + b o .
Revised: + b s + b o .

Original: A simple example is TransE, where b s =
Revised: A simple example is TransE, where b s =

Original: b o =0 .
Revised: b o =0 .

Original: Multiplicative models have the generic score function φ ( e s , r, e o )=
Revised: Multiplicative models have the generic score function φ ( e s , r, e o )=

Original: e (cid:62) s Re o , i.e. a bilinear product of the entity embeddings and a relation-speciﬁc matrix R .
Revised: e (cid:62) s Re o , i.e. a bilinear product of the entity embeddings and a relation-speciﬁc matrix R .

Original: DistMult is a simple example with R diagonal and so cannot model asymmetric relations (Trouillon et al., 2016).
Revised: DistMult is a simple example with R diagonal and so cannot model asymmetric relations (Trouillon et al., 2016).

Original: In TuckER, each relation-speciﬁc
Revised: In TuckER, each relation-speciﬁc

Original: R = W × 3 r is a linear combination of d r “prototype” relation matrices in a core tensor W ∈ R d e × d r × d e ( × n denoting tensor product along mode n ), facilitating multi-task learning across relations.
Revised: R = W × 3 r is a linear combination of d r “prototype” relation matrices in a core tensor W ∈ R d e × d r × d e ( × n denoting tensor product along mode n ), facilitating multi-task learning across relations.

Original: Some models, e.g. MuRE, combine both multiplicative ( R ) and additive ( r ) components.
Revised: Some models, e.g. MuRE, combine both multiplicative ( R ) and additive ( r ) components.

Original: Word embedding: Algorithms such as Word2Vec (Mikolov et al., 2013a) and GloVe (Pennington et al., 2014) generate low-dimensional word embeddings that perform well on downstream tasks (Baroni et al., 2014).
Revised: Word embedding: Algorithms such as Word2Vec (Mikolov et al., 2013a) and GloVe (Pennington et al., 2014) generate low-dimensional word embeddings that perform well on downstream tasks (Baroni et al., 2014).

Original: Such models predict the context words ( c j ) observed around a target word ( w i ) in a text corpus using shallow neural networks.
Revised: Such models predict the context words ( c j ) observed around a target word ( w i ) in a text corpus using shallow neural networks.

Original: Whilst recent language models (e.g. Devlin et al., 2018; Peters et al., 2018) achieve strong performance using contextualised word embeddings, we focus on “context-free” embeddings since knowledge graph entities have no obvious context and, importantly, they offer insight into embedding interpretability.
Revised: Whilst recent language models (e.g. Devlin et al., 2018; Peters et al., 2018) achieve strong performance using contextualised word embeddings, we focus on “context-free” embeddings since knowledge graph entities have no obvious context and, importantly, they offer insight into embedding interpretability.

Original: Levy & Goldberg (2014) show that, for a dictionary of n e unique words and embedding dimension d e (cid:28) n e , W2V’s loss function is minimised when its embeddings w i ,
Revised: Levy & Goldberg (2014) show that, for a dictionary of n e unique words and embedding dimension d e (cid:28) n e , W2V’s loss function is minimised when its embeddings w i ,

Original: c
Revised: c

Original: j form matrices W , C ∈ R d e × n e that factorise a pointwise mutual information (PMI) matrix of word co-occurrence statistics (PMI ( w i , c j )=log P ( w i ,c j ) P ( w i ) P ( c j ) ), subject to a shift term.
Revised: j form matrices W , C ∈ R d e × n e that factorise a pointwise mutual information (PMI) matrix of word co-occurrence statistics (PMI ( w i , c j )=log P ( w i ,c j ) P ( w i ) P ( c j ) ), subject to a shift term.

Original: This result relates W2V to earlier count-based embeddings and speciﬁcally PMI, which has a history in linguistic analysis (Turney & Pantel, 2010).
Revised: This result relates W2V to earlier count-based embeddings and speciﬁcally PMI, which has a history in linguistic analysis (Turney & Pantel, 2010).

Original: From its loss function, GloVe can be seen to perform a related factorisation.
Revised: From its loss function, GloVe can be seen to perform a related factorisation.

Original: Recent work (Allen & Hospedales, 2019; Allen et al., 2019) shows how the semantic relationships of similarity , relatedness , paraphrase and analogy are encoded in PMI-based word embeddings by recognising such embeddings as low-rank projections of high dimensional rows of the PMI matrix, termed PMI vectors .
Revised: Recent work (Allen & Hospedales, 2019; Allen et al., 2019) shows how the semantic relationships of similarity , relatedness , paraphrase and analogy are encoded in PMI-based word embeddings by recognising such embeddings as low-rank projections of high dimensional rows of the PMI matrix, termed PMI vectors .

Original: Those semantic relationships are described in terms of multiplicative interactions between co-occurrence probabilities (subject to deﬁned error terms), that correspond to additive interactions between (logarithmic) PMI statistics, and hence PMI vectors.
Revised: Those semantic relationships are described in terms of multiplicative interactions between co-occurrence probabilities (subject to deﬁned error terms), that correspond to additive interactions between (logarithmic) PMI statistics, and hence PMI vectors.

Original: Thus, under a sufﬁciently linear projection, those semantic relationships correspond to linear relationships between word embeddings.
Revised: Thus, under a sufﬁciently linear projection, those semantic relationships correspond to linear relationships between word embeddings.

Original: Note that although the relative geometry reﬂecting semantic relationships is preserved, the direct interpretability of dimensions, as in PMI vectors, is lost since the embedding matrices can be arbitrarily scaled/rotated if the other is inversely transformed.
Revised: Note that although the relative geometry reﬂecting semantic relationships is preserved, the direct interpretability of dimensions, as in PMI vectors, is lost since the embedding matrices can be arbitrarily scaled/rotated if the other is inversely transformed.

Original: We state the relevant semantic relationships on which we build, denoting the set of unique dictionary words by E :
Revised: We state the relevant semantic relationships on which we build, denoting the set of unique dictionary words by E :

Original: • Paraphrase : word subsets W , W ∗ ⊆E are said to paraphrase if they induce similar distributions over nearby words, i.e. p ( E|W )
Revised: • Paraphrase : word subsets W , W ∗ ⊆E are said to paraphrase if they induce similar distributions over nearby words, i.e. p ( E|W )

Original: ≈ p ( E|W ∗ ) , e.g. { king } paraphrases { man, royal } .
Revised: ≈ p ( E|W ∗ ) , e.g. { king } paraphrases { man, royal } .

Original: • Analogy : a common example
Revised: • Analogy : a common example

Original: of an analogy is “ woman is to queen as man is to king ” and can be deﬁned as any set of word pairs { ( w i , w ∗ i ) } i ∈I for which it is semantically meaningful to say “ w a is to w ∗ a as w b is to w ∗ b ” ∀ a, b ∈I .
Revised: of an analogy is “ woman is to queen as man is to king ” and can be deﬁned as any set of word pairs { ( w i , w ∗ i ) } i ∈I for which it is semantically meaningful to say “ w a is to w ∗ a as w b is to w ∗ b ” ∀ a, b ∈I .

Original: Where one word subset paraphrases another, the sums of their embeddings are shown to be equal (subject to the independence of words within each set), e.g. w king ≈ w man + w royal .
Revised: Where one word subset paraphrases another, the sums of their embeddings are shown to be equal (subject to the independence of words within each set), e.g. w king ≈ w man + w royal .

Original: An interesting connection is established between the two semantic relationships: a set of word pairs
Revised: An interesting connection is established between the two semantic relationships: a set of word pairs

Original: A = { ( w a , w ∗ a ) , ( w b , w ∗ b ) } is an analogy if { w a , w ∗ b } paraphrases { w ∗ a , w b } , in which case the embeddings satisfy w a ∗ − w a ≈ w b ∗ − w b (“vector offset”).
Revised: A = { ( w a , w ∗ a ) , ( w b , w ∗ b ) } is an analogy if { w a , w ∗ b } paraphrases { w ∗ a , w b } , in which case the embeddings satisfy w a ∗ − w a ≈ w b ∗ − w b (“vector offset”).

Original:  F ROM ANALOGIES TO KNOWLEDGE GRAPH RELATIONS
Revised:  F ROM ANALOGIES TO KNOWLEDGE GRAPH RELATIONS

Original: Analogies from the ﬁeld of word embeddings are our starting point for developing a theoretical basis for representing knowledge graph relations.
Revised: Analogies from the ﬁeld of word embeddings are our starting point for developing a theoretical basis for representing knowledge graph relations.

Original: The relevance of analogies stems from the observation that for an analogy to hold (see § 2), its word pairs, e.g { (man, king) , (woman, queen) , (girl, princess) }, must be related in the same way, comparably to subject-object entity pairs under a common knowledge graph relation.
Revised: The relevance of analogies stems from the observation that for an analogy to hold (see § 2), its word pairs, e.g { (man, king) , (woman, queen) , (girl, princess) }, must be related in the same way, comparably to subject-object entity pairs under a common knowledge graph relation.

Original: Our aim is to develop the understanding of PMI-based word embeddings (henceforth word embeddings ), to identify the mathematical properties necessary for a relation representation to map subject word embeddings to all related object word embeddings.
Revised: Our aim is to develop the understanding of PMI-based word embeddings (henceforth word embeddings ), to identify the mathematical properties necessary for a relation representation to map subject word embeddings to all related object word embeddings.

Original: Considering the paraphrasing word sets { king } and { man , royal } corresponding to the word embedding relationship w king ≈ w man + w royal ( § 2), royal can be interpreted as the semantic difference between man and king , ﬁtting intuitively with the relationship w royal ≈ w king − w man .
Revised: Considering the paraphrasing word sets { king } and { man , royal } corresponding to the word embedding relationship w king ≈ w man + w royal ( § 2), royal can be interpreted as the semantic difference between man and king , ﬁtting intuitively with the relationship w royal ≈ w king − w man .

Original: Fundamentally, this relationship holds because the difference between words that co-occur (i.e. occur more frequently than if independent) with king and those that co-occur with man , reﬂects those words that co-occur with royal .
Revised: Fundamentally, this relationship holds because the difference between words that co-occur (i.e. occur more frequently than if independent) with king and those that co-occur with man , reﬂects those words that co-occur with royal .

Original: We refer to this difference in co-occurrence distribution as a “context shift”, from man (subject) to king (object).
Revised: We refer to this difference in co-occurrence distribution as a “context shift”, from man (subject) to king (object).

Original: Allen & Hospedales (2019) effectively show that where multiple word pairs share a common context shift, they form an analogy whose embeddings satisfy the vector offset relationship.
Revised: Allen & Hospedales (2019) effectively show that where multiple word pairs share a common context shift, they form an analogy whose embeddings satisfy the vector offset relationship.

Original: This result seems obvious where the context shift mirrors an identiﬁable word, the embedding of which is approximated by the common vector offset, e.g. queen and woman are related by the same context shift, i.e. w queen ≈ w woman + w royal , thus w queen − w woman ≈ w king − w man .
Revised: This result seems obvious where the context shift mirrors an identiﬁable word, the embedding of which is approximated by the common vector offset, e.g. queen and woman are related by the same context shift, i.e. w queen ≈ w woman + w royal , thus w queen − w woman ≈ w king − w man .

Original: However, the same result holds, i.e. an analogy is formed with a common vector offset between embeddings, for an arbitrary (common) context shift that may reﬂect no particular word.
Revised: However, the same result holds, i.e. an analogy is formed with a common vector offset between embeddings, for an arbitrary (common) context shift that may reﬂect no particular word.

Original: Importantly, these context shift relations evidence a case in which it is known how a relation can be represented, i.e. by an additive vector (comparable to TransE) if entities are represented by word embeddings.
Revised: Importantly, these context shift relations evidence a case in which it is known how a relation can be represented, i.e. by an additive vector (comparable to TransE) if entities are represented by word embeddings.

Original: More generally, this provides an interpretable foothold into relation representation.
Revised: More generally, this provides an interpretable foothold into relation representation.

Original: Note that not all sets of word pairs considered analogies exhibit a clear context shift relation, e.g. in the analogy {( car , engine ), ( bus , seats )}, the difference between words co-occurring with engine and car is not expected to reﬂect the corresponding difference between bus and seats .
Revised: Note that not all sets of word pairs considered analogies exhibit a clear context shift relation, e.g. in the analogy {( car , engine ), ( bus , seats )}, the difference between words co-occurring with engine and car is not expected to reﬂect the corresponding difference between bus and seats .

Original: This illustrates how
Revised: This illustrates how

Original: [Figure]
Revised: [Figure]

Original: analogies are a loosely deﬁned concept, e.g. their implicit relation may be semantic or syntactic, with several sub-categories of each (e.g. see Gladkova et al.
Revised: analogies are a loosely deﬁned concept, e.g. their implicit relation may be semantic or syntactic, with several sub-categories of each (e.g. see Gladkova et al.

Original: The same is readily observed for the relations of knowledge graphs.
Revised: The same is readily observed for the relations of knowledge graphs.

Original: This likely explains the observed variability in “solving” analogies by use of vector offset (e.g. Köper et al., 2015; Karpinska et al., 2018; Gladkova et al., 2016) and suggests that further consideration is required to represent relations (or solve analogies) in general.
Revised: This likely explains the observed variability in “solving” analogies by use of vector offset (e.g. Köper et al., 2015; Karpinska et al., 2018; Gladkova et al., 2016) and suggests that further consideration is required to represent relations (or solve analogies) in general.

Original: We have seen that the existence of a context shift relation between a subject and object word implies a (relation-speciﬁc) geometric relationship between word embeddings, thus the latter provides a necessary condition for the relation to hold .
Revised: We have seen that the existence of a context shift relation between a subject and object word implies a (relation-speciﬁc) geometric relationship between word embeddings, thus the latter provides a necessary condition for the relation to hold .

Original: We refer to this as a “relation condition” and aim to identify relation conditions for other classes of relation.
Revised: We refer to this as a “relation condition” and aim to identify relation conditions for other classes of relation.

Original: Once identiﬁed, relation conditions deﬁne a mapping from subject embeddings to all related object embeddings, by which related entities might be identiﬁed with a proximity measure (e.g. Euclidean distance or dot product).
Revised: Once identiﬁed, relation conditions deﬁne a mapping from subject embeddings to all related object embeddings, by which related entities might be identiﬁed with a proximity measure (e.g. Euclidean distance or dot product).

Original: This is the precise aim of a knowledge graph representation model, but loss functions are typically developed heuristically.
Revised: This is the precise aim of a knowledge graph representation model, but loss functions are typically developed heuristically.

Original: Given the existence of many representation models, we can verify identiﬁed relation conditions by contrasting the per-relation performance of various models with the extent to which their loss function reﬂects the appropriate relation conditions.
Revised: Given the existence of many representation models, we can verify identiﬁed relation conditions by contrasting the per-relation performance of various models with the extent to which their loss function reﬂects the appropriate relation conditions.

Original: Note that since relation conditions are necessary rather than sufﬁcient, they do not guarantee a relation holds, i.e. false positives may arise.
Revised: Note that since relation conditions are necessary rather than sufﬁcient, they do not guarantee a relation holds, i.e. false positives may arise.

Original: Whilst we seek to establish relation conditions based on PMI word embeddings, the data used to train knowledge graph embeddings differs signiﬁcantly to the text data used by word embeddings , and the relevance of conditions ultimately based on PMI statistics may seem questionable.
Revised: Whilst we seek to establish relation conditions based on PMI word embeddings, the data used to train knowledge graph embeddings differs signiﬁcantly to the text data used by word embeddings, and the relevance of conditions ultimately based on PMI statistics may seem questionable.

Original: However, where a knowledge graph representation model implements relation conditions and measures proximity between embeddings, the parameters of word embeddings necessarily provide a potential solution that minimises the loss function.
Revised: However, where a knowledge graph representation model implements relation conditions and measures proximity between embeddings, the parameters of word embeddings necessarily provide a potential solution that minimises the loss function.

Original: Many equivalent solutions may exist due to symmetry as typical for neural network architectures.
Revised: Many equivalent solutions may exist due to symmetry as typical for neural network architectures.

Original: We now deﬁne relation types and identify their relation conditions (underlined); we then consider the completeness of this categorisation.
Revised: We now deﬁne relation types and identify their relation conditions (underlined); we then consider the completeness of this categorisation.

Original: • Similarity: Semantically similar words induce similar distributions over the words they co-occur with.
Revised: • Similarity: Semantically similar words induce similar distributions over the words they co-occur with.

Original: Thus their PMI vectors and word embeddings are similar (Fig 1a).
Revised: Thus their PMI vectors and word embeddings are similar (Fig 1a).

Original: • Relatedness: The relatedness of two words can be considered in terms of the words S ⊆E with which both co-occur similarly.
Revised: • Relatedness: The relatedness of two words can be considered in terms of the words S ⊆E with which both co-occur similarly.

Original: S deﬁnes the nature of relatedness, e.g. milk and cheese are related by S = { dairy , breakfast , ... } ; and |S| reﬂects the strength of relatedness.
Revised: S deﬁnes the nature of relatedness, e.g. milk and cheese are related by S = { dairy , breakfast , ... } ; and |S| reﬂects the strength of relatedness.

Original: Since PMI vector components corresponding to S are similar (Fig 1b), embeddings of S -related words have similar components in the subspace V S that spans the projected PMI vector dimensions corresponding to S .
Revised: Since PMI vector components corresponding to S are similar (Fig 1b), embeddings of S -related words have similar components in the subspace V S that spans the projected PMI vector dimensions corresponding to S .

Original: The rank of V S is thus anticipated to reﬂect relatedness strength.
Revised: The rank of V S is thus anticipated to reﬂect relatedness strength.

Original: Relatedness can be seen as a weaker and more variable generlisation of similarity, its limiting case where S = E , hence rank( V S )=
Revised: Relatedness can be seen as a weaker and more variable generalisation of similarity, its limiting case where S = E , hence rank( V S )=

Original: d e .
Revised: d e .

Original: • Context-shift: As discussed above, words related by a common difference between their distributions of co-occurring words, deﬁned as context-shifts , share a common vector offset between word embeddings.
Revised: • Context-shift: As discussed above, words related by a common difference between their distributions of co-occurring words, deﬁned as context-shifts , share a common vector offset between word embeddings.

Original: Context might be considered added (e.g. man to king , termed a specialisation , Fig 1c), subtracted (e.g. king to man ) or both (Fig 1d).
Revised: Context might be considered added (e.g. man to king ), termed a specialisation (Fig 1c), subtracted (e.g. king to man ) or both (Fig 1d).

Original: These relations are 1-to-1 (subject to synonyms) and include an aspect of relatedness due to the word associations in common.
Revised: These relations are 1-to-1 (subject to synonyms) and include an aspect of relatedness due to the word associations in common.

Revised: Note that, specialisations include hyponyms/hypernyms and context shifts include meronyms.

Original: • Generalised context-shift: Context-shift relations generalise to 1-to-many, many-to-1 and manyto-many relations where the added/subtracted context may be from a (relation-speciﬁc) context set (Fig 1e), e.g. any city or anything bigger.
Revised: • Generalised context-shift: Context-shift relations generalise to 1-to-many, many-to-1 and manyto-many relations where the added/subtracted context may be from a (relation-speciﬁc) context set (Fig 1e), e.g. any city or anything bigger.

Original: The potential scope and size of context sets adds variability to these relations.
Revised: The potential scope and size of context sets adds variability to these relations.

Original: The limiting case in which the context set is “small” reduces to a 1-tocontext-shift (above) and the embedding difference is a known vector offset.
Revised: The limiting case in which the context set is “small” reduces to a 1-tocontext-shift (above) and the embedding difference is a known vector offset.

Original: In the limiting case of a “large” context set, the added/subtracted context is essentially unrestricted such that only the relatedness aspect of the relation, and thus a common subspace component of embeddings, is ﬁxed.
Revised: In the limiting case of a “large” context set, the added/subtracted context is essentially unrestricted such that only the relatedness aspect of the relation, and thus a common subspace component of embeddings, is ﬁxed.

Original: Categorisation completeness: Taking intuition from Fig 1 and considering PMI vectors as sets of word features , these relation types can be interpreted as set operations: similarity as set equality; relatedness as subset equality; and context-shift as a relation-speciﬁc set difference.
Revised: Categorisation completeness: Taking intuition from Fig 1 and considering PMI vectors as sets of word features , these relation types can be interpreted as set operations: similarity as set equality; relatedness as subset equality; and context-shift as a relation-speciﬁc set difference.

Original: Since for any relation each feature must either remain unchanged (relatedness), change (context shift) or else be irrelevant, we conjecture that the above relation types give a complete partition of semantic relations
Revised: Since for any relation each feature must either remain unchanged (relatedness), change (context shift) or else be irrelevant, we conjecture that the above relation types give a complete partition of semantic relations

Original: [Table] [Table]
Revised: [Table] [Table]

Original: 3. C ATEGORISING R EAL K NOWLEDGE G RAPH R ELATIONS
Revised: 3. C ATEGORISING R EAL K NOWLEDGE G RAPH R ELATIONS

Original: Analysing the relations of popular knowledge graph datasets, we observe that they indeed imply (i) a relatedness aspect reﬂecting a common theme (e.g. both entities are animals or geographic terms); and (ii) contextual themes speciﬁc to the subject and/or object entities.
Revised: Analysing the relations of popular knowledge graph datasets, we observe that they indeed imply (i) a relatedness aspect reﬂecting a common theme (e.g. both entities are animals or geographic terms); and (ii) contextual themes speciﬁc to the subject and/or object entities.

Original: Further, relations fall under a hierarchy of three relation types : highly related ( R ); generalised specialisation ( S ); and generalised context-shift ( C ).
Revised: Further, relations fall under a hierarchy of three relation types : highly related ( R ); generalised specialisation ( S ); and generalised context-shift ( C ).

Original: As above, “generalised” indicates that context differences are not restricted to be 1-1.
Revised: As above, “generalised” indicates that context differences are not restricted to be 1-1.

Original: From Fig 1, it can be seen that type R relations are a special case of S, which are a special case of
Revised: From Fig 1, it can be seen that type R relations are a special case of S, which are a special case of

Original: C. Thus type C encompasses all considered relations.
Revised: C. Thus type C encompasses all considered relations.

Original: Whilst there are many ways to classify relations, e.g. by hierarchy, transitivity, the proposed relation conditions delineate relations by the required mathematical form (and complexity) of their representation.
Revised: Whilst there are many ways to classify relations, e.g. by hierarchy, transitivity, the proposed relation conditions delineate relations by the required mathematical form (and complexity) of their representation.

Original: Table 2 shows a categorisation of the relations of the WN18RR dataset (Dettmers et al., 2018) comprising 11 relations and 40,943 entities.
Revised: Table 2 shows a categorisation of the relations of the WN18RR dataset (Dettmers et al., 2018) comprising 11 relations and 40,943 entities.

Original: An explanation for the category assignment is in Appx.
Revised: An explanation for the category assignment is in Appx.

Original: A. Analysing the commonly used FB15kdataset (Toutanova et al., 2015) reveals relations to be almost exclusively of type C, precluding a contrast of performance per relation type and hence that dataset is omitted from our analysis.
Revised: A. Analysing the commonly used FB15kdataset (Toutanova et al., 2015) reveals relations to be almost exclusively of type C, precluding a contrast of performance per relation type and hence that dataset is omitted from our analysis.

Original: Instead, we categorise a random subsample of 12 relations from the NELL-995 dataset (Xiong et al., 2017), containing 75,492 entities and 200 relations (see Tables 8 and 9 in Appx.
Revised: Instead, we categorise a random subsample of 12 relations from the NELL-995 dataset (Xiong et al., 2017), containing 75,492 entities and 200 relations (see Tables 8 and 9 in Appx.

Original: B).
Revised: B).

Original: 3. R ELATIONS AS MAPPINGS BETWEEN EMBEDDINGS
Revised: 3. R ELATIONS AS MAPPINGS BETWEEN EMBEDDINGS

Original: Given the relation conditions of a relation type, we now consider mappings that satisfy them and thereby loss functions able to identify relations of each type, evaluating proximity between mapped entity embeddings by dot product or Euclidean distance.
Revised: Given the relation conditions of a relation type, we now consider mappings that satisfy them and thereby loss functions able to identify relations of each type, evaluating proximity between mapped entity embeddings by dot product or Euclidean distance.

Original: We then contrast our theoretically derived loss functions, speciﬁc to a relation type, with those of several knowledge graph models (Table 1) to predict identiﬁable properties and the relative performance of different knowledge graph models for each relation type.
Revised: We then contrast our theoretically derived loss functions, speciﬁc to a relation type, with those of several knowledge graph models (Table 1) to predict identiﬁable properties and the relative performance of different knowledge graph models for each relation type.

Original: R: Identifying S -relatedness requires testing both entity embeddings e s , e o for a common subspace component V S , which can be achieved by projecting both embeddings onto V S and comparing their images.
Revised: R: Identifying S -relatedness requires testing both entity embeddings e s , e o for a common subspace component V S , which can be achieved by projecting both embeddings onto V S and comparing their images.

Original: Projection requires multiplication by a matrix
Revised: Projection requires multiplication by a matrix

Original: P r ∈ R d × d and cannot be achieved additively, except in the trivial limiting case of similarity ( P r = I ) when r ≈ 0 can be added.
Revised: P r ∈ R d × d and cannot be achieved additively, except in the trivial limiting case of similarity ( P r = I ) when r ≈ 0 can be added.

Original: Comparison by dot product gives ( P r e s ) (cid:62) ( P r e o )=
Revised: Comparison by dot product gives ( P r e s ) (cid:62) ( P r e o )=

Original: e (cid:62) s P (cid:62) r P r e o = e (cid:62) s M r e o (for relation-speciﬁc symmetric M r = P (cid:62) r P r ).
Revised: e (cid:62) s P (cid:62) r P r e o = e (cid:62) s M r e o (for relation-speciﬁc symmetric M r = P (cid:62) r P r ).

Original: Euclidean distance gives (cid:107) P r e s − P r e o (cid:107) 2 = ( e s − e o ) (cid:62) M r ( e s − e o )
Revised: Euclidean distance gives (cid:107) P r e s − P r e o (cid:107) 2 = ( e s − e o ) (cid:62) M r ( e s − e o )

Original: = (cid:107) P r e s (cid:107) 2 − 2 e (cid:62) s M
Revised: = (cid:107) P r e s (cid:107) 2 − 2 e (cid:62) s M

Original: r e o
Revised: r e o

Original: + (cid:107) P r e o (cid:107) 2 .
Revised: + (cid:107) P r e o (cid:107) 2 .

Original: S/C:
Revised: S/C:

Original: The relation conditions require testing for both S -relatedness and relation-speciﬁc entity component(s) ( v sr , v or ).
Revised: The relation conditions require testing for both S -relatedness and relation-speciﬁc entity component(s) ( v sr , v or ).

Original: This is achieved by (i) multiplying both entity embeddings by a relationspeciﬁc projection matrix P r that projects onto the subspace that spans the low-rank projection of dimensions corresponding to S , v sr and v or (i.e. testing for S -relatedness while preserving relationspeciﬁc entity components); and (ii) adding a relation-speciﬁc vector r = v or − v sr to the transformed subject entity embeddings.
Revised: This is achieved by (i) multiplying both entity embeddings by a relationspeciﬁc projection matrix P r that projects onto the subspace that spans the low-rank projection of dimensions corresponding to S , v sr and v or (i.e. testing for S -relatedness while preserving relationspeciﬁc entity components); and (ii) adding a relation-speciﬁc vector r = v or − v sr to the transformed subject entity embeddings.

Original: predict.
Revised: predict.

Original: Comparing the transformed entity embeddings by dot product equates to ( P r e s + r ) (cid:62) P r e o ; and by
Revised: Comparing the transformed entity embeddings by dot product equates to ( P r e s + r ) (cid:62) P r e o ; and by

Original: Euclidean distance to (cid:107) P r e s + r − P r e o (cid:107) 

Revised: Euclidean distance to (cid:107) P r e s + r − P r e o (cid:107)

Original: = (cid:107) P r e s + r (cid:107) 

Revised: = (cid:107) P r e s + r (cid:107)

Original: − 2( P r e s + r ) (cid:62) P r e o
Revised: − 2( P r e s + r ) (cid:62) P r e o

Original: + (cid:107) P r e o (cid:107) 2 ( cf
Revised: + (cid:107) P r e o (cid:107) 2 ( cf

Original: MuRE: (cid:107) Re s + r − e
Revised: MuRE: (cid:107) Re s + r − e

Original: o
Revised: o

Original: (cid:107) 2 ).
Revised: (cid:107) 2 ).

Original: Contrasting these theoretically derived loss functions with those of knowledge graph models (Table 1), we make the following predictions:
Revised: Contrasting these theoretically derived loss functions with those of knowledge graph models (Table 1), we make the following predictions:

Original: P1: The ability to learn the representation of a relation is expected to reﬂect: (a) the complexity of its type (R < S < C) independently of model choice; and (b) whether relation conditions (e
Revised: P1: The ability to learn the representation of a relation is expected to reﬂect: (a) the complexity of its type (R < S < C) independently of model choice; and (b) whether relation conditions (e

Original: g. additive/multiplicative interactions) are met by the model.
Revised: g. additive/multiplicative interactions) are met by the model.

Original: P2: Knowledge graph relation representations reﬂect the following type-speciﬁc properties: (a) relation matrices for relatedness (type R) relations are highly symmetric; (b) offset vectors for relatedness relations have low norm; and (c) as a proxy to the rank of V S , the eigenvalues of a relation matrix reﬂect relatedness strength.
Revised: P2: Knowledge graph relation representations reﬂect the following type-speciﬁc properties: (a) relation matrices for relatedness (type R) relations are highly symmetric; (b) offset vectors for relatedness relations have low norm; and (c) as a proxy to the rank of V S , the eigenvalues of a relation matrix reﬂect relatedness strength.

Original: To elaborate, our core prediction P1(b) anticipates that: (i) additive-only models (e.g. TransE) are not suited to identifying the relatedness aspect of relations, except in limiting cases of similarity, requiring a zero vector); (ii) multiplicative-only models (e.g. DistMult) should perform well on type R relations, but are not suited to identifying entity-speciﬁc features of type S/C (an asymmetric relation matrix in TuckER may help compensate); and (iii) the loss function of MuRE closely resembles that derived for type C relations, which generalise all others, and is thus expected to perform best overall.
Revised: To elaborate, our core prediction P1(b) anticipates that: (i) additive-only models (e.g. TransE) are not suited to identifying the relatedness aspect of relations, except in limiting cases of similarity, requiring a zero vector); (ii) multiplicative-only models (e.g. DistMult) should perform well on type R relations, but are not suited to identifying entity-speciﬁc features of type S/C (an asymmetric relation matrix in TuckER may help compensate); and (iii) the loss function of MuRE closely resembles that derived for type C relations, which generalise all others, and is thus expected to perform best overall.

Original:  E VIDENCE LINKING KNOWLEDGE GRAPH AND WORD EMBEDDINGS
Revised:  E VIDENCE LINKING KNOWLEDGE GRAPH AND WORD EMBEDDINGS

Original: We test whether the predictions P1 and P2, made on the basis of word embeddings, apply to knowledge graph relations by analysing the performance and properties of competitive knowledge graph models.
Revised: We test whether the predictions P1 and P2, made on the basis of word embeddings, apply to knowledge graph relations by analysing the performance and properties of competitive knowledge graph models.

Original: We compare TransE, DistMult, TuckER and MuRE, which entail different forms of relation representation, on all WN18RR relations and a similar number of NELL-995 relations (spanning all relation types).
Revised: We compare TransE, DistMult, TuckER and MuRE, which entail different forms of relation representation, on all WN18RR relations and a similar number of NELL-995 relations (spanning all relation types).

Original: All models have a comparable number of free parameters.
Revised: All models have a comparable number of free parameters.

Original: Since for TransE, the logistic sigmoid cannot be applied to the score function to give a probabilistic interpretation comparable to other models, for fair comparison we include MuRE I , a constrained variant of MuRE with R s =
Revised: Since for TransE, the logistic sigmoid cannot be applied to the score function to give a probabilistic interpretation comparable to other models, for fair comparison we include MuRE I , a constrained variant of MuRE with R s =

Original: R o
Revised: R o

Original: =
Revised: =

Original: I , as a proxy to TransE. Implementation details are included in
Revised: I , as a proxy to TransE. Implementation details are included in

Original: Appx.
Revised: Appx.

Original: D. For evaluation, we generate 2 n e evaluation triples for each test triple ( n e = |E|
Revised: D. For evaluation, we generate 2 n e evaluation triples for each test triple ( n e = |E|

Original: denoting the number of entities) by ﬁxing the subject entity e s and relation r and replacing the object entity e o with each entity in turn and then keeping e o and r ﬁxed and varying e s .
Revised: denoting the number of entities) by ﬁxing the subject entity e s and relation r and replacing the object entity e o with each entity in turn and then keeping e o and r ﬁxed and varying e s .

Original: Each model’s scores for the evaluation triples are ranked to give the standard metric Hits@10 (Bordes et al., 2013), i.e. the fraction of times a true triple appears in the top 10 ranked evaluation triples.
Revised: Each model’s scores for the evaluation triples are ranked to give the standard metric Hits@10 (Bordes et al., 2013), i.e. the fraction of times a true triple appears in the top 10 ranked evaluation triples.

Original: 4. P1: J USTIFYING THE RELATIVE PERFORMANCE OF KNOWLEDGE GRAPH MODELS
Revised: 4. P1: J USTIFYING THE RELATIVE PERFORMANCE OF KNOWLEDGE GRAPH MODELS

Original: Ranking performance: Tables 3 and 4 report Hits@10 for each relation and include the relation type as well as known confounding inﬂuences: percentage of relation instances in the training and test sets (approximately equal), the actual number of instances in the test set (causing some results to be highly granular), Krackhardt hierarchy score (see Appx.
Revised: Ranking performance: Tables 3 and 4 report Hits@10 for each relation and include the relation type as well as known confounding inﬂuences: percentage of relation instances in the training and test sets (approximately equal), the actual number of instances in the test set (causing some results to be highly granular), Krackhardt hierarchy score (see Appx.

Original: E)
Revised: E)

Original: (Krackhardt, 2014; Balaževi´c et al., 2019a) and maximum and average shortest path between any two related nodes.
Revised: (Krackhardt, 2014; Balaževi´c et al., 2019a) and maximum and average shortest path between any two related nodes.

Original: A further confounding effect is dependence between relations: Lacroix et al.
Revised: A further confounding effect is dependence between relations: Lacroix et al.

Original: and Balaževi´c et al.
Revised: and Balaževi´c et al.

Original: (2019b) independently show that constraining the rank of relation representations is beneﬁcial for datasets with many relations due to multi-task learning , particularly when the number of instances per relation is low.
Revised: (2019b) independently show that constraining the rank of relation representations is beneﬁcial for datasets with many relations due to multi-task learning , particularly when the number of instances per relation is low.

Original: This is expected to beneﬁt TuckER on the NELL-995 dataset (200 relations).
Revised: This is expected to beneﬁt TuckER on the NELL-995 dataset (200 relations).

Original: As predicted in P1(a), all models tend to perform best at type R relations, with a clear performance gap to other relation types.
Revised: As predicted in P1(a), all models tend to perform best at type R relations, with a clear performance gap to other relation types.

Original: Also, performance on type S relations appears higher in general than type C.
Revised: Also, performance on type S relations appears higher in general than type C.

Original: In accordance with P1(b), additive-only models (TransE, MuRE I ) perform worst on average since all relation types involve (multiplicative) relatedness.
Revised: In accordance with P1(b), additive-only models (TransE, MuRE I ) perform worst on average since all relation types involve (multiplicative) relatedness.

Original: Best performance is achieved on type R relations, which can be represented by a small/zero additive vector.
Revised: Best performance is achieved on type R relations, which can be represented by a small/zero additive vector.

Original: Multiplicative-only DistMult performs well, sometimes best, on type R relations, ﬁtting expectation as it can represent those relations and has no inessential parameters, e.g. that may overﬁt to noise, which may explain instances where MuRE performs slightly worse.
Revised: Multiplicative-only DistMult performs well, sometimes best, on type R relations, ﬁtting expectation as it can represent those relations and has no inessential parameters, e.g. that may overﬁt to noise, which may explain instances where MuRE performs slightly worse.

Original: As expected, MuRE, performs best overall (particularly on WN18RR), and most strongly on S and C type relations, predicted to require both multiplicative and additive components.
Revised: As expected, MuRE, performs best overall (particularly on WN18RR), and most strongly on S and C type relations, predicted to require both multiplicative and additive components.

Original: Comparable performance of TuckER on NELL-995 may be explained by its multi-task learning ability.
Revised: Comparable performance of TuckER on NELL-995 may be explained by its multi-task learning ability.

Original: [Table] [Table]
Revised: [Table] [Table]

Original: teamplaysagainstteam
Revised: teamplaysagainstteam

Original: [Table]
Revised: [Table]

Original: clothingtogowithclothing
Revised: clothingtogowithclothing

Original: [Table]
Revised: [Table]

Original: bodypartcontainsbodypart
Revised: bodypartcontainsbodypart

Original: [Table]
Revised: [Table]

Original: locationlocatedwithinlocation
Revised: locationlocatedwithinlocation

Original: [Table]
Revised: [Table]

Original: Other anomalous results also closely align with confounding factors.
Revised: Other anomalous results also closely align with confounding factors.

Original: For example, all models perform poorly on the hypernym relation, despite it having a relative abundance of training data (40% of all instances), which may be explained by its hierarchical nature (Khs ≈ 1 and long paths).
Revised: For example, all models perform poorly on the hypernym relation, despite it having a relative abundance of training data (40% of all instances), which may be explained by its hierarchical nature (Khs ≈ 1 and long paths).

Original: The same may explain the reduced performance on relations also_see and agentcollaborateswithagent .
Revised: The same may explain the reduced performance on relations also_see and agentcollaborateswithagent .

Original: As found previously (Balaževi ´ c et al., 2019a), none of the models considered are well suited to modelling hierarchical structures.
Revised: As found previously (Balaževi ´ c et al., 2019a), none of the models considered are well suited to modelling hierarchical structures.

Original: We also note that the percentage of training instances of a relation is not a dominant factor on performance, as would be expected if all relations could be equally represented.
Revised: We also note that the percentage of training instances of a relation is not a dominant factor on performance, as would be expected if all relations could be equally represented.

Original: Classiﬁcation performance: We further evaluate whether P1 holds when comparing knowledge graph models by classiﬁcation accuracy on WN18RR.
Revised: Classiﬁcation performance: We further evaluate whether P1 holds when comparing knowledge graph models by classiﬁcation accuracy on WN18RR.

Original: Independent predictions of whether a given triple is true or false are not commonly evaluated, instead metrics such as mean reciprocal rank and
Revised: Independent predictions of whether a given triple is true or false are not commonly evaluated, instead metrics such as mean reciprocal rank and

Original: Hits@
Revised: Hits@

Original: k are reported that compare the prediction of a test triple against all evaluation triples.
Revised: k are reported that compare the prediction of a test triple against all evaluation triples.

Original: Not only is this computationally costly, the evaluation is ﬂawed if an entity is related to l>k others ( k is often 1 or 3).
Revised: Not only is this computationally costly, the evaluation is ﬂawed if an entity is related to l>k others ( k is often 1 or 3).

Original: A correct prediction validly falling within the top l but not the top k would appear incorrect under the metric.
Revised: A correct prediction validly falling within the top l but not the top k would appear incorrect under the metric.

Original: Some recent works also note the importance of standalone predictions (Speranskaya et al., 2020; Pezeshkpour et al., 2020).
Revised: Some recent works also note the importance of standalone predictions (Speranskaya et al., 2020; Pezeshkpour et al., 2020).

Original: Since for each relation there are n 2 e possible entity-entity relationships, we sub-sample by computing predictions only for those ( e s , r, e o ) triples for which the e s , r pairs appear in the test set.
Revised: Since for each relation there are n 2 e possible entity-entity relationships, we sub-sample by computing predictions only for those ( e s , r, e o ) triples for which the e s , r pairs appear in the test set.

Original: We split positive predictions ( σ ( φ ( e s , r, e o )) > 0 . 5 ) between (i) known truths – training or test/validation instances; and (ii) other , the truth of which is not known.
Revised: We split positive predictions ( σ ( φ ( e s , r, e o )) > 0 . 5 ) between (i) known truths – training or test/validation instances; and (ii) other , the truth of which is not known.

Original: We then compute per-relation accuracy over the true training instances (“train”) and true test/validation instances (“test”); and the average number of “other” triples predicted true per e s , r pair.
Revised: We then compute per-relation accuracy over the true training instances (“train”) and true test/validation instances (“test”); and the average number of “other” triples predicted true per e s , r pair.

Original: Table 5 shows results for MuRE I , DistMult,
Revised: Table 5 shows results for MuRE I , DistMult,

Original: TuckER and MuRE.
Revised: TuckER and MuRE.

Original: All models achieve near perfect training accuracy.
Revised: All models achieve near perfect training accuracy.

Original: The additive-multiplicative
Revised: The additive-multiplicative

Original: MuRE gives best test set performance, followed (surprisingly) closely by MuRE I , with multiplicative models (DistMult and TuckER) performing poorly on all but type R relations in line with P1(b), with near-zero performance on most type S/C relations.
Revised: MuRE gives best test set performance, followed (surprisingly) closely by MuRE I , with multiplicative models (DistMult and TuckER) performing poorly on all but type R relations in line with P1(b), with near-zero performance on most type S/C relations.

Original: Since the ground truth labels for “other” triples predicted to be true are not in the dataset, we analyse a sample of “other” true predictions for one relation of each type (see Appx.
Revised: Since the ground truth labels for “other” triples predicted to be true are not in the dataset, we analyse a sample of “other” true predictions for one relation of each type (see Appx.

Original: G).
Revised: G).

Original: From this, we estimate that TuckER is relatively accurate but pessimistic ( ∼ 0.3 correct of the 0.5 predictions ≈ 60% ), MuRE
Revised: From this, we estimate that TuckER is relatively accurate but pessimistic ( ∼ 0.3 correct of the 0.5 predictions ≈ 60% ), MuRE

Original: I is optimistic but inaccurate ( ∼ 2.3 of 7.5 ≈ 31% ), whereas MuRE is both optimistic and accurate ( ∼ 1.1 of 1.5 ≈ 73% ).
Revised: I is optimistic but inaccurate ( ∼ 2.3 of 7.5 ≈ 31% ), whereas MuRE is both optimistic and accurate ( ∼ 1.1 of 1.5 ≈ 73% ).

Original: Summary:
Revised: Summary:

Original: Our analysis identiﬁes the best performing model per relation type as predicted by P1(b): multiplicative-only DistMult for type R, additive-multiplicative MuRE for types S/C; providing a basis for dataset-dependent model selection .
Revised: Our analysis identiﬁes the best performing model per relation type as predicted by P1(b): multiplicative-only DistMult for type R, additive-multiplicative MuRE for types S/C; providing a basis for dataset-dependent model selection .

Original: The per-relation insight into where models perform
Revised: The per-relation insight into where models perform

Original: [Table]
Revised: [Table]

Original: poorly, e.g. hierarchical or type C relations, can be used to aid and direct future model design.
Revised: poorly, e.g. hierarchical or type C relations, can be used to aid and direct future model design.

Original: Analysis of the classiﬁcation performance: (i) shows that MuRE is the most reliable fact prediction model; and (ii) emphasises the poorer ability of multiplicative-only models to represent S/C relations
Revised: Analysis of the classiﬁcation performance: (i) shows that MuRE is the most reliable fact prediction model; and (ii) emphasises the poorer ability of multiplicative-only models to represent S/C relations

Original: . 4. P2: P ROPERTIES OF RELATION REPRESENTATION
Revised: . 4. P2: P ROPERTIES OF RELATION REPRESENTATION

Original: P2(a)-(b): Table 6 shows the symmetry score ( ∈ [-1, 1] indicating perfect anti-symmetry to symmetry; see Appx.
Revised: P2(a)-(b): Table 6 shows the symmetry score ( ∈ [-1, 1] indicating perfect anti-symmetry to symmetry; see Appx.

Original: F) for the relation matrix of TuckER and the norm of relation vectors of TransE,
Revised: F) for the relation matrix of TuckER and the norm of relation vectors of TransE,

Original: MuRE I and MuRE on the WN18RR dataset.
Revised: MuRE I and MuRE on the WN18RR dataset.

Original: As expected, type R relations have materially higher symmetry than both other relation types, ﬁtting the prediction of how TuckER compensates for having no additive component.
Revised: As expected, type R relations have materially higher symmetry than both other relation types, ﬁtting the prediction of how TuckER compensates for having no additive component.

Original: All additive models learn relation vectors of a noticeably lower norm for type R relations, which in the limiting case (similarity) require no additive component, than for types S or C.
Revised: All additive models learn relation vectors of a noticeably lower norm for type R relations, which in the limiting case (similarity) require no additive component, than for types S or C.

Original: P2(c): Fig 2 shows eigenvalue magnitudes (scaled relative to the largest and ordered) of relationspeciﬁc matrices R of MuRE, labelled by relation type, as predicted to reﬂect the strength of a relation’s relatedness aspect.
Revised: P2(c): Fig 2 shows eigenvalue magnitudes (scaled relative to the largest and ordered) of relationspeciﬁc matrices R of MuRE, labelled by relation type, as predicted to reﬂect the strength of a relation’s relatedness aspect.

Original: As expected, values are highest for type R relations.
Revised: As expected, values are highest for type R relations.

Original: For relation types
Revised: For relation types

Original: S and C
Revised: S and C

Original: the proﬁles are more varied, supporting the understanding that relatedness of such relations is highly variable, both in its nature (components of S ) and strength (cardinality of S ).
Revised: the proﬁles are more varied, supporting the understanding that relatedness of such relations is highly variable, both in its nature (components of S ) and strength (cardinality of S ).

Original: [Table]
Revised: [Table]

Original: Type
Revised: Type

Original: TuckER
Revised: TuckER

Original: [Table]
Revised: [Table]

Original: verb
Revised: verb

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table]
Revised: [Table]

Original: 89 derivationally_related
Revised: 89 derivationally_related

Original: [Table]
Revised: [Table]

Original: form
Revised: form

Original: R
Revised: R

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: 69 also_see
Revised: 69 also_see

Original: R 0.
Revised: R 0.

Original: 0.97 instance_hypernym
Revised: 0.97 instance_hypernym

Original: S
Revised: S

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: 88 member
Revised: 88 member

Original: [Table]
Revised: [Table]

Original: of
Revised: of

Original: [Table]
Revised: [Table]

Original: usage
Revised: usage

Original: C
Revised: C

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: 88 member
Revised: 88 member

Original: [Table]
Revised: [Table]

Original: region
Revised: region

Original: C
Revised: C

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: topic
Revised: topic

Original: [Table]
Revised: [Table]

Original: C
Revised: C

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table]
Revised: [Table]

Original: 52 member
Revised: 52 member

Original: [Table]
Revised: [Table]

Original: C 0.
Revised: C 0.

Original: 1.97 has_part
Revised: 1.97 has_part

Original: C 0.
Revised: C 0.

Original: 1.25 hypernym
Revised: 1.25 hypernym

Original: S 0.
Revised: S 0.

Original: [Figure]
Revised: [Figure]

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: Many low-rank knowledge graph representation models have been developed, yet little is known of the latent structure they learn.
Revised: Many low-rank knowledge graph representation models have been developed, yet little is known of the latent structure they learn.

Original: We build on recent understanding of PMI-based word embeddings to theoretically establish a set of geometric properties of relation representations (relation conditions) required to map PMI-based word embeddings of subject entities to related object entities under knowledge graph relations.
Revised: We build on recent understanding of PMI-based word embeddings to theoretically establish a set of geometric properties of relation representations (relation conditions) required to map PMI-based word embeddings of subject entities to related object entities under knowledge graph relations.

Original: These conditions partition relations into three types and provide a basis to consider the loss functions of existing knowledge graph models.
Revised: These conditions partition relations into three types and provide a basis to consider the loss functions of existing knowledge graph models.

Original: Models that satisfy the relation conditions of a particular type have a known set of model parameters that minimise the loss function, i.e. the parameters of PMI embeddings, together with potentially many equivalent solutions.
Revised: Models that satisfy the relation conditions of a particular type have a known set of model parameters that minimise the loss function, i.e. the parameters of PMI embeddings, together with potentially many equivalent solutions.

Original: We show that the better a model’s architecture satisﬁes a relation’s conditions, the better its performance at link prediction, evaluated under both rank-based metrics and accuracy.
Revised: We show that the better a model’s architecture satisﬁes a relation’s conditions, the better its performance at link prediction, evaluated under both rank-based metrics and accuracy.

Original: Overall, we generalise recent theoretical understanding of how particular semantic relations, e.g. similarity and analogy, are encoded between PMI-based word embeddings to the general relations of knowledge graphs.
Revised: Overall, we generalise recent theoretical understanding of how particular semantic relations, e.g. similarity and analogy, are encoded between PMI-based word embeddings to the general relations of knowledge graphs.

Original: In doing so, we provide evidence in support of our initial premise: that common latent structure is exploited by both PMI-based word embeddings (e.g. W2V) and knowledge graph representation.
Revised: In doing so, we provide evidence in support of our initial premise: that common latent structure is exploited by both PMI-based word embeddings (e.g. W2V) and knowledge graph representation.

Original: A C ATEGORISING WN18RR R ELATIONS
Revised: A C ATEGORISING WN18RR R ELATIONS

Original: Table 7 describes how each WN18RR relation was assigned to its respective category.
Revised: Table 7 describes how each WN18RR relation was assigned to its respective category.

Original: [Table]
Revised: [Table]

Original: B C ATEGORISING NELL-995 R ELATIONS
Revised: B C ATEGORISING NELL-995 R ELATIONS

Original: Categorisation of NELL-995 relations and the explanation for the category assignment of are shown in Tables 8 and 9 respectively
Revised: Categorisation of NELL-995 relations and the explanation for the category assignment of are shown in Tables 8 and 9 respectively

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table]
Revised: [Table]

Original: C S PLITTING THE NELL-995 D ATASET
Revised: C S PLITTING THE NELL-995 D ATASET

Original: The test set of NELL-995 created by Xiong et al.
Revised: The test set of NELL-995 created by Xiong et al.

Original: (2017) contains only 10 out of 200 relations present in the training set.
Revised: (2017) contains only 10 out of 200 relations present in the training set.

Original: To ensure a fair representation of all training set relations in the validation and test sets, we create new validation and test set splits by combining the initial validation and test sets with the training set and randomly selecting 10,000 triples each from the combined dataset
Revised: To ensure a fair representation of all training set relations in the validation and test sets, we create new validation and test set splits by combining the initial validation and test sets with the training set and randomly selecting 10,000 triples each from the combined dataset

Original: [Table]
Revised: [Table]

Original: D I MPLEMENTATION D ETAILS
Revised: D I MPLEMENTATION D ETAILS

Original: All algorithms are re-implemented in PyTorch with the Adam optimizer (Kingma & Ba, 2015) that minimises binary cross-entropy loss, using hyper-parameters that work well for all models (learning rate: 0.001, batch size: 128, number of negative samples: 50).
Revised: All algorithms are re-implemented in PyTorch with the Adam optimizer (Kingma & Ba, 2015) that minimises binary cross-entropy loss, using hyper-parameters that work well for all models (learning rate: 0.001, batch size: 128, number of negative samples: 50).

Original: Entity and relation embedding dimensionality is set to d e = d r =200 for all models except TuckER, for which d r =30 (Balaževi´c et al., 2019b).
Revised: Entity and relation embedding dimensionality is set to d e = d r =200 for all models except TuckER, for which d r =30 (Balaževi´c et al., 2019b).

Original: E K RACKHARDT H IERARCHY S CORE
Revised: E K RACKHARDT H IERARCHY S CORE

Original: The Krackhardt hierarchy score measures the proportion of node pairs ( x, y ) where there exists a directed path x
Revised: The Krackhardt hierarchy score measures the proportion of node pairs ( x, y ) where there exists a directed path x

Original: → y , but not y
Revised: → y , but not y

Original: → x ; and it takes a value of one for all directed acyclic graphs, and zero for cycles and cliques (Krackhardt, 2014; Balaževi´c et al., 2019a).
Revised: → x ; and it takes a value of one for all directed acyclic graphs, and zero for cycles and cliques (Krackhardt, 2014; Balaževi´c et al., 2019a).

Original: Let M ∈ R n × n be the binary reachability matrix of a directed graph G with n nodes, with M i,j =
Revised: Let M ∈ R n × n be the binary reachability matrix of a directed graph G with n nodes, with M i,j =

Original: if there exists a directed path from node i to node j and 0 otherwise.
Revised: if there exists a directed path from node i to node j and 0 otherwise.

Original: The Krackhardt hierarchy score of G is deﬁned as:
Revised: The Krackhardt hierarchy score of G is deﬁned as:

Original: [Equation]
Revised: [Equation]

Original: F S YMMETRY S CORE
Revised: F S YMMETRY S CORE

Original: The symmetry score ∈ [ − 1 , 1] (Hubert & Baker, 1979) for a relation matrix
Revised: The symmetry score ∈ [ − 1 , 1] (Hubert & Baker, 1979) for a relation matrix

Original: R ∈ R d e × d e is deﬁned as:
Revised: R ∈ R d e × d e is deﬁned as:

Original: [Equation]
Revised: [Equation]

Original: where 1 indicates a symmetric and -1 an anti-symmetric matrix.
Revised: where 1 indicates a symmetric and -1 an anti-symmetric matrix.

Original: G “O THER ” P REDICTED F ACTS
Revised: G “O THER ” P REDICTED F ACTS

Original: Tables 10 to 13 shows a sample of the unknown triples (i.e. those formed using the WN18RR entities and relations, but not present in the dataset) for the derivationally_related form (R), instance_hypernym (S) and synset_domain_topic_of (C) relations at a range of probabilities ( σ ( φ ( e s , r, e o ))
Revised: Tables 10 to 13 shows a sample of the unknown triples (i.e. those formed using the WN18RR entities and relations, but not present in the dataset) for the derivationally_related form (R), instance_hypernym (S) and synset_domain_topic_of (C) relations at a range of probabilities ( σ ( φ ( e s , r, e o ))

Original: ≈ { 0 . 4 , 0 .
Revised: ≈ { 0 . 4 , 0 .

Original: 8 , 1 } ), as predicted by each model.
Revised: 8 , 1 } ), as predicted by each model.

Original: True triples are indicated in bold; instances where a model predicts an entity is related to itself are indicated in blue.
Revised: True triples are indicated in bold; instances where a model predicts an entity is related to itself are indicated in blue.

Original: [Table]
Revised: [Table]

Original: r a w _ c l o s e _ V
Revised: r a w _ c l o s e _ V

Original: B _3 ) ( s t ee r _ VB _1 , s t ee r _
Revised: B _3 ) ( s t ee r _ VB _1 , s t ee r _

Original: VB _1 ) ( c on ti nu e _ VB _10 , p r o cee d_
Revised: VB _1 ) ( c on ti nu e _ VB _10 , p r o cee d_

Original: VB _1 ) ( e x t r apo l a t e _
Revised: VB _1 ) ( e x t r apo l a t e _

Original: VB _3 , s y n t
Revised: VB _3 , s y n t

Original: h e s i s _ NN _ ) ( t r a i n _ VB _3 , t r a i n i n g_ NN _1 ) ( s o r t
Revised: h e s i s _ NN _ ) ( t r a i n _ VB _3 , t r a i n i n g_ NN _1 ) ( s o r t

Original: ou t _ VB _1 , s o r t _ou t _
Revised: ou t _ VB _1 , s o r t _ou t _

Original: VB _1 ) ( s uppo r t _ VB _6 , d e f e nd_
Revised: VB _1 ) ( s uppo r t _ VB _6 , d e f e nd_

Original: VB _5 ) ( s t r a t e g
Revised: VB _5 ) ( s t r a t e g

Original: i s t _
Revised: i s t _

Original: NN _1 , m a c h i na ti on _ NN _1 ) ( s c r a t c h _ VB _3 , s k i n _ s e n s a ti o n _
Revised: NN _1 , m a c h i na ti on _ NN _1 ) ( s c r a t c h _ VB _3 , s k i n _ s e n s a ti o n _

Original: NN _1 ) ( m a ke
Revised: NN _1 ) ( m a ke

Original: f u ll _
Revised: f u ll _

Original: VB _1 , m a ke _ f u ll _ VB _1 ) ( s hu tt e r _ NN _1 , ﬁ ll _up_
Revised: VB _1 , m a ke _ f u ll _ VB _1 ) ( s hu tt e r _ NN _1 , ﬁ ll _up_

Original: VB _3 ) ( c r u s h _ VB _4 , g r i nd _ VB _2 ) ( s c h e m e _ NN _5 , s c
Revised: VB _3 ) ( c r u s h _ VB _4 , g r i nd _ VB _2 ) ( s c h e m e _ NN _5 , s c

Original: h
Revised: h

Original: e m a ti z a ti on _
Revised: e m a ti z a ti on _

Original: NN _1 ) ( u tili z
Revised: NN _1 ) ( u tili z

Original: e _ VB _1 , u tili z e _
Revised: e _ VB _1 , u tili z e _

Original: VB _1 ) ( y a w n i ng_ NN _1 , pa t e ll a r _ r e ﬂ ex _ NN _1 ) ( s p i ke _ VB _5 , s t e ad y _ VB _2 ) ( o r da i n_ VB _3 , ve s t _ VB _1 )
Revised: VB _1 ) ( y a w n i ng_ NN _1 , pa t e ll a r _ r e ﬂ ex _ NN _1 ) ( s p i ke _ VB _5 , s t e ad y _ VB _2 ) ( o r da i n_ VB _3 , ve s t _ VB _1 )

Original: ( g e o l og y _ NN _1 , g e o l og y _
Revised: ( g e o l og y _ NN _1 , g e o l og y _

Original: NN _1 ) ( y a w _
Revised: NN _1 ) ( y a w _

Original: NN _1 , s p
Revised: NN _1 , s p

Original: i r a l _
Revised: i r a l _

Original: VB _1 ) ( li ck i ng_ NN _1 , v anqu i s h_
Revised: VB _1 ) ( li ck i ng_ NN _1 , v anqu i s h_

Original: VB _1 ) ( li e _
Revised: VB _1 ) ( li e _

Original: VB _1 , f r on t _ e nd _ NN _1 ) ( z oo l og y _ NN _2 , z oo l og y _
Revised: VB _1 , f r on t _ e nd _ NN _1 ) ( z oo l og y _ NN _2 , z oo l og y _

Original: NN _2 ) ( s t r a t u m _ NN _2 , s o c
Revised: NN _2 ) ( s t r a t u m _ NN _2 , s o c

Original: i a l _g r oup
Revised: i a l _g r oup

Original: _ NN _1 ) ( s y n t
Revised: _ NN _1 ) ( s y n t

Original: h e ti c a l _
Revised: h e ti c a l _

Original: JJ_1 , s y n t
Revised: JJ_1 , s y n t

Original: h e s i
Revised: h e s i

Original: z
Revised: z

Original: e r _ NN _2 ) ( t r e ad _ NN _1 , s t e p_
Revised: e r _ NN _2 ) ( t r e ad _ NN _1 , s t e p_

Original: NN _9 ) ( u r ano l og y _
Revised: NN _9 ) ( u r ano l og y _

Original: NN _1 , u r ano l og y _ NN _1 ) ( du e l _
Revised: NN _1 , u r ano l og y _ NN _1 ) ( du e l _

Original: VB _1 , s c u f ﬂ e _
Revised: VB _1 , s c u f ﬂ e _

Original: NN _3 ) ( r e a li z a ti on_
Revised: NN _3 ) ( r e a li z a ti on_

Original: NN _2 , e m bod
Revised: NN _2 , e m bod

Original: i m e n t _
Revised: i m e n t _

Original: NN _3 ) ( r e g i s t e r _ NN _3 , ﬁ l e _a w a y _ VB _ 1 ) ( t r a ve l _ VB _1 , t
Revised: NN _3 ) ( r e g i s t e r _ NN _3 , ﬁ l e _a w a y _ VB _ 1 ) ( t r a ve l _ VB _1 , t

Original: r a ve l _ VB _1 ) i n s t a n ce _ hyp e
Revised: r a ve l _ VB _1 ) i n s t a n ce _ hyp e

Original: r ny m ( S ) ( t ho m a s _aqu i na s _
Revised: r ny m ( S ) ( t ho m a s _aqu i na s _

Original: NN _1 , m a r t y r _
Revised: NN _1 , m a r t y r _

Original: NN _2 ) ( t a i w a n _ NN _1 , a s i a n _ n a ti o n _
Revised: NN _2 ) ( t a i w a n _ NN _1 , a s i a n _ n a ti o n _

Original: NN _1 ) ( p r op h
Revised: NN _1 ) ( p r op h

Original: e t s _
Revised: e t s _

Original: NN _1 , go s p e l s _
Revised: NN _1 , go s p e l s _

Original: NN _ 1 ) ( h e l s i n k i _ NN _1 , u r ba n _ ce n t e r _
Revised: NN _ 1 ) ( h e l s i n k i _ NN _1 , u r ba n _ ce n t e r _

Original: NN _1 ) ( v o l c ano_
Revised: NN _1 ) ( v o l c ano_

Original: i s l and s _
Revised: i s l and s _

Original: NN _1 , v o l c ano _ NN _2 ) ( s t .
Revised: NN _1 , v o l c ano _ NN _2 ) ( s t .

Original: _g r e go r y _
Revised: _g r e go r y _

Original: o f _ n .
Revised: o f _ n .

Original: NN _1 , c a n o n i z a ti o n _
Revised: NN _1 , c a n o n i z a ti o n _

Original: NN _1 ) ( m a l c o l m _ x _
Revised: NN _1 ) ( m a l c o l m _ x _

Original: NN _1 , pa ss i ve _ r e
Revised: NN _1 , pa ss i ve _ r e

Original: s i s t e r _ NN _1 ) ( m annh e i m _
Revised: s i s t e r _ NN _1 ) ( m annh e i m _

Original: NN _1 , s t o ck ad e _ NN _2 ) ( c ap e _ ho r n_
Revised: NN _1 , s t o ck ad e _ NN _2 ) ( c ap e _ ho r n_

Original: NN _1 , u r ban_ ce n t e r _
Revised: NN _1 , u r ban_ ce n t e r _

Original: NN _1 ) ( s t .
Revised: NN _1 ) ( s t .

Original: _g r e go r y _
Revised: _g r e go r y _

Original: o f _n .
Revised: o f _n .

Original: NN _1 , s a i n t _
Revised: NN _1 , s a i n t _

Original: VB _2 ) ( t a i w an_ NN _1 , na ti ona l _ c ap it a l _
Revised: VB _2 ) ( t a i w an_ NN _1 , na ti ona l _ c ap it a l _

Original: NN _1 ) ( n i ppon_
Revised: NN _1 ) ( n i ppon_

Original: NN _1 , n i ppon _ NN _1 ) ( b e r g e n_ NN _1 , na ti ona l _ c ap it a l _
Revised: NN _1 , n i ppon _ NN _1 ) ( b e r g e n_ NN _1 , na ti ona l _ c ap it a l _

Original: NN _1 ) ( m cc o r
Revised: NN _1 ) ( m cc o r

Original: m
Revised: m

Original: i ck _ NN _1 , ﬁnd_ VB _8 )
Revised: i ck _ NN _1 , ﬁnd_ VB _8 )

Original: ( t r u t h_
Revised: ( t r u t h_

Original: NN _5 , abo liti on i s m _
Revised: NN _5 , abo liti on i s m _

Original: NN _1 ) ( v i c t
Revised: NN _1 ) ( v i c t

Original: o r _hugo _ NN _1 , no ve l _
Revised: o r _hugo _ NN _1 , no ve l _

Original: NN _1 ) ( m a rs ha ll _
Revised: NN _1 ) ( m a rs ha ll _

Original: NN _2 , g e n e r a l s h i p_
Revised: NN _2 , g e n e r a l s h i p_

Original: NN _1 ) ( s t .
Revised: NN _1 ) ( s t .

Original: _g r e go r y _ i _ NN _1 , b i s h op _ NN _ )
Revised: _g r e go r y _ i _ NN _1 , b i s h op _ NN _ )

Original: ( t h o m a s _aq u i n a s _
Revised: ( t h o m a s _aq u i n a s _

Original: NN _1 , s a i n t _
Revised: NN _1 , s a i n t _

Original: VB _2 ) ( r e g i o m on t anu s _
Revised: VB _2 ) ( r e g i o m on t anu s _

Original: NN _1 , u r ano l og y _ NN _1 ) ( n a n s e n _ NN _1 , ve n t u r e r _ NN _ )
Revised: NN _1 , u r ano l og y _ NN _1 ) ( n a n s e n _ NN _1 , ve n t u r e r _ NN _ )

Original: ( r i c ha r d_bu ck
Revised: ( r i c ha r d_bu ck

Original: m i n s t e r _ f .
Revised: m i n s t e r _ f .

Original: NN _1 , t ec hno l og i c a l _
Revised: NN _1 , t ec hno l og i c a l _

Original: JJ _2 ) ( ce n t r a l _a m e r i c a_
Revised: JJ _2 ) ( ce n t r a l _a m e r i c a_

Original: NN _1 , s .
Revised: NN _1 , s .

Original: _a m .
Revised: _a m .

Original: _ na ti on _ NN _1 ) ( p r op h
Revised: _ na ti on _ NN _1 ) ( p r op h

Original: e t s _
Revised: e t s _

Original: NN _1 , book_
Revised: NN _1 , book_

Original: NN _ ) ( w i s c on s i n_
Revised: NN _ ) ( w i s c on s i n_

Original: NN _2 , s t a t e _
Revised: NN _2 , s t a t e _

Original: c
Revised: c

Original: ap it a l _
Revised: ap it a l _

Original: NN _1 ) ( t ho m a s _aqu i na s _
Revised: NN _1 ) ( t ho m a s _aqu i na s _

Original: NN _1 , a r c hb i s hop
Revised: NN _1 , a r c hb i s hop

Original: _ NN _1 ) ( d e _ m ill e _ NN _1 , dan ce _
Revised: _ NN _1 ) ( d e _ m ill e _ NN _1 , dan ce _

Original: VB _1 ) ( t ho m a s _aqu i na s _
Revised: VB _1 ) ( t ho m a s _aqu i na s _

Original: NN _1 , c hu r c h_ f a t
Revised: NN _1 , c hu r c h_ f a t

Original: h e r _
Revised: h e r _

Original: NN _1 ) ( p r u ss i a_ NN _1 , s t o ck ad e _
Revised: NN _1 ) ( p r u ss i a_ NN _1 , s t o ck ad e _

Original: NN _2 ) ( m a rs
Revised: NN _2 ) ( m a rs

Original: h
Revised: h

Original: a ll _
Revised: a ll _

Original: NN _2 , g e n e r a l _ o f ﬁ ce r _ NN _1 ) ( s t .
Revised: NN _2 , g e n e r a l _ o f ﬁ ce r _ NN _1 ) ( s t .

Original: _g r e go r y _ i _ NN _1 , apo s tl e _
Revised: _g r e go r y _ i _ NN _1 , apo s tl e _

Original: NN _3 ) ( w ood y _gu t h r i e _
Revised: NN _3 ) ( w ood y _gu t h r i e _

Original: NN _1 , m i n s t r e l _ VB _1 ) ( d e _ m ill e _
Revised: NN _1 , m i n s t r e l _ VB _1 ) ( d e _ m ill e _

Original: NN _1 , da n c i n g - m a s t
Revised: NN _1 , da n c i n g - m a s t

Original: e r _ NN _ ) ( n e w m an_ NN _2 , p r
Revised: e r _ NN _ ) ( n e w m an_ NN _2 , p r

Original: i m a t
Revised: i m a t

Original: e s h i p
Revised: e s h i p

Original: _ NN _1 ) ( f e r til e _ c r e
Revised: _ NN _1 ) ( f e r til e _ c r e

Original: s ce n t _
Revised: s ce n t _

Original: NN _1 , a s i an_ na ti on _ NN _1 ) ( ce n t r a l _a m
Revised: NN _1 , a s i an_ na ti on _ NN _1 ) ( ce n t r a l _a m

Original: e
Revised: e

Original: r
Revised: r

Original: i c a_
Revised: i c a_

Original: NN _1 , c .
Revised: NN _1 , c .

Original: _a m .
Revised: _a m .

Original: _ na ti on _ NN _1 ) ( a e g e an_ s e a_
Revised: _ na ti on _ NN _1 ) ( a e g e an_ s e a_

Original: NN _1 , a e g e an _
Revised: NN _1 , a e g e an _

Original: i s l and _ NN _1 ) ( t ho m a s _ t h e _apo s tl e _ NN _1 , s an c tif y _
Revised: i s l and _ NN _1 ) ( t ho m a s _ t h e _apo s tl e _ NN _1 , s an c tif y _

Original: VB _1 ) ( r ob e r t _o w e n _ NN _1 , i ndu s t r y _ NN _1 ) ( a e g e an_ s e a_
Revised: VB _1 ) ( r ob e r t _o w e n _ NN _1 , i ndu s t r y _ NN _1 ) ( a e g e an_ s e a_

Original: NN _1 , i s l and_ NN _1 ) s yn s e t _ do m a i n_ t op i c
Revised: NN _1 , i s l and_ NN _1 ) s yn s e t _ do m a i n_ t op i c

Original: _o f ( C ) ( w r it e _
Revised: _o f ( C ) ( w r it e _

Original: VB _8 , t ap e _ VB _3 ) ( d r a w _ VB _6 , c
Revised: VB _8 , t ap e _ VB _3 ) ( d r a w _ VB _6 , c

Original: r e a ti ve _p e rs on_ NN _1 ) ( li b e l _
Revised: r e a ti ve _p e rs on_ NN _1 ) ( li b e l _

Original: NN _1 , s u ll y _
Revised: NN _1 , s u ll y _

Original: VB _3 ) ( li b e l _
Revised: VB _3 ) ( li b e l _

Original: NN _1 , d i s pa r ag e m e n t _ NN _1 ) ( i n t r o ve r t _
Revised: NN _1 , d i s pa r ag e m e n t _ NN _1 ) ( i n t r o ve r t _

Original: NN _1 , s c
Revised: NN _1 , s c

Original: i e n ti ﬁ c _d
Revised: i e n ti ﬁ c _d

Original: i s c
Revised: i s c

Original: i p li n e _
Revised: i p li n e _

Original: NN _1 ) ( s u bo r d e r _
Revised: NN _1 ) ( s u bo r d e r _

Original: NN _1 , t axo n o m i
Revised: NN _1 , t axo n o m i

Original: c _
Revised: c _

Original: g r o u p_ NN _1 ) ( r e l a ti on s h
Revised: g r o u p_ NN _1 ) ( r e l a ti on s h

Original: i p_
Revised: i p_

Original: NN _4 , r e l a ti on s h
Revised: NN _4 , r e l a ti on s h

Original: i p_
Revised: i p_

Original: NN _4 ) ( r o ll - o n _
Revised: NN _4 ) ( r o ll - o n _

Original: r
Revised: r

Original: o llo ff _
Revised: o llo ff _

Original: NN _1 , t r a n s po r t _ NN _1 ) ( li b e l _
Revised: NN _1 , t r a n s po r t _ NN _1 ) ( li b e l _

Original: NN _1 , s l u r _ NN _2 ) ( d r a w _ VB _6 , d r a w _
Revised: NN _1 , s l u r _ NN _2 ) ( d r a w _ VB _6 , d r a w _

Original: VB _6 ) ( e t y m o l
Revised: VB _6 ) ( e t y m o l

Original: og
Revised: og

Original: i
Revised: i

Original: z i n g_ NN _1 , li n g u i s ti
Revised: z i n g_ NN _1 , li n g u i s ti

Original: c s _ NN _1 ) ( p r a n ce _ VB _4 , e q u e s t
Revised: c s _ NN _1 ) ( p r a n ce _ VB _4 , e q u e s t

Original: r
Revised: r

Original: i a n _ s po r t _ NN _1 ) ( e t y m o
Revised: i a n _ s po r t _ NN _1 ) ( e t y m o

Original: l
Revised: l

Original: og i
Revised: og i

Original: z
Revised: z

Original: i ng_ NN _1 , l a w _ NN _1 ) ( ﬁ rs t _ s a c k e r _
Revised: i ng_ NN _1 , l a w _ NN _1 ) ( ﬁ rs t _ s a c k e r _

Original: NN _1 , ba ll ga m e _
Revised: NN _1 , ba ll ga m e _

Original: NN _2 ) ( t u r n _
Revised: NN _2 ) ( t u r n _

Original: VB _12 , c u lti v a ti o n _
Revised: VB _12 , c u lti v a ti o n _

Original: NN _ 2 ) ( li b e l _
Revised: NN _ 2 ) ( li b e l _

Original: NN _1 , t r ad u
Revised: NN _1 , t r ad u

Original: ce m e n t _
Revised: ce m e n t _

Original: NN _1 ) ( t e m p l e _ NN _4 , p l a ce _o f _ w o rs h
Revised: NN _1 ) ( t e m p l e _ NN _4 , p l a ce _o f _ w o rs h

Original: i p_ NN _1 ) ( a l c h e m i z e _
Revised: i p_ NN _1 ) ( a l c h e m i z e _

Original: VB _1 , m od if y _
Revised: VB _1 , m od if y _

Original: VB _3 ) ( b r y nh il d_ NN _1 , m y t
Revised: VB _3 ) ( b r y nh il d_ NN _1 , m y t

Original: ho l og i z
Revised: ho l og i z

Original: e _
Revised: e _

Original: VB _2 ) ( s e ll _
Revised: VB _2 ) ( s e ll _

Original: VB _1 , s e lli n g_
Revised: VB _1 , s e lli n g_

Original: NN _1 ) ( t r i a l _ i m p r e ss i on_
Revised: NN _1 ) ( t r i a l _ i m p r e ss i on_

Original: NN _1 , p r oo f _
Revised: NN _1 , p r oo f _

Original: VB _1 ) ( s e r m on_ NN _1 , s e r m on_
Revised: VB _1 ) ( s e r m on_ NN _1 , s e r m on_

Original: NN _1 ) ( b r y nh il d_ NN _1 , m y t h _
Revised: NN _1 ) ( b r y nh il d_ NN _1 , m y t h _

Original: NN _1 ) ( t r o t _
Revised: NN _1 ) ( t r o t _

Original: VB _2 , r i d e _ho rs e ba
Revised: VB _2 , r i d e _ho rs e ba

Original: ck
Revised: ck

Original: _ VB _1 ) ( f r i e nd _o f _ t h e
Revised: _ VB _1 ) ( f r i e nd _o f _ t h e

Original: _ c ou r t _
Revised: _ c ou r t _

Original: NN _1 , w a r _ m a c h i n e _
Revised: NN _1 , w a r _ m a c h i n e _

Original: N N _1 ) ( s a i n t _
Revised: N N _1 ) ( s a i n t _

Original: VB _2 , c a t h o li c _ c hu r c h _
Revised: VB _2 , c a t h o li c _ c hu r c h _

Original: NN _1 ) ( a ss i s t _
Revised: NN _1 ) ( a ss i s t _

Original: NN _2 , a m .
Revised: NN _2 , a m .

Original: _ f oo t ba ll _ ga m e _
Revised: _ f oo t ba ll _ ga m e _

Original: NN _1 ) ( p r an ce _ VB _4 , r i d e _ho
Revised: NN _1 ) ( p r an ce _ VB _4 , r i d e _ho

Original: rs e ba ck
Revised: rs e ba ck

Original: _ VB _1 )
Revised: _ VB _1 )

Original: ( m u lti v .
Revised: ( m u lti v .

Original: _a n a l
Revised: _a n a l

Original: y s i s _
Revised: y s i s _

Original: NN _1 , app li e d_ m a t h _
Revised: NN _1 , app li e d_ m a t h _

Original: NN _ 1 ) ( m a l e _
Revised: NN _ 1 ) ( m a l e _

Original: JJ _1 , m a s c u li n e _ JJ _2 ) ( m it z v ah_ NN _2 , hu
Revised: JJ _1 , m a s c u li n e _ JJ _2 ) ( m it z v ah_ NN _2 , hu

Original: m an_a
Revised: m an_a

Original: c ti v it y _
Revised: c ti v it y _

Original: NN _1 ) ( ga ll op_ VB _1 , r i d e _ho rs
Revised: NN _1 ) ( ga ll op_ VB _1 , r i d e _ho rs

Original: e ba ck
Revised: e ba ck

Original: _ VB _1 )
Revised: _ VB _1 )

Original: ( s e ll _
Revised: ( s e ll _

Original: VB _1 , t r a n s a c ti o n _
Revised: VB _1 , t r a n s a c ti o n _

Original: NN _1 ) ( ﬁ r e _
Revised: NN _1 ) ( ﬁ r e _

Original: VB _3 , z oo l og y _ NN _2 ) ( d r i ve _ NN _ 12 , d r i ve _ VB _8 ) ( b r y nh il d_ NN _1 , m y t
Revised: VB _3 , z oo l og y _ NN _2 ) ( d r i ve _ NN _ 12 , d r i ve _ VB _8 ) ( b r y nh il d_ NN _1 , m y t

Original: h
Revised: h

Original: o l og y _
Revised: o l og y _

Original: NN _2 ) ( d r a w _ VB _6 , r e p r e s e n t _ VB _9 ) ( s e ll _
Revised: NN _2 ) ( d r a w _ VB _6 , r e p r e s e n t _ VB _9 ) ( s e ll _

Original: VB _1 , s e ll _ VB _1 ) ( r e l a ti o n s h i p _ NN _4 , b i o l og y _ NN _1 ) ( d r i ve _ NN _ 12 , bad m i n t o n _ NN _ )
Revised: VB _1 , s e ll _ VB _1 ) ( r e l a ti o n s h i p _ NN _4 , b i o l og y _ NN _1 ) ( d r i ve _ NN _ 12 , bad m i n t o n _ NN _ )

Original: [Table]
Revised: [Table]

Original: l e 11 : “
Revised: l e 11 : “

Original: O t h e r ” f ac
Revised: O t h e r ” f ac

Original: [Table]
Revised: [Table]

Original: a s p
Revised: a s p

Original: [Table]
Revised: [Table]

Original: c t
Revised: c t

Original: [Table]
Revised: [Table]

Original: i s t
Revised: i s t

Original: M u
Revised: M u

Original: [Table] [Table]
Revised: [Table] [Table]

Original: ti on (
Revised: ti on (

Original: [Table]
Revised: [Table]

Original: yp e ) σ ( φ ( e s , r , e o ) ) ≈
Revised: yp e ) σ ( φ ( e s , r , e o ) ) ≈

Original: [Table] [Table]
Revised: [Table] [Table]

Original: σ ( φ (
Revised: σ ( φ (

Original: [Table]
Revised: [Table]

Original: s , r , e o ) )
Revised: s , r , e o ) )

Original: [Table]
Revised: [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: r i
Revised: r i

Original: [Table]
Revised: [Table]

Original: l
Revised: l

Original: [Table]
Revised: [Table]

Original: t
Revised: t

Original: [Table]
Revised: [Table]

Original: f
Revised: f

Original: [Table] [Table]
Revised: [Table] [Table]

Original: ) ( s t ag _ VB
Revised: ) ( s t ag _ VB

Original: [Table]
Revised: [Table]

Original: , un
Revised: , un

Original: [Table]
Revised: [Table]

Original: ve r
Revised: ve r

Original: [Table]
Revised: [Table]

Original: r k_ NN _
Revised: r k_ NN _

Original: [Table] [Table]
Revised: [Table] [Table]

Original: i
Revised: i

Original: [Table]
Revised: [Table]

Original: NN
Revised: NN

Original: [Table]
Revised: [Table]

Original: , s t e w _
Revised: , s t e w _

Original: NN
Revised: NN

Original: [Table] [Table]
Revised: [Table] [Table]

Original: NN
Revised: NN

Original: [Table] [Table]
Revised: [Table] [Table]

Original: NN
Revised: NN

Original: [Table]
Revised: [Table]

Original: ( p r i n t
Revised: ( p r i n t

Original: [Table]
Revised: [Table]

Original: VB _4 , p u b li
Revised: VB _4 , p u b li

Original: [Table]
Revised: [Table]

Original: h e r _ NN _2 ) ( e xpo s e
Revised: h e r _ NN _2 ) ( e xpo s e

Original: [Table]
Revised: [Table]

Original: VB _3 , s
Revised: VB _3 , s

Original: [Table]
Revised: [Table]

Original: o
Revised: o

Original: [Table]
Revised: [Table]

Original: NN _
Revised: NN _

Original: [Table]
Revised: [Table]

Original: ( ﬁn i s h _ VB _
Revised: ( ﬁn i s h _ VB _

Original: [Table]
Revised: [Table]

Original: , ﬁn i s
Revised: , ﬁn i s

Original: [Table]
Revised: [Table]

Original: i
Revised: i

Original: [Table]
Revised: [Table]

Original: g_ NN _
Revised: g_ NN _

Original: [Table] [Table]
Revised: [Table] [Table]

Original: og
Revised: og

Original: [Table]
Revised: [Table]

Original: NN
Revised: NN

Original: [Table] [Table]
Revised: [Table] [Table]

Original: ( c r i e r _
Revised: ( c r i e r _

Original: NN _3 , p it
Revised: NN _3 , p it

Original: [Table]
Revised: [Table]

Original: h m an_ NN _2 ) ( s y s t
Revised: h m an_ NN _2 ) ( s y s t

Original: e m _ NN _9 ,
Revised: e m _ NN _9 ,

Original: [Table]
Revised: [Table]

Original: r d e r li n e ss _ NN _1 ) ( p l a y _
Revised: r d e r li n e ss _ NN _1 ) ( p l a y _

Original: VB _26 , p l a
Revised: VB _26 , p l a

Original: [Table]
Revised: [Table]

Original: i n g_ NN _3 ) ( r e s ec t _ VB _ 1 , a m p u t a ti o n _
Revised: i n g_ NN _3 ) ( r e s ec t _ VB _ 1 , a m p u t a ti o n _

Original: NN _ 2 ) ( p l a y _
Revised: NN _ 2 ) ( p l a y _

Original: VB _26 , t u r n _
Revised: VB _26 , t u r n _

Original: NN _ ) ( s p r e ad_ NN _4 , s t
Revised: NN _ ) ( s p r e ad_ NN _4 , s t

Original: r e w _ VB _1 ) ( ce n t r a li z a ti o n _ NN _1 , un it e _ VB _6 ) ( n u t r iti o n_ NN _ 3 , m an _ NN _4 ) ( c oun t _ VB _4 , r ec it e _ VB _2 ) ( t a ke _do w n _ VB _2 , pu t _ VB _2 ) ( ex i s t
Revised: r e w _ VB _1 ) ( ce n t r a li z a ti o n _ NN _1 , un it e _ VB _6 ) ( n u t r iti o n_ NN _ 3 , m an _ NN _4 ) ( c oun t _ VB _4 , r ec it e _ VB _2 ) ( t a ke _do w n _ VB _2 , pu t _ VB _2 ) ( ex i s t

Original: e n ce _ NN _1 , li v i ng_ NN _3 ) ( s a i n t _
Revised: e n ce _ NN _1 , li v i ng_ NN _3 ) ( s a i n t _

Original: NN _3 , s a n c ti f y _ VB _ ) ( v i v i d n e ss _
Revised: NN _3 , s a n c ti f y _ VB _ ) ( v i v i d n e ss _

Original: NN _2 , i m b u e _
Revised: NN _2 , i m b u e _

Original: VB _3 ) ( w r e s tl e _ VB _4 , w r e s tl
Revised: VB _3 ) ( w r e s tl e _ VB _4 , w r e s tl

Original: e r _
Revised: e r _

Original: NN _1 ) ( m ou t h
Revised: NN _1 ) ( m ou t h

Original: _ VB _3 , s a ss i ng_ NN _1 ) ( r i g h t _ ﬁ e l d e r _ NN _1 , l e ft ﬁ e l d_ NN _ 1 ) ( s e a_ m e w _
Revised: _ VB _3 , s a ss i ng_ NN _1 ) ( r i g h t _ ﬁ e l d e r _ NN _1 , l e ft ﬁ e l d_ NN _ 1 ) ( s e a_ m e w _

Original: NN _1 , l a r u s _
Revised: NN _1 , l a r u s _

Original: NN _1 ) ( a u t o t r .
Revised: NN _1 ) ( a u t o t r .

Original: _o r ga
Revised: _o r ga

Original: n i s m _
Revised: n i s m _

Original: NN _1 , e p i p
Revised: NN _1 , e p i p

Original: h
Revised: h

Original: y ti c _JJ_ 1 ) ( c on s t
Revised: y ti c _JJ_ 1 ) ( c on s t

Original: e
Revised: e

Original: ll a ti on _ NN _2 , s t a r _
Revised: ll a ti on _ NN _2 , s t a r _

Original: NN _1 ) ( li s t _
Revised: NN _1 ) ( li s t _

Original: VB _ 4 , s l op e _
Revised: VB _ 4 , s l op e _

Original: NN _ 2 ) ( a l k a li _ NN _2 , a c i d if y _ VB _2 )
Revised: NN _ 2 ) ( a l k a li _ NN _2 , a c i d if y _ VB _2 )

Original: ( d u e l _
Revised: ( d u e l _

Original: VB _1 , s l u g f e s t _
Revised: VB _1 , s l u g f e s t _

Original: NN _1 ) ( p r i n t _
Revised: NN _1 ) ( p r i n t _

Original: VB _4 , p u b li s h i n g_
Revised: VB _4 , p u b li s h i n g_

Original: h
Revised: h

Original: o u s e _
Revised: o u s e _

Original: NN _ 1 ) ( li e u t e n an cy _ NN _1 , c ap t a i n _ NN _) ( s e e _
Revised: NN _ 1 ) ( li e u t e n an cy _ NN _1 , c ap t a i n _ NN _) ( s e e _

Original: VB _17 , und e rs t and_ VB _2 ) ( v o c a l _ NN _2 , r o ck _ s t a r _ NN _1 ) ( p u zz l e _ VB _2 , s ec r e t _
Revised: VB _17 , und e rs t and_ VB _2 ) ( v o c a l _ NN _2 , r o ck _ s t a r _ NN _1 ) ( p u zz l e _ VB _2 , s ec r e t _

Original: NN _3 ) ( t r e a d _ NN _ 1 , s t e p _ VB _ 7 ) ( s hun
Revised: NN _3 ) ( t r e a d _ NN _ 1 , s t e p _ VB _ 7 ) ( s hun

Original: _ VB _1 , h e dg i ng_ NN _2 ) ( s m e lli n g_
Revised: _ VB _1 , h e dg i ng_ NN _2 ) ( s m e lli n g_

Original: NN _1 , s ce n t _
Revised: NN _1 , s ce n t _

Original: VB _1 ) ( u r ano l og y _
Revised: VB _1 ) ( u r ano l og y _

Original: NN _1 , tt _ NN _ ) ( e x e n t e r a ti o n _ NN _ 1 , e nu c l e a t e _ VB _ ) i n s t a n ce _ hyp e r ny m ( S ) ( w i s c on s i n_
Revised: NN _1 , tt _ NN _ ) ( e x e n t e r a ti o n _ NN _ 1 , e nu c l e a t e _ VB _ ) i n s t a n ce _ hyp e r ny m ( S ) ( w i s c on s i n_

Original: NN _2 , u r ban _ ce n t e r _ NN _ ) ( m i ss i ss i
Revised: NN _2 , u r ban _ ce n t e r _ NN _ ) ( m i ss i ss i

Original: pp i
Revised: pp i

Original: _ r i ve r _
Revised: _ r i ve r _

Original: NN _1 , a m e r i c an_ s t a t e _ NN _ ) ( d ee p _ s ou t h
Revised: NN _1 , a m e r i c an_ s t a t e _ NN _ ) ( d ee p _ s ou t h

Original: _ NN _1 , s ou t h_
Revised: _ NN _1 , s ou t h_

Original: NN _1 ) ( h e l s i n k i _ NN _ 1 , u r b a n _ ce n t e r _
Revised: NN _1 ) ( h e l s i n k i _ NN _ 1 , u r b a n _ ce n t e r _

Original: NN _1 ) ( m a rs
Revised: NN _1 ) ( m a rs

Original: h
Revised: h

Original: a ll _
Revised: a ll _

Original: NN _2 , li e u t e n a n t _g e n e r a l _
Revised: NN _2 , li e u t e n a n t _g e n e r a l _

Original: NN _1 ) ( r . _ e .
Revised: NN _1 ) ( r . _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _1 , c
Revised: NN _1 , c

Original: o mm i ss i on e d_ o f
Revised: o mm i ss i on e d_ o f

Original: ﬁ
Revised: ﬁ

Original: ce
Revised: ce

Original: r _ NN _ ) ( c ap it a l _o f _ ga m b i a _ NN _1 , u r ba n _ ce n t e r _
Revised: r _ NN _ ) ( c ap it a l _o f _ ga m b i a _ NN _1 , u r ba n _ ce n t e r _

Original: NN _1 ) ( t h e _ na z a r e n e _ NN _1 , s a ve _ VB _7 ) ( ab i d j an_ NN _1 , c
Revised: NN _1 ) ( t h e _ na z a r e n e _ NN _1 , s a ve _ VB _7 ) ( ab i d j an_ NN _1 , c

Original: o t e _d ’ i v o i r e _
Revised: o t e _d ’ i v o i r e _

Original: NN _1 ) ( kob e nh a v n _
Revised: NN _1 ) ( kob e nh a v n _

Original: NN _1 , u r ba n _ ce n t e r _
Revised: NN _1 , u r ba n _ ce n t e r _

Original: NN _1 ) ( s ou t h _ w e
Revised: NN _1 ) ( s ou t h _ w e

Original: s t _ a f r i c a _ NN _1 , a f r i c a _ NN _ ) ( i r i s h
Revised: s t _ a f r i c a _ NN _1 , a f r i c a _ NN _ ) ( i r i s h

Original: _ c a p it
Revised: _ c a p it

Original: a l _ NN _1 , u r ba n _ ce n t e r _
Revised: a l _ NN _1 , u r ba n _ ce n t e r _

Original: NN _ 1 ) ( w o r l d _ w a r _ i _ NN _1 , u r ban _ ce n t e r _ NN _ ) ( t h e _ ga m b i a _ NN _1 , a f r i c a _ NN _ ) ( b r and e nbu r g _ NN _1 , u r ban _ ce n t e r _ N N _ ) ( r .
Revised: NN _ 1 ) ( w o r l d _ w a r _ i _ NN _1 , u r ban _ ce n t e r _ NN _ ) ( t h e _ ga m b i a _ NN _1 , a f r i c a _ NN _ ) ( b r and e nbu r g _ NN _1 , u r ban _ ce n t e r _ N N _ ) ( r .

Original: _ e .
Revised: _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _ 1 , i n s p ec t
Revised: NN _ 1 , i n s p ec t

Original: o r _g e n e r a l _
Revised: o r _g e n e r a l _

Original: NN _1 ) ( s t .
Revised: NN _1 ) ( s t .

Original: _ pau l _
Revised: _ pau l _

Original: NN _2 , ev ang e li s t _
Revised: NN _2 , ev ang e li s t _

Original: NN _2 ) ( ti r i
Revised: NN _2 ) ( ti r i

Original: c h
Revised: c h

Original: m i r _
Revised: m i r _

Original: NN _1 , u r ban _ ce n t e r _ NN _ ) ( s i e rr a_n ev ada_ NN _1 , u r ban_ ce n t e r _
Revised: NN _1 , u r ban _ ce n t e r _ NN _ ) ( s i e rr a_n ev ada_ NN _1 , u r ban_ ce n t e r _

Original: NN _ 1 ) ( r . _ e .
Revised: NN _ 1 ) ( r . _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _ 1 , c h i e f _
Revised: NN _ 1 , c h i e f _

Original: o f _ s t a ff _ NN _ 1 ) ( d ee p _ s ou t h
Revised: o f _ s t a ff _ NN _ 1 ) ( d ee p _ s ou t h

Original: _ NN _1 , u r ban _ c e n t e r _ NN _ ) ( r .
Revised: _ NN _1 , u r ban _ c e n t e r _ NN _ ) ( r .

Original: _ e .
Revised: _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _1 , m ilit a r y _ad v i s o r _ NN _1 ) ( m a l c o l m _x_
Revised: NN _1 , m ilit a r y _ad v i s o r _ NN _1 ) ( m a l c o l m _x_

Original: NN _1 , e m a n c i pa ti o n i s t
Revised: NN _1 , e m a n c i pa ti o n i s t

Original: _ NN _ 1 ) ( ce n t r a l _a m
Revised: _ NN _ 1 ) ( ce n t r a l _a m

Original: e
Revised: e

Original: r
Revised: r

Original: i c a _
Revised: i c a _

Original: NN _1 , c .
Revised: NN _1 , c .

Original: _a m .
Revised: _a m .

Original: _ na ti o n_
Revised: _ na ti o n_

Original: NN _ 1 ) ( nup t s e _ NN _1 , u r ban _ c e n t
Revised: NN _ 1 ) ( nup t s e _ NN _1 , u r ban _ c e n t

Original: e r _ NN _ ) ( r .
Revised: e r _ NN _ ) ( r .

Original: _ e .
Revised: _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _1 , a i d e - d e - c a m p _ NN _ ) ( no r t h_ p l a tt e _ r
Revised: NN _1 , a i d e - d e - c a m p _ NN _ ) ( no r t h_ p l a tt e _ r

Original: i ve r _
Revised: i ve r _

Original: NN _1 , u r ban _ ce n t e r _ NN _ ) ( m a l c o l m _ x _
Revised: NN _1 , u r ban _ ce n t e r _ NN _ ) ( m a l c o l m _ x _

Original: NN _1 , e n v i r on m e n t a li s t _ NN _ 1 ) ( ti c i no _ NN _1 , u r ban _ c e n t e r _ NN _ ) ( t a m pa_ba y _
Revised: NN _1 , e n v i r on m e n t a li s t _ NN _ 1 ) ( ti c i no _ NN _1 , u r ban _ c e n t e r _ NN _ ) ( t a m pa_ba y _

Original: NN _1 , u r ban_
Revised: NN _1 , u r ban_

Original: ce n t e r _
Revised: ce n t e r _

Original: NN _1 ) ( o s l o_
Revised: NN _1 ) ( o s l o_

Original: NN _1 , u r ba n _ ce n t e r _
Revised: NN _1 , u r ba n _ ce n t e r _

Original: NN _1 ) ( t h e _ n a z a r e n e _ NN _ 1 , c h r
Revised: NN _1 ) ( t h e _ n a z a r e n e _ NN _ 1 , c h r

Original: i s ti a n _ J J_ 1 ) ( a e g e an_ s e a _ NN _1 , a e g e an _
Revised: i s ti a n _ J J_ 1 ) ( a e g e an_ s e a _ NN _1 , a e g e an _

Original: i s l and _ NN _ ) ( ti d e w a t e r _ r
Revised: i s l and _ NN _ ) ( ti d e w a t e r _ r

Original: e
Revised: e

Original: g
Revised: g

Original: i on_
Revised: i on_

Original: NN _1 , s ou t h_
Revised: NN _1 , s ou t h_

Original: NN _1 ) ( z a i r e _ r i ve r _
Revised: NN _1 ) ( z a i r e _ r i ve r _

Original: NN _1 , u r ban _ ce n t e r _ N N _ ) ( t h o m a s _aqu i na s _
Revised: NN _1 , u r ban _ ce n t e r _ N N _ ) ( t h o m a s _aqu i na s _

Original: NN _ 1 , c hu r c h_ f a t
Revised: NN _ 1 , c hu r c h_ f a t

Original: h e r _
Revised: h e r _

Original: NN _1 ) ( c o w p e n s _
Revised: NN _1 ) ( c o w p e n s _

Original: NN _1 , w a r _o f _a m .
Revised: NN _1 , w a r _o f _a m .

Original: _ i nd .
Revised: _ i nd .

Original: _ NN _1 ) ( r .
Revised: _ NN _1 ) ( r .

Original: _ e .
Revised: _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _1 , e x ec u ti ve _o f ﬁ ce r _
Revised: NN _1 , e x ec u ti ve _o f ﬁ ce r _

Original: NN _1 ) ( t r an s y l v an i an_a l p s _ NN _1 , u r ban _ ce n t e r _ NN _ ) ( t h e _ na z a r e n e _ NN _1 , e l _ n i n o_
Revised: NN _1 ) ( t r an s y l v an i an_a l p s _ NN _1 , u r ban _ ce n t e r _ NN _ ) ( t h e _ na z a r e n e _ NN _1 , e l _ n i n o_

Original: NN _2 ) s yn s e t _ do m a i n_ t op i
Revised: NN _2 ) s yn s e t _ do m a i n_ t op i

Original: c _o f ( C ) ( li m it a ti on_
Revised: c _o f ( C ) ( li m it a ti on_

Original: NN _4 , t r a mm e l _
Revised: NN _4 , t r a mm e l _

Original: VB _2 ) ( r o ll - o n _ r o llo ff _
Revised: VB _2 ) ( r o ll - o n _ r o llo ff _

Original: NN _1 , t r a n s po r t _ NN _1 ) ( e t y m o l og i
Revised: NN _1 , t r a n s po r t _ NN _1 ) ( e t y m o l og i

Original: z i n g_
Revised: z i n g_

Original: NN _1 , e xp l a n a ti o n _
Revised: NN _1 , e xp l a n a ti o n _

Original: NN _1 ) ( ﬂ a t _
Revised: NN _1 ) ( ﬂ a t _

Original: JJ _ 5 , m a tt e _
Revised: JJ _ 5 , m a tt e _

Original: NN _2 ) ( li gh t _ c o l on e l _ NN _1 , c
Revised: NN _2 ) ( li gh t _ c o l on e l _ NN _1 , c

Original: o l on e l _
Revised: o l on e l _

Original: NN _1 ) ( h
Revised: NN _1 ) ( h

Original: i
Revised: i

Original: z
Revised: z

Original: b_ u t - t
Revised: b_ u t - t

Original: ah r i r _
Revised: ah r i r _

Original: NN _1 , a s i a_
Revised: NN _1 , a s i a_

Original: NN _1 ) (f e rr y _ VB _3 , t r a ve l _ VB _ ) ( e t y m o l o g
Revised: NN _1 ) (f e rr y _ VB _3 , t r a ve l _ VB _ ) ( e t y m o l o g

Original: i z
Revised: i z

Original: i ng _ NN _1 , d e r i ve _
Revised: i ng _ NN _1 , d e r i ve _

Original: VB _ 3 ) ( nu rs e _ VB _1 , nu rs i n g_
Revised: VB _ 3 ) ( nu rs e _ VB _1 , nu rs i n g_

Original: NN _1 ) ( s l u gg e r _ NN _1 , s o f t ba ll _ga m e _
Revised: NN _1 ) ( s l u gg e r _ NN _1 , s o f t ba ll _ga m e _

Original: NN _1 ) ( pub li c _p r o s ec u t
Revised: NN _1 ) ( pub li c _p r o s ec u t

Original: o r _ NN _1 , p r o s ec
Revised: o r _ NN _1 , p r o s ec

Original: u t e _
Revised: u t e _

Original: VB _2 ) ( h o l e _ o u t _
Revised: VB _2 ) ( h o l e _ o u t _

Original: VB _ 1 , h o l e _
Revised: VB _ 1 , h o l e _

Original: NN _ 3 ) ( s e r m on_ NN _1 , p r oph
Revised: NN _ 3 ) ( s e r m on_ NN _1 , p r oph

Original: e s y _
Revised: e s y _

Original: VB _2 ) ( s e r m on_ NN _1 , s e r m on i z
Revised: VB _2 ) ( s e r m on_ NN _1 , s e r m on i z

Original: e _
Revised: e _

Original: VB _1 ) ( a l c h
Revised: VB _1 ) ( a l c h

Original: e
Revised: e

Original: m i z e _
Revised: m i z e _

Original: VB _1 , m od if y _
Revised: VB _1 , m od if y _

Original: VB _3 ) ( r e l a ti o n s h i p _ NN _ 4 , r e l a ti o n _
Revised: VB _3 ) ( r e l a ti o n s h i p _ NN _ 4 , r e l a ti o n _

Original: NN _ 1 ) ( li b e l _
Revised: NN _ 1 ) ( li b e l _

Original: NN _1 , p r a c ti ce _
Revised: NN _1 , p r a c ti ce _

Original: o f _
Revised: o f _

Original: l a w _ NN _1 ) ( d r a w _ VB _6 , d r a w e r _ NN _3 ) ( li b e l _
Revised: l a w _ NN _1 ) ( d r a w _ VB _6 , d r a w e r _ NN _3 ) ( li b e l _

Original: NN _1 , li b e l _ VB _1 ) ( d r i ve _ NN _1 2 , ba
Revised: NN _1 , li b e l _ VB _1 ) ( d r i ve _ NN _1 2 , ba

Original: d m i n t o n _
Revised: d m i n t o n _

Original: NN _ 1 ) ( s l u gg e r _ NN _1 , ba s e ba ll _p l a ye r _ NN _1 ) ( t u r n _
Revised: NN _ 1 ) ( s l u gg e r _ NN _1 , ba s e ba ll _p l a ye r _ NN _1 ) ( t u r n _

Original: VB _12 , p l o w _
Revised: VB _12 , p l o w _

Original: NN _1 ) ( t u r n_
Revised: NN _1 ) ( t u r n_

Original: VB _12 , till _ VB _1 ) ( e t y m o l o g i z i n g_ NN _1 , e t y m o l o g i z
Revised: VB _12 , till _ VB _1 ) ( e t y m o l o g i z i n g_ NN _1 , e t y m o l o g i z

Original: e _ VB _2 ) ( r n a_
Revised: e _ VB _2 ) ( r n a_

Original: NN _1 , c
Revised: NN _1 , c

Original: h e m i s t
Revised: h e m i s t

Original: r
Revised: r

Original: y
Revised: y

Original: NN _1 ) ( a ss i s t _
Revised: NN _1 ) ( a ss i s t _

Original: NN _2 , s o f t ba ll _ga m e _
Revised: NN _2 , s o f t ba ll _ga m e _

Original: NN _1 ) ( h it _
Revised: NN _1 ) ( h it _

Original: NN _1 , h it _ VB _1 ) ( m a t
Revised: NN _1 , h it _ VB _1 ) ( m a t

Original: r
Revised: r

Original: i x _ a l g e b r a_ NN _1 , d i a g on a li z a ti on _
Revised: i x _ a l g e b r a_ NN _1 , d i a g on a li z a ti on _

Original: NN _ 1 ) ( m e t r if y _ VB _1 , v e rs if y _
Revised: NN _ 1 ) ( m e t r if y _ VB _1 , v e rs if y _

Original: VB _1 ) ( c o un c il _
Revised: VB _1 ) ( c o un c il _

Original: NN _2 , a ss e m b l y _ NN _4 ) ( ﬁ r e _
Revised: NN _2 , a ss e m b l y _ NN _4 ) ( ﬁ r e _

Original: VB _3 , ﬂ a m i n g _ NN _1 ) ( c ab i n e t w o r k _ NN _ 2 , w o od w o r k i n g_
Revised: VB _3 , ﬂ a m i n g _ NN _1 ) ( c ab i n e t w o r k _ NN _ 2 , w o od w o r k i n g_

Original: NN _ 1 ) ( t r i a l _ i m p r e ss i on_
Revised: NN _ 1 ) ( t r i a l _ i m p r e ss i on_

Original: NN _1 , pub li s h_
Revised: NN _1 , pub li s h_

Original: VB _1 ) ( t
Revised: VB _1 ) ( t

Original: h r o u g h p u t _
Revised: h r o u g h p u t _

Original: NN _1 , t u r n o u t _
Revised: NN _1 , t u r n o u t _

Original: NN _4 ) ( r i ng _ NN _4 , c
Revised: NN _4 ) ( r i ng _ NN _4 , c

Original: h e m i c a l _
Revised: h e m i c a l _

Original: c ha i n_ NN _1 ) ( c a b i n e t w o r k _ NN _2 , b o tt o m _ VB _ 1 ) ( t u r n _
Revised: c ha i n_ NN _1 ) ( c a b i n e t w o r k _ NN _2 , b o tt o m _ VB _ 1 ) ( t u r n _

Original: VB _12 , p l o w m a n _
Revised: VB _12 , p l o w m a n _

Original: NN _1 ) ( c r e a m _ VB _1 , c
Revised: NN _1 ) ( c r e a m _ VB _1 , c

Original: r e a m _
Revised: r e a m _

Original: NN _2 ) ( li b i d i n a l _ e n e r g y _
Revised: NN _2 ) ( li b i d i n a l _ e n e r g y _

Original: NN _1 , c h a r g e _
Revised: NN _1 , c h a r g e _

Original: NN _ 9 ) ( c ab i n e t w o r k _ NN _ 2 , up ho l s t e r _ VB _1 )
Revised: NN _ 9 ) ( c ab i n e t w o r k _ NN _ 2 , up ho l s t e r _ VB _1 )

Original: [Table]
Revised: [Table]

Original: n
Revised: n

Original: [Table]
Revised: [Table]

Original: NN
Revised: NN

Original: [Table]
Revised: [Table]

Original: NN
Revised: NN

Original: [Table]
Revised: [Table]

Original: ( l o ve _
Revised: ( l o ve _

Original: VB _3 , l o ve r _
Revised: VB _3 , l o ve r _

Original: NN _2 ) ( da t e _
Revised: NN _2 ) ( da t e _

Original: VB _5 , g e
Revised: VB _5 , g e

Original: o l og i c a l _da ti n g_
Revised: o l og i c a l _da ti n g_

Original: NN _1 ) ( m u s i c a l _
Revised: NN _1 ) ( m u s i c a l _

Original: i n s t r u m e n t _ NN _1 , w r it e _ VB _6 ) ( t r e m b l e r _
Revised: i n s t r u m e n t _ NN _1 , w r it e _ VB _6 ) ( t r e m b l e r _

Original: NN _1 , v i b r a t e _
Revised: NN _1 , v i b r a t e _

Original: VB _1 ) ( s nu gg l e _
Revised: VB _1 ) ( s nu gg l e _

Original: NN _1 , s q u ee z e _ VB _ ) ( s e t _
Revised: NN _1 , s q u ee z e _ VB _ ) ( s e t _

Original: VB _23 , e m b l e m _ NN _ ) ( li n i n g _ NN _ 3 , w r ap_ u p_
Revised: VB _23 , e m b l e m _ NN _ ) ( li n i n g _ NN _ 3 , w r ap_ u p_

Original: VB _1 ) ( m o ti v a t o r _
Revised: VB _1 ) ( m o ti v a t o r _

Original: NN _1 , t r i p_ VB _4 ) ( d e pa r t e d_
Revised: NN _1 , t r i p_ VB _4 ) ( d e pa r t e d_

Original: NN _1 , d i e _ VB _2 ) ( t y r o_
Revised: NN _1 , d i e _ VB _2 ) ( t y r o_

Original: NN _1 , s t a r t _
Revised: NN _1 , s t a r t _

Original: VB _5 ) ( s c r
Revised: VB _5 ) ( s c r

Original: ap
Revised: ap

Original: _ VB _2 , s t r u gg l e _ NN _2 ) ( s u ppo r t _ VB _6 , i n do rs e m e n t _
Revised: _ VB _2 , s t r u gg l e _ NN _2 ) ( s u ppo r t _ VB _6 , i n do rs e m e n t _

Original: NN _1 ) ( po s iti o n _ VB _1 , p l a ce m e n t _
Revised: NN _1 ) ( po s iti o n _ VB _1 , p l a ce m e n t _

Original: NN _1 ) ( i d e n ti ﬁ c a ti o n _ NN _1 , n a m e _ VB _5 ) ( t ap e _
Revised: NN _1 ) ( i d e n ti ﬁ c a ti o n _ NN _1 , n a m e _ VB _5 ) ( t ap e _

Original: VB _3 , t ap e _ r ec o r d e r _
Revised: VB _3 , t ap e _ r ec o r d e r _

Original: NN _1 ) (f e d e r a t e _
Revised: NN _1 ) (f e d e r a t e _

Original: VB _2 , c o n f e d e r a ti o n _ NN _ 1 ) ( r e p e n t an t _ JJ _1 , r e p e n t an t _ JJ _1 ) ( s t abb
Revised: VB _2 , c o n f e d e r a ti o n _ NN _ 1 ) ( r e p e n t an t _ JJ _1 , r e p e n t an t _ JJ _1 ) ( s t abb

Original: e
Revised: e

Original: r _ NN _1 , t h r u s t _
Revised: r _ NN _1 , t h r u s t _

Original: VB _5 ) ( v i n d i c a t e _
Revised: VB _5 ) ( v i n d i c a t e _

Original: VB _2 , j u s ti ﬁ c
Revised: VB _2 , j u s ti ﬁ c

Original: a
Revised: a

Original: ti o n _
Revised: ti o n _

Original: NN _2 ) ( t ak e _ o ve r _
Revised: NN _2 ) ( t ak e _ o ve r _

Original: VB _6 , r e t u r n _
Revised: VB _6 , r e t u r n _

Original: NN _ 7 ) ( t r e ad _ NN _ 1 , s t e p _ VB _ ) ( j u s ti ﬁ c
Revised: NN _ 7 ) ( t r e ad _ NN _ 1 , s t e p _ VB _ ) ( j u s ti ﬁ c

Original: a
Revised: a

Original: ti on _ NN _1 , apo l og y _ NN _ ) ( l e a c h i n g_
Revised: ti on _ NN _1 , apo l og y _ NN _ ) ( l e a c h i n g_

Original: NN _1 , p e r c o l a t e _ VB _3 ) ( w a it _ o n _ VB _1 , s u ppo r t e r _
Revised: NN _1 , p e r c o l a t e _ VB _3 ) ( w a it _ o n _ VB _1 , s u ppo r t e r _

Original: NN _ 3 ) ( s t
Revised: NN _ 3 ) ( s t

Original: o ck
Revised: o ck

Original: i s t _ NN _1 , s t
Revised: i s t _ NN _1 , s t

Original: o ck
Revised: o ck

Original: i s t _
Revised: i s t _

Original: NN _1 ) ( m a nu f a c t
Revised: NN _1 ) ( m a nu f a c t

Original: u
Revised: u

Original: r
Revised: r

Original: e
Revised: e

Original: _ VB _1 , p r ev a r
Revised: _ VB _1 , p r ev a r

Original: i c a ti o n _
Revised: i c a ti o n _

Original: NN _1 ) ( s y n c h r o n i
Revised: NN _1 ) ( s y n c h r o n i

Original: z
Revised: z

Original: e _ VB _2 , s y n c
Revised: e _ VB _2 , s y n c

Original: h r o s c op e
Revised: h r o s c op e

Original: _ NN _1 ) ( d e n o t e _ VB _3 , p r o m u l ga ti o n _
Revised: _ NN _1 ) ( d e n o t e _ VB _3 , p r o m u l ga ti o n _

Original: NN _1 ) ( ph il an t h r op i s t _ NN _1 , ph il an t h r op i s t _
Revised: NN _1 ) ( ph il an t h r op i s t _ NN _1 , ph il an t h r op i s t _

Original: NN _1 ) i n s t a n ce _ hyp e
Revised: NN _1 ) i n s t a n ce _ hyp e

Original: r ny m ( S ) ( d ee p _
Revised: r ny m ( S ) ( d ee p _

Original: s ou t h
Revised: s ou t h

Original: _ NN _1 , s ou t h_
Revised: _ NN _1 , s ou t h_

Original: NN _1 ) ( t ho m a s _aqu i na s _
Revised: NN _1 ) ( t ho m a s _aqu i na s _

Original: NN _1 , b i s hop _ NN _ ) ( c o w p e n s _
Revised: NN _1 , b i s hop _ NN _ ) ( c o w p e n s _

Original: NN _1 , s i e g e _
Revised: NN _1 , s i e g e _

Original: NN _1 ) ( r . _ e .
Revised: NN _1 ) ( r . _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _1 , s i e g e _
Revised: NN _1 , s i e g e _

Original: NN _1 ) ( s t .
Revised: NN _1 ) ( s t .

Original: _ pau l _
Revised: _ pau l _

Original: NN _2 , o r gan i s t _
Revised: NN _2 , o r gan i s t _

Original: NN _1 ) ( i r i s h
Revised: NN _1 ) ( i r i s h

Original: c
Revised: c

Original: ap it
Revised: ap it

Original: a l _ NN _1 , u r ba n _ ce n t e r _
Revised: a l _ NN _1 , u r ba n _ ce n t e r _

Original: NN _1 ) ( m cc o r
Revised: NN _1 ) ( m cc o r

Original: m
Revised: m

Original: i ck _ NN _1 , a r m s _ m anu f a c t u r e r _
Revised: i ck _ NN _1 , a r m s _ m anu f a c t u r e r _

Original: NN _1 ) ( s h a w _ NN _ 3 , w o m e n ’ s _ r
Revised: NN _1 ) ( s h a w _ NN _ 3 , w o m e n ’ s _ r

Original: i g
Revised: i g

Original: h ti s t _ NN _1 ) ( h e l s i n k i _ NN _1 , u r ba n _ ce n t e r _
Revised: h ti s t _ NN _1 ) ( h e l s i n k i _ NN _1 , u r ba n _ ce n t e r _

Original: NN _1 ) ( t h o m a s _ t h e _apo s tl e _ NN _1 , apo s tl e _
Revised: NN _1 ) ( t h o m a s _ t h e _apo s tl e _ NN _1 , apo s tl e _

Original: NN _2 ) ( t ho m a s _ t h e _apo s tl e _ NN _1 , ev a ng e li s t _ NN _ ) ( a e g e an_ s e a_
Revised: NN _2 ) ( t ho m a s _ t h e _apo s tl e _ NN _1 , ev a ng e li s t _ NN _ ) ( a e g e an_ s e a_

Original: NN _1 , a e g e an _
Revised: NN _1 , a e g e an _

Original: i s l and _ NN _ ) ( m a l c o l m _x_
Revised: i s l and _ NN _ ) ( m a l c o l m _x_

Original: NN _1 , e m a n c i pa ti o n i s t
Revised: NN _1 , e m a n c i pa ti o n i s t

Original: _ NN _ 1 ) ( s t .
Revised: _ NN _ 1 ) ( s t .

Original: _ pau l _
Revised: _ pau l _

Original: NN _2 , apo s tl e _
Revised: NN _2 , apo s tl e _

Original: NN _3 ) ( m cc o r
Revised: NN _3 ) ( m cc o r

Original: m
Revised: m

Original: i
Revised: i

Original: c k_
Revised: c k_

Original: NN _1 , t ec hn
Revised: NN _1 , t ec hn

Original: o l
Revised: o l

Original: og i s t _
Revised: og i s t _

Original: NN _1 ) ( t ho m a s _aqu i na s _
Revised: NN _1 ) ( t ho m a s _aqu i na s _

Original: NN _1 , c hu r c h_ f a t
Revised: NN _1 , c hu r c h_ f a t

Original: h e r _
Revised: h e r _

Original: NN _1 ) ( t ho m a s _ t h e _apo s tl e _ NN _1 , c hu r c h_
Revised: NN _1 ) ( t ho m a s _ t h e _apo s tl e _ NN _1 , c hu r c h_

Original: f a t
Revised: f a t

Original: h e r _ N
Revised: h e r _ N

Original: N _1 ) ( m cc
Revised: N _1 ) ( m cc

Original: o r m i ck _ NN _1 , pa i n t e r _
Revised: o r m i ck _ NN _1 , pa i n t e r _

Original: NN _1 ) ( s t .
Revised: NN _1 ) ( s t .

Original: _g r e go r y _ i _ NN _ 1 , c hu r c h _
Revised: _g r e go r y _ i _ NN _ 1 , c hu r c h _

Original: f a t h e r _
Revised: f a t h e r _

Original: NN _1 ) ( s t .
Revised: NN _1 ) ( s t .

Original: _g r e go r y _
Revised: _g r e go r y _

Original: o f _ n .
Revised: o f _ n .

Original: NN _1 , s e r m o n
Revised: NN _1 , s e r m o n

Original: i
Revised: i

Original: z
Revised: z

Original: e r _
Revised: e r _

Original: NN _1 ) ( t ho m a s _ t h e _apo s tl e _ NN _1 , t r og l od y t e _ NN _ ) ( r ob e r t _o w e n _ NN _1 , m o v
Revised: NN _1 ) ( t ho m a s _ t h e _apo s tl e _ NN _1 , t r og l od y t e _ NN _ ) ( r ob e r t _o w e n _ NN _1 , m o v

Original: i e _ m a ke r _
Revised: i e _ m a ke r _

Original: NN _1 ) ( m cc o r
Revised: NN _1 ) ( m cc o r

Original: m
Revised: m

Original: i ck _ NN _1 , e l ec t r i c a l _ e ng i n ee r _
Revised: i ck _ NN _1 , e l ec t r i c a l _ e ng i n ee r _

Original: NN _1 ) ( t h e r e s a_ NN _1 , m on k _
Revised: NN _1 ) ( t h e r e s a_ NN _1 , m on k _

Original: NN _1 ) ( m i ss i ss i pp
Revised: NN _1 ) ( m i ss i ss i pp

Original: i
Revised: i

Original: _ r i ve r _
Revised: _ r i ve r _

Original: NN _1 , a m e r i c an_ s t a t e _ NN _ ) ( s t .
Revised: NN _1 , a m e r i c an_ s t a t e _ NN _ ) ( s t .

Original: _ pau l _
Revised: _ pau l _

Original: NN _2 , ph il o s oph e r _
Revised: NN _2 , ph il o s oph e r _

Original: NN _1 ) ( i bn - r
Revised: NN _1 ) ( i bn - r

Original: o s hd_
Revised: o s hd_

Original: NN _1 , p e dagogu e _ NN _1 ) s yn s e t _ do m a i n_ t op i
Revised: NN _1 , p e dagogu e _ NN _1 ) s yn s e t _ do m a i n_ t op i

Original: c _o f ( C ) ( r o ll - o n _ r
Revised: c _o f ( C ) ( r o ll - o n _ r

Original: o llo ff _
Revised: o llo ff _

Original: NN _1 , m o t
Revised: NN _1 , m o t

Original: o r c a r _ NN _1 ) ( d r i ve _ NN _ 12 , bad m i n t o n _ NN _ 1 ) ( li b e l _
Revised: o r c a r _ NN _1 ) ( d r i ve _ NN _ 12 , bad m i n t o n _ NN _ 1 ) ( li b e l _

Original: NN _1 , l e g i s l a t u r e _ NN _1 ) ( r o ll - o n _ r
Revised: NN _1 , l e g i s l a t u r e _ NN _1 ) ( r o ll - o n _ r

Original: o llo ff _
Revised: o llo ff _

Original: NN _1 , pa ss e n g e r _ ve h
Revised: NN _1 , pa ss e n g e r _ ve h

Original: i c l e _
Revised: i c l e _

Original: N N _1 )
Revised: N N _1 )

Original: [Table]
Revised: [Table]

Original: _ NN _1 ) ( li g h ti n g_ NN _4 , s e t _ o n _ ﬁ r e _ VB _1 ) ( s nu f ﬂ e _ VB _3 , w h i n e _
Revised: _ NN _1 ) ( li g h ti n g_ NN _4 , s e t _ o n _ ﬁ r e _ VB _1 ) ( s nu f ﬂ e _ VB _3 , w h i n e _

Original: NN _1 ) ( m ag n it u d e _
Revised: NN _1 ) ( m ag n it u d e _

Original: NN _1 , t a ll _JJ_ 1 ) ( v i o l a t e _ VB _2 , v i
Revised: NN _1 , t a ll _JJ_ 1 ) ( v i o l a t e _ VB _2 , v i

Original: o
Revised: o

Original: l a ti o n _
Revised: l a ti o n _

Original: NN _3 ) ( s y m pha l angu s _
Revised: NN _3 ) ( s y m pha l angu s _

Original: NN _1 , on e - ha lf _ NN _1 ) ( n a s a li z a ti o n _ NN _1 , s o un d _o u t _
Revised: NN _1 , on e - ha lf _ NN _1 ) ( n a s a li z a ti o n _ NN _1 , s o un d _o u t _

Original: VB _1 ) ( t e s ti f y _ VB _2 , i n f o r m a ti o n _
Revised: VB _1 ) ( t e s ti f y _ VB _2 , i n f o r m a ti o n _

Original: NN _1 ) ( s u ff o c a t e _
Revised: NN _1 ) ( s u ff o c a t e _

Original: VB _1 , s t r a n g l e r _ t r ee _
Revised: VB _1 , s t r a n g l e r _ t r ee _

Original: NN _1 ) ( j u s t _
Revised: NN _1 ) ( j u s t _

Original: JJ_3 , v a li d it y _
Revised: JJ_3 , v a li d it y _

Original: NN _1 ) ( t r e ad _ NN _1 , s t e p_
Revised: NN _1 ) ( t r e ad _ NN _1 , s t e p_

Original: VB _7 ) ( c o nn ec t _ VB _6 , c o n ve r g i n g_ NN _1 ) ( nu m b e r _ VB _3 , po i n t _
Revised: VB _7 ) ( c o nn ec t _ VB _6 , c o n ve r g i n g_ NN _1 ) ( nu m b e r _ VB _3 , po i n t _

Original: NN _12 ) ( r e p r o ve _ VB _1 , t a l k
Revised: NN _12 ) ( r e p r o ve _ VB _1 , t a l k

Original: i ng_ t o_
Revised: i ng_ t o_

Original: NN _1 ) ( ye a r n _ VB _1 , p i n i n g _ NN _ ) ( c o nn ec t _ VB _6 , c o nn e x i o n _ NN _ ) ( d eve l op_
Revised: NN _1 ) ( ye a r n _ VB _1 , p i n i n g _ NN _ ) ( c o nn ec t _ VB _6 , c o nn e x i o n _ NN _ ) ( d eve l op_

Original: VB _10 , o r ga n i c _p r
Revised: VB _10 , o r ga n i c _p r

Original: o ce ss _
Revised: o ce ss _

Original: NN _1 ) ( s u s t a i n _ VB _5 , b e a m _ NN _2 ) ( un r e li ab l e n e ss _ NN _1 , a r b it r a r y _
Revised: NN _1 ) ( s u s t a i n _ VB _5 , b e a m _ NN _2 ) ( un r e li ab l e n e ss _ NN _1 , a r b it r a r y _

Original: JJ_1 )
Revised: JJ_1 )

Original: ( op e r a t e _ VB _4 , p s y op
Revised: ( op e r a t e _ VB _4 , p s y op

Original: _ NN _1 ) ( p li c a ti o n _ NN _1 , t w i s t _ VB _4 ) ( s p r i n g_ NN _6 , hu
Revised: _ NN _1 ) ( p li c a ti o n _ NN _1 , t w i s t _ VB _4 ) ( s p r i n g_ NN _6 , hu

Original: r d l e _
Revised: r d l e _

Original: VB _1 ) ( t r a ve s t y _
Revised: VB _1 ) ( t r a ve s t y _

Original: NN _2 , t r a ve s t y _
Revised: NN _2 , t r a ve s t y _

Original: NN _2 ) ( m a r k e t _ VB _1 , t r ad e _
Revised: NN _2 ) ( m a r k e t _ VB _1 , t r ad e _

Original: NN _4 ) ( s p lit _ u p_ VB _3 , s e pa r a ti o n _ NN _5 ) ( s pa r k _ NN _1 , s c i n till a t e _
Revised: NN _4 ) ( s p lit _ u p_ VB _3 , s e pa r a ti o n _ NN _5 ) ( s pa r k _ NN _1 , s c i n till a t e _

Original: VB _1 ) ( s pa r k _ NN _1 , s pa r k l e _ VB _1 ) ( op e r a t e _ VB _4 , m i ss i o n _
Revised: VB _1 ) ( s pa r k _ NN _1 , s pa r k l e _ VB _1 ) ( op e r a t e _ VB _4 , m i ss i o n _

Original: NN _2 ) ( p li c a ti o n _ NN _1 , w r i n k l e _
Revised: NN _2 ) ( p li c a ti o n _ NN _1 , w r i n k l e _

Original: VB _2 ) ( u tilit y _ NN _2 , f un c ti o n a l _JJ_1 ) ( s t
Revised: VB _2 ) ( u tilit y _ NN _2 , f un c ti o n a l _JJ_1 ) ( s t

Original: o ck
Revised: o ck

Original: i s t _ NN _1 , s t
Revised: i s t _ NN _1 , s t

Original: o ck
Revised: o ck

Original: i s t _
Revised: i s t _

Original: NN _1 ) i n s t a n ce _ hyp e
Revised: NN _1 ) i n s t a n ce _ hyp e

Original: r ny m ( S ) ( m a l c o l m _ x _
Revised: r ny m ( S ) ( m a l c o l m _ x _

Original: NN _1 , h i p s t e r _
Revised: NN _1 , h i p s t e r _

Original: NN _1 ) ( ce n t r a l _a m e r
Revised: NN _1 ) ( ce n t r a l _a m e r

Original: i c a_ NN _1 , ce n t r a l _a m e r
Revised: i c a_ NN _1 , ce n t r a l _a m e r

Original: i c a _ NN _1 ) ( t h e r e s a_ NN _1 , m on k _ NN _1 ) ( t h e _ na z a r e n e _ NN _1 , j uda i s m _
Revised: i c a _ NN _1 ) ( t h e r e s a_ NN _1 , m on k _ NN _1 ) ( t h e _ na z a r e n e _ NN _1 , j uda i s m _

Original: NN _2 ) ( s t .
Revised: NN _2 ) ( s t .

Original: _g r e go r y _ i _ NN _1 , c hu r c h _
Revised: _g r e go r y _ i _ NN _1 , c hu r c h _

Original: f a t
Revised: f a t

Original: h e r _ NN _1 ) ( n i ppon_
Revised: h e r _ NN _1 ) ( n i ppon_

Original: NN _1 , e u r op e an_na ti on_
Revised: NN _1 , e u r op e an_na ti on_

Original: NN _ 1 ) ( o l d_ li n e _ s t a t e _ NN _1 , r i ve r _ NN _1 ) ( s ou t h _ k
Revised: NN _ 1 ) ( o l d_ li n e _ s t a t e _ NN _1 , r i ve r _ NN _1 ) ( s ou t h _ k

Original: o r e a _ NN _1 , a f r i c an _na ti on_ NN _1 ) ( g r e a t _ l a ke s _
Revised: o r e a _ NN _1 , a f r i c an _na ti on_ NN _1 ) ( g r e a t _ l a ke s _

Original: NN _1 , r i ve r _ NN _1 ) ( r .
Revised: NN _1 , r i ve r _ NN _1 ) ( r .

Original: _ e .
Revised: _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _1 , c
Revised: NN _1 , c

Original: o mm i ss i on e d_ o f
Revised: o mm i ss i on e d_ o f

Original: ﬁ
Revised: ﬁ

Original: ce
Revised: ce

Original: r
Revised: r

Original: _ NN _1 ) ( m a l c o l m _ x _
Revised: _ NN _1 ) ( m a l c o l m _ x _

Original: NN _1 , pa ss i ve _ r e
Revised: NN _1 , pa ss i ve _ r e

Original: s i s t e r _ NN _1 ) ( r .
Revised: s i s t e r _ NN _1 ) ( r .

Original: _ e .
Revised: _ e .

Original: _ b y r d_
Revised: _ b y r d_

Original: NN _1 , n o n c
Revised: NN _1 , n o n c

Original: o mm i ss i o n e d _
Revised: o mm i ss i o n e d _

Original: o f ﬁ ce r _
Revised: o f ﬁ ce r _

Original: NN _1 ) ( s ou t h _ k
Revised: NN _1 ) ( s ou t h _ k

Original: o r e a _ NN _1 , p e n i n s u l a_ NN _1 ) ( m a l c o l m _ x _
Revised: o r e a _ NN _1 , p e n i n s u l a_ NN _1 ) ( m a l c o l m _ x _

Original: NN _1 , b i r t h - c on t r o l _ r e f o r m e r _ NN _1 ) ( w o r l d _ w a r _ i _ NN _1 , p it c h e d _ba ttl
Revised: NN _1 , b i r t h - c on t r o l _ r e f o r m e r _ NN _1 ) ( w o r l d _ w a r _ i _ NN _1 , p it c h e d _ba ttl

Original: e
Revised: e

Original: _ NN _ ) ( s t .
Revised: _ NN _ ) ( s t .

Original: _g r e go r y _
Revised: _g r e go r y _

Original: o f _n .
Revised: o f _n .

Original: NN _1 , v i c a r _o f _
Revised: NN _1 , v i c a r _o f _

Original: c h r
Revised: c h r

Original: i s t _
Revised: i s t _

Original: NN _ 1 ) ( l o s _a n g e l e s _
Revised: NN _ 1 ) ( l o s _a n g e l e s _

Original: NN _1 , po r t _
Revised: NN _1 , po r t _

Original: NN _1 ) ( i r i s h
Revised: NN _1 ) ( i r i s h

Original: c
Revised: c

Original: ap it a l _
Revised: ap it a l _

Original: NN _1 , u r ba n _ ce n t e r _
Revised: NN _1 , u r ba n _ ce n t e r _

Original: NN _1 ) ( n i ppon_
Revised: NN _1 ) ( n i ppon_

Original: NN _1 , it a li an_ r e g i on _ NN _1 ) ( g r e a t _ l a ke s _
Revised: NN _1 , it a li an_ r e g i on _ NN _1 ) ( g r e a t _ l a ke s _

Original: NN _1 , c anad i an _p r o v i n ce _ NN _1 ) ( v o l c ano_
Revised: NN _1 , c anad i an _p r o v i n ce _ NN _1 ) ( v o l c ano_

Original: i s l and s _
Revised: i s l and s _

Original: NN _1 , u r ban_ ce n t e r _
Revised: NN _1 , u r ban_ ce n t e r _

Original: NN _1 ) ( r ob e r t _o w e n _ NN _1 , t yc oon _ NN _1 ) ( t r an s y l v an i an_a l p s _ NN _1 , u r ban _ ce
Revised: NN _1 ) ( r ob e r t _o w e n _ NN _1 , t yc oon _ NN _1 ) ( t r an s y l v an i an_a l p s _ NN _1 , u r ban _ ce

Original: n t e r _ NN _1 ) ( n i ppon_
Revised: n t e r _ NN _1 ) ( n i ppon_

Original: NN _1 , a m e r i c an _ s t a t e _
Revised: NN _1 , a m e r i c an _ s t a t e _

Original: NN _ 1 ) ( m anda l a y _
Revised: NN _ 1 ) ( m anda l a y _

Original: NN _1 , na ti ona l _ c
Revised: NN _1 , na ti ona l _ c

Original: ap it a l _
Revised: ap it a l _

Original: NN _1 ) ( g e tt y s b u r g_
Revised: NN _1 ) ( g e tt y s b u r g_

Original: NN _2 , s i e g e _
Revised: NN _2 , s i e g e _

Original: NN _1 ) ( h e l s i n k i _ NN _1 , u r ba n _ ce n t e r _ NN _ 1 ) ( nan_ li ng_ NN _1 , u r ban_ ce n t e r _ NN _1 ) ( w i s c
Revised: NN _1 ) ( h e l s i n k i _ NN _1 , u r ba n _ ce n t e r _ NN _ 1 ) ( nan_ li ng_ NN _1 , u r ban_ ce n t e r _ NN _1 ) ( w i s c

Original: o n s i n _
Revised: o n s i n _

Original: NN _2 , g e og r ap h
Revised: NN _2 , g e og r ap h

Original: i c a l _ r e
Revised: i c a l _ r e

Original: g
Revised: g

Original: i o n _
Revised: i o n _

Original: NN _1 ) ( c ap it a l _o f _ ga m b i a_
Revised: NN _1 ) ( c ap it a l _o f _ ga m b i a_

Original: NN _1 , u r ba n _ ce n t e r _
Revised: NN _1 , u r ba n _ ce n t e r _

Original: NN _1 ) s yn s e t _ do m a i n_ t op i
Revised: NN _1 ) s yn s e t _ do m a i n_ t op i

Original: c _o f ( C ) ( li b e l _
Revised: c _o f ( C ) ( li b e l _

Original: NN _1 , c
Revised: NN _1 , c

Original: r i m i n a l _ l a w _ NN _ ) ( w r it e _
Revised: r i m i n a l _ l a w _ NN _ ) ( w r it e _

Original: VB _8 , t r a n s c r
Revised: VB _8 , t r a n s c r

Original: i
Revised: i

Original: p ti o n _
Revised: p ti o n _

Original: NN _5 ) ( a ss i s t _
Revised: NN _5 ) ( a ss i s t _

Original: NN _2 , a m .
Revised: NN _2 , a m .

Original: _ f oo t ba ll _ ga m e _
Revised: _ f oo t ba ll _ ga m e _

Original: NN _1 ) ( b r y nh il d_ NN _1 , m y t
Revised: NN _1 ) ( b r y nh il d_ NN _1 , m y t

Original: h o l og y _ NN _2 ) ( t e m p l e _ NN _4 , m u s li m i s m _ NN _2 ) ( d r i ve _ NN _ 12 , c
Revised: h o l og y _ NN _2 ) ( t e m p l e _ NN _4 , m u s li m i s m _ NN _2 ) ( d r i ve _ NN _ 12 , c

Original: o u r t _ga m e _ NN _1 ) ( s l u gg e r _ NN _1 , s po r t _ NN _1 ) ( a ss i s t _
Revised: o u r t _ga m e _ NN _1 ) ( s l u gg e r _ NN _1 , s po r t _ NN _1 ) ( a ss i s t _

Original: NN _2 , h
Revised: NN _2 , h

Original: o c k ey _ NN _ ) ( s e ll _
Revised: o c k ey _ NN _ ) ( s e ll _

Original: VB _1 , o ff e n s e _
Revised: VB _1 , o ff e n s e _

Original: NN _3 ) ( s e ll _
Revised: NN _3 ) ( s e ll _

Original: VB _1 , l a w _ NN _1 ) ( r e l a ti o n s h i p _ NN _4 , b i o l og y _ NN _1 ) ( s l u gg e r _ NN _1 , s o f t ba ll _ga m e _ NN _ 1 ) ( s e m iti c
Revised: VB _1 , l a w _ NN _1 ) ( r e l a ti o n s h i p _ NN _4 , b i o l og y _ NN _1 ) ( s l u gg e r _ NN _1 , s o f t ba ll _ga m e _ NN _ 1 ) ( s e m iti c

Original: d e it y _
Revised: d e it y _

Original: NN _1 , m y t
Revised: NN _1 , m y t

Original: h o l og y _ NN _1 ) ( apo s tl e _ NN _3 , w e s t e r n _ c hu
Revised: h o l og y _ NN _1 ) ( apo s tl e _ NN _3 , w e s t e r n _ c hu

Original: r c h _ NN _ ) ( d r i ve _ NN _ 12 , bad m i n t o n _ NN _ ) ( nu c l e a r _d e t e rr e n ce _ NN _1 , l a w _
Revised: r c h _ NN _ ) ( d r i ve _ NN _ 12 , bad m i n t o n _ NN _ ) ( nu c l e a r _d e t e rr e n ce _ NN _1 , l a w _

Original: NN _1 ) ( a ss i s t _
Revised: NN _1 ) ( a ss i s t _

Original: NN _2 , s po r t _
Revised: NN _2 , s po r t _

Original: NN _1 ) ( r ece p ti o n _
Revised: NN _1 ) ( r ece p ti o n _

Original: NN _5 , ba s e ba ll _ga m e _ NN _1 ) ( t r o t _
Revised: NN _5 , ba s e ba ll _ga m e _ NN _1 ) ( t r o t _

Original: VB _2 , e q u e s t
Revised: VB _2 , e q u e s t

Original: r
Revised: r

Original: i a n _ s po r t _ NN _1 ) ( p h o t o
Revised: i a n _ s po r t _ NN _1 ) ( p h o t o

Original: s y n t
Revised: s y n t

Original: h e s i s _
Revised: h e s i s _

Original: NN _1 , c
Revised: NN _1 , c

Original: h e m i s t
Revised: h e m i s t

Original: r
Revised: r

Original: y
Revised: y

Original: NN _1 ) ( r n a_
Revised: NN _1 ) ( r n a_

Original: NN _1 , c
Revised: NN _1 , c

Original: h e m i s t
Revised: h e m i s t

Original: r
Revised: r

Original: y
Revised: y

Original: NN _1 ) ( i s o l d e _ NN _1 , pa r ab
Revised: NN _1 ) ( i s o l d e _ NN _1 , pa r ab

Original: l e _ NN _ ) ( a ss i s t _
Revised: l e _ NN _ ) ( a ss i s t _

Original: NN _2 , s o cce r _
Revised: NN _2 , s o cce r _

Original: NN _1 ) ( a ss i s t _
Revised: NN _1 ) ( a ss i s t _

Original: NN _2 , c o u r t _ ga m e _
Revised: NN _2 , c o u r t _ ga m e _

Original: NN _1 ) ( a ss i s t _
Revised: NN _1 ) ( a ss i s t _

Original: NN _2 , f oo t ba ll _ga m e _ NN _ )
Revised: NN _2 , f oo t ba ll _ga m e _ NN _ )