2016 UnsupervisedRelationExtractioni

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Subject Headings: Unsupervised Relation Extraction.


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This paper deals with the extraction of semantic relations from scientific texts. Pattern-based representations are compared to word embeddings in unsupervised clustering experiments, according to their potential to discover new types of semantic relations and recognize their instances. The results indicate that sequential pattern mining can significantly improve pattern-based representations, even in a completely unsupervised setting.

1 Introduction

Relation extraction and classification deal with identifying the semantic relation linking two entities or concepts based on different kinds of information, such as their respective contexts, their co-occurrences in a corpus and their position in an ontology or other kind of semantic hierarchy. It includes the task of finding the instances of the semantic relations, i.e. the entity tuples, and categorizing their relation according to an ontology or a typology. Most systems focus on binary semantic relations. In supervised learning approaches, relation extraction is usually performed in two steps: first, the entity couples corresponding to concepts are extracted or generated, and a binary classification is performed to distinguish those couples which are instances of any semantic relation. Second, the relation itself is categorized according to its similarity to other, known relation types. Unsupervised relation extraction has received far less attention so far. In an unsupervised framework, relation types are inferred directly from the data and, instead of a pre-defined list of relations, new types can be discovered in parallel with the categorization of relation instances.

Unsupervised extraction is often applied to specialized domains, since the manual construction of knowledge bases or training examples for such domains is costly in terms of effort and expertise. The research we present is concerned with unsupervised relation extraction in the scientific domain. The types of relations we expect to extract allow to provide a deeper semantic model and understanding of scientific papers and, in the long term, contribute to automatically build the state of the art of a given domain from a corpus of articles relevant to it. The deep semantic analysis of scientific literature allows the identification of inter-document links to facilitate inter-textual navigation and the access to semantic context [28]. It also allows to study the evolution of a scientific field over time [6,26], as well as the access to scientific information through information retrieval [30].

Within this research context, a typology of domain-specific semantic relations was first created and used for corpus annotation in order to confirm the feasibility of the extraction task and to perform extrinsic evaluation of the results [12]. From this point on, our approach to the relation extraction task is completely unsupervised: we do not rely on any of the manually annotated or categorized data. The most important issue consists in defining an approach which is independent from both the domain and the corpus: we do not want to impose any constraint on the types of the relations to be extracted. Different types of information, such as pattern-based representations and word embeddings, are used as input to the classification of the entity couples according to the semantic relation. After performing a range of clustering experiments, we conclude that patternbased clustering can be significantly improved using sequence mining techniques, yielding the best results in every clustering algorithm we tested.

In what follows, we present the state of the art and explain the specificities of our approach as compared to previous work (2). We then describe the data we used (3), the input features (4) and the algorithms (5). Subsequently, we present the evaluation (6) and discuss the results (6.3). Section 7 concludes the paper and indicates the lines of future work.

2 Related work

Semantic relation extraction and classification is an important task in the domain of information extraction, knowledge extraction and knowledge base population. A plethora of approaches have been applied to relation extraction, among which we can distinguish tendencies according to:

  • whether the method aims to classify entity couples in a given set of relations or to discover new types of relations,
  • the approach to be used: symbolic approaches through e.g. hand-crafted extraction patterns, or machine learning approaches (classification/clustering or sequence labeling methods),
  • the input information and the representation used: pattern-based, lexical, syntactic features, distributional vectors, etc.

Supervised systems rely on a list of pre-defined relations and categorized examples, as described in the shared tasks of MUC and ACE campaigns [15]. Using small, manually annotated training corpora, these systems extract different kinds of features eventually combined with external knowledge sources, and build classifiers to categorize new relationship mentions [38]. Symbolic systems, similarly to supervised learning algorithms, are specific to the list of relations they are designed to recognize - based on hand-crafted linguistic rules created by linguists or domain experts [13]. On the other hand, with the proliferation of available corpora, a new task emerged: Open Information Extraction (OpenIE) [1,8]. It aims at developing unsupervised or distantly supervised systems with a double objective: overcome the need of scarcely available annotated data, and ensure domain-independence by being able to categorize instances of new relation types. In this kind of work, applications are not limited to a given set of relations and become able to cope with the variety of domain-specific relations [1,10]. Such experiments can also be beneficial for the automated population of ontologies [27] or thesauri [10]. Our work belongs to that second line of research.

According to the type of input features which serve as a base for classification, we can distinguish pattern-based approaches from classification approaches relying on diverse quantifiable attributes. The hypothesis behind pattern-based approaches is that the semantic relation between two entities is explicit in at least a part of their co-occurrences in the text, and therefore relation instances can be identified based on text sequences between/around the entities. Such characteristic patterns are usually manually defined, incorporating linguistic and/or domain knowledge in rule-based approaches [13, 34]. Patterns are not limited to sequences of words, they can contain a combination of lexical and syntactic information [9]. Patterns can also be used indirectly as inputs to supervised classifiers [1] or for calculating similarities between entities’ distribution over patterns [20,32]. Most of these approaches rely on hand-crafted lists of patterns. In [4], sequential pattern mining is used to discover new linguistic patterns within the framework of a symbolic approach.

Another way of including quantifiable context features for relation extraction is to use distributional word vectors, either as "count models" [3] or as word embeddings [21]. Entity couples can be represented by a vector built from the vectors associated with each of its members: popular methods include concatenating the two vectors [2] or taking their difference [33]. These representations will then serve as input for a supervised classifier. However, it has recently been argued in [19] that both concatenation and difference are "clearly ignoring the relation between x and y" (i.e. what links the entities): they only provide information on the type of the individual entities. In this article, the conclusion was that "contextual features might lack the necessary information to deduce how one word relates to another".

Finally, certain biclustering or iterative clustering methods are sometimes applied to divide not only the objects (word or entity couples), but also the dimensions (patterns or features) in parallel. Generative models are more prevalent in this framework. In [35] Latent Dirichlet Allocation (LDA) is adapted to the task of unsupervised relation classification. In [16] Markov logic is used, while in [24] an iterative soft clustering algorithm is applied, based on a combination of distributional similarities and a heuristic method for mining hypernyms in the corpus.

The approach we put forward belongs to the unsupervised/OpenIE framework. We do not rely on any manually classified data or typology of relations. Our experiments rely on unsupervised clustering using two types of representations: text patterns and word embeddings. Moreover, we make use of sequential pattern mining in order to enrich our couples of entities/text patterns matrix and address data sparsity. Our experiments were conducted on the ACL Anthology Corpus of computational linguistics papers, but they can be applied to any field in the scientific domain. In the context of our work, the final purpose is to extract the state of the art of a scientific domain, therefore the constitution of the corpus and the evaluations are focused on the relation types relevant for this kind of information; however, this context does not directly influence our choice of representation and clustering algorithm. Our approach differs from standard relation classification tasks, as defined e.g. in SemEval campaigns [14] in two respects. First, we do not target relations belonging to a pre-defined set. Second, the semantic relations considered in SemEval were lexical by nature, e.g.:
Component-Whole Example: My apartment has a large kitchen.
Member-Collection Example: There are many trees in the forest.

On the contrary, the relations we hope to extract are largely contextual. The same couple of entities can instantiate several distinct relations in the same corpus in different contexts:
Uses_information: (...) models extract rules using parse trees (...)
Used_for: (...) models derive discourse parse trees (...)

3 Data and Resources

For the purpose of these experiments, we used a corpus where concepts in the scientific domain are annotated. The corpus is extracted from the ACL Anthology Corpus [29]. We decided to focus on the abstract and introduction parts of scientific papers since they express essential information in a compact and often repetitive manner, which makes them an optimal source for mining sequential patterns. The resulting corpus of abstracts and introductions contains 4,200,000 words from 11,000 papers.

Entity annotation was done in two steps. First, candidates were generated with the terminology extraction tool TermSuite [7]. The list of extracted terms was then mapped to different ontological resources: the knowledge base of Saffron Knowledge Extraction Framework [5], and the BabelNet ontology [25]. If a term was validated as a domain concept (i.e., found in at least one of the resources), it was annotated in the text. The reader is referred to [11] for further information on the annotated corpus.

4 Input representations

The goal of this part is to represent each co-occurring entity couple in a vector space which allows to calculate a similarity between them. Three distinct types of vector spaces were used as representation bases for our clustering experiments. The first two are pattern-based: they rely on the assumption that couples of entities linked by the same semantic relation will be characterized by similar patterns in at least a part of their co-occurrence contexts (i.e. in the text between the two elements of the couple). One of the representations uses complete text sequences as they are found in the corpus, while the other one relies on patterns that were extracted from these sequences using sequential pattern mining.

The expected advantages of identifying patterns inside the sequences are similar to those using distributed representations. First, using the complete sequences as features leads to data sparsity. Although patterns are basically subsequences of the sequences in the string representation and thus we can expect the size of the feature space to grow, the same sequence can belong to more than one pattern, and thus the number of frequent features is also expected to grow. Second, while adding some words to a sequence may not modify its meaning and the relationship between the two entities, it will still result in separate features in the full sequence representation. A pattern-based representation can capture and quantify the elements of similarity between close, but not identical sequences. Finally, sub-sequences can encode different types of information, e.g. grammatical words can be relevant for the relation between the entities, while content words will provide information about the topic of the context, and both kinds of information are expected to bring us closer to characterizing the semantic relation.

The third representation uses word embeddings of the entities considered separately and hypotheses that their semantic relation is mainly context-independent. By calculating the pairwise similarity between the entities, we expect to quantify the similarity between relation instances. This representation is similar to the one used in [2], though the scope of the experiments and the classification method are different.

4.1 Pattern-based representations

In the pattern-based representation, attributes correspond to text sequences that are found between co-occurring entity couples. We extracted from our corpus every entity couple occurring in the same sentence, together with the text between them. Text sequences can contain other entities, but their length is limited to = 8 words. This results in 998,000 instances extracted.

String representation

Using these co-occurrence data, we first built a sparse matrix M with lines corresponding to entity couples e=(e1, e2) and columns corresponding to text sequences p ? P. The cells Me,p contain an association value between e et p. One of the representations uses raw co-occurrence count, while the other one uses PPMIa weighting. This weight is a variant of Pointwise Mutual Information (PMI) in which values below 0 are replaced by 0. Moreover, the context distribution smoothing method proposed by [18] is applied to the positive PMI weighting. This smoothing, inspired by the success of neural word embeddings in semantic similarity tasks [3, 21], allows to reduce the bias of PMI towards rare contexts. Context words’ co-occurrence counts are raised to the power of a (in equation (2)). Its optimal value is reported to be a = 0,75 according to the experiments of [22]. This finding was directly adapted to PMI [18]:

PPMIa(e, p) = max(log2 P(e, p) P(e) × Pa(p) , 0) (1)
Pa(p) = freq(c)a P c freq(c)a (2)

We will refer to the vectors built as such as the string representation.

Sequential pattern representation

For the second experiment, we applied sequential pattern mining techniques [31] to discover relevant patterns which are specific to semantic relations. The extraction is completely unsupervised: frequent sequential patterns which fulfill a certain number of constraints are automatically extracted from the input. A sequence, in this context, is a list of literals (items) and an item is a word in the corpus. The input corpus was made of all the sequences extracted from co-occurring entities (i.e. the feature space for the string representation). The pattern mining process is applied to word forms without using any additional linguistic information.

The sequence mining tool [4] we used allows distinct options to add constraints on the extracted sequences. We selected contiguous sequences of length between 2 and 8 words and a minimum support of 10. The support of a sequential pattern in a sequence database is the number of sequences in the database containing the pattern. Only closed sequential patterns were considered, i.e. patterns which are not sub-sequences of another sequence with the same support.

To construct the matrices, we filled the cells with raw co-occurrences (how many times a pattern occurs somewhere between the two entities) and, for a second matrix, with the PPMIa-weighted values. We will refer to this representation as pattern representation.

4.2 Distributional representation

This type of feature space also uses contextual information, but it is computed independently for the two entities. We used word2vec [21] to create the distributional vectors, as it proved to be particularly well adapted for semantic similarity tasks and is presumed to encode analogies between semantic relations [23]. word2vec was trained on the whole ACL Anthology Corpus using the skip-gram model [21] and the resulting word embeddings (size=200) were used to represent each entity.

The vector of an entity couple is simply made of the concatenation of the vectors of each entity[1] [2]. We expect this representation to capture very specific relation types, where the potential arguments belong to a restricted semantic class.

5 Clustering

Two methods of hierarchical clustering were tested using cosine similarity and Cluto’s [37] clustering functions. The first one is a top-down clustering based on repeated bisections: at every iteration, a cluster is split in two until the desired number of clusters is reached. This number has to be pre-defined: experiments were performed using different values. The cluster to be divided is chosen so that it maximizes the sum of inter-cluster similarities for each resulting cluster. We will refer to this method as divisive.

The second method is a hierarchical agglomerative clustering with a bisective initialization [36] : a clustering in v n clusters is first calculated (where n is the number of clusters to be produced) through repeated bisections. The vector space is then augmented with v n new dimensions that represent the clusters calculated at the first step, and the values of these dimensions are given by the distance of each object from the centroids of the clusters produced at the initiation stage. The agglomerative clustering is then performed on this augmented vector space. This method was created to combine advantages of divisive (global) and agglomerative (local) algorithms by reducing the impact of errors from initial merging decisions, which tend to be multiplied as the agglomeration progresses [36].

We will refer to this algorithm as [[agglo.

6 Evaluation

6.1 Standard classification

For the sake of the experiments, we selected a sample of 500 abstracts (about 100 words/abstract) and manually annotated relevant relations occurring in this sample. The typology of relations was data-driven: it was established in parallel with the categorization of the examples. An illustration of the relations we identified is shown in Table 1, for a complete description of the manual annotation work and the typology, see [12]. The relations are not specific to the natural language processing domain; they can be used for any scientific corpora.

char ARG1: observed characteristics of an observed ARG2: entity composed_of ARG1: database/resource ARG2: data methodapplied ARG1: method applied to ARG2: data model ARG1: abstract representation of an ARG2: observed entity phenomenon ARG1: entity, a phenomenon found in ARG2: context propose ARG1: paper/author presents ARG2: an idea Table 1. Extract of the typology of semantic relations As a second step, a sample of 615 entity couples which co-occur in the corpus was manually categorized according to this typology. This sample was used as a gold standard for clustering evaluation.

6.2 Baseline and evaluation measures

The clustering results were compared to the standard one as a series of decisions: whether to classify two couples in the same group or in different groups. This evaluation is less influenced by structural differences between two clustering solutions and allows to quantify results in terms of precision and recall.

We also calculated APP Adjusted Pairwise Precision [17]: this measure quantifies average cluster purity, weighted by the size of the clusters. This provides additional information on the proportion of the relevant clusters.

APP = 1 |K| X|K| i=1 nb correct pairs in ki nb pairs in ki × |ki| - 1 |ki| + 1 (3)

For each experiment with respect to cluster size, we created a corresponding random clustering to estimate the difficulty of the task and the contribution of our approaches.

6.3 Results and discussion

The evaluation was conducted so as to allow comparisons between the two clustering algorithms, the three input representations and the two weighting systems. Cluster sizes have an important effect on the results because they are correlated with the number of classes in the standard (21 in our case). On the other hand, a different cluster structure e.g. with finer grained distinctions, may also be semantically justified. The real validity of the clusters must therefore be established by human inspection.

Input #clusters algorithm weight APP Prec Recall F-measure
baseline 100 random N/A 0.0813 0.0955 0.0097 0.0176
baseline 50 random N/A 0.0883 0.1036 0.0198 0.0332
baseline 25 random N/A 0.0979 0.1040 0.0410 0.0588
string 100 divisive freq 0.2498 0.3037 0.1030 0.1538
pattern 100 divisive freq 0.2823 0.3718 0.0993 0.1568
string 50 divisive freq 0.2985 0.2805 0.1302 0.1778
pattern 50 divisive freq 0.3265 0.3159 0.1235 0.1776
string 25 divisive freq 0.3941 0.2219 0.1904 0.2050
pattern 25 divisive freq 0.3947 0.2776 0.1773 0.2164
word2vec 100 divisive incl 0.3396 0.5734 0.0527 0.0965
word2vec 50 divisive incl 0.3541 0.4761 0.0890 0.1499
word2vec 25 divisive incl 0.3545 0.4182 0.1539 0.2250
Table 2. Clustering results with the divisive algorithm

Table 2 shows the results of the divisive method. The string and pattern representations with raw frequency counts are compared with the baseline and word2vec vector representations (where weights are implicitly included in the language model learning). Although the word2vec representation yields a very good performance with respect to both precision measures, it comes at the cost of a very low recall. Since this representation is solely based on the similarity between individual entities, this means that mainly couples having nearly identical entities end up in the same cluster, e.g. : parsing - sentences, parses - sentences, parse sentence. In agreement with [19], this result reveals that this representation is not good at capturing relational similarities.

Input #clusters algorithm weight APP Prec Recall F-measure
string 100 divisive PPMIa 0.3112 0.4905 0.0462 0.0844
string 50 divisive PPMIa 0.3625 0.3789 0.0799 0.1320
string 25 divisive PPMIa 0.3555 0.3133 0.1400 0.1936
Table 3. The effect of the PPMIa weighting with the divisive algorithm

Another interest of Table 2 is the improvement in precision (for both measures) brought by the pattern representation, compared to the string representation. This improvement is accompanied by slight decreases in recall. It is also interesting to note that, as shown by Table 3, PPMIa weighting transforms sequence-based scores the same way as to what we observe with word2vec representations: very high precision with very low recall -despite the fact that the semantics captured by the input representations are different in both cases.

Input #clusters algorithm weight APP Prec Recall F-measure
string 100 agglo PPMIa 0.3020 0.4184 0.1582 0.2296
pattern 100 agglo PPMIa 0.2810 0.4758 0.1936 0.2752
string 50 agglo PPMIa 0.2535 0.3246 0.2142 0.2581
pattern 50 agglo PPMIa 0.2697 0.4200* 0.2657* 0.3268
string 25 agglo PPMIa 0.2585 0.2898 0.2277 0.2550
pattern 25 agglo PPMIa 0.2460 0.3777* 0.2914 0.3290
word2vec 100 agglo incl 0.3630 0.5285 0.1316 0.2107
word2vec 50 agglo incl 0.2966 0.3694 0.1938 0.2542
word2vec 25 agglo incl 0.2972 0.3330 0.2399 0.2789
Table 4. Clustering results with the agglomerative algorithm

Table 4 presents the results of the agglo clustering method. This algorithm works better for every type of representation we considered. The scores reported here are obtained on PPMIa-weighted string and pattern representations. The pattern representation comes out as the absolute winner, with important improvements over string both in terms of precision (6-10%) and recall (3.5-6.5%).

Scores marked by * indicate statistically significant improvements according to a 10-fold cross-validation on the string and pattern clustering solutions. The pattern representation also beats the precision of word2vec in two out of the three settings. Although the recall obtained with the word2vec vectors is also improved by the agglomerative method, the pattern representation still holds an important advantage.

7 Conclusion and future work

We presented an approach to extract new types of semantic relations and instances of relations from specialized corpora using unsupervised clustering. Two types of representations were compared: pattern-based vectors and word embeddings. In agreement with previous results, we found that concatenated word embeddings tend to have a limited contribution to discovering new relation types.

An important finding is that sequential pattern mining contributes to create a much more adapted feature space, as shown by the significant improvement both in terms of precision and recall. This confirms our expectation that sequential patterns are better than full sequences in capturing relational similarities. Another advantage is that the pattern mining process is completely unsupervised.

We plan to conduct a manual evaluation of the resulting clusters. This would allow to have a better insight on the nature of the resulting clusters. Biclustering methods can also be tested on the data: they have the potential to automatically identify the most relevant patterns for each relation type.


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2016 UnsupervisedRelationExtractioniKata Gábor
Haïfa Zargayouna
Isabelle Tellier
Davide Buscaldi
Thierry Charnois
Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining2016
  1. Entities corresponding to multiword expressions will have their unique vector, since word2vec includes an internal module for recognizing multiword expressions.
AuthorKata Gábor +, Haïfa Zargayouna +, Isabelle Tellier +, Davide Buscaldi + and Thierry Charnois +
titleUnsupervised Relation Extraction in Specialized Corpora Using Sequence Mining +
year2016 +