Definition Frames: Using Definitions for Hybrid Concept Representations

09/10/2019 ∙ by Evangelia Spiliopoulou, et al. ∙ Carnegie Mellon University 0

Concept representations is a particularly active area in NLP. Although recent advances in distributional semantics have shown tremendous improvements in performance, they still lack semantic interpretability. In this paper, we introduce a novel hybrid representation called Definition Frames, which is extracted from definitions under the formulation of domain-transfer Relation Extraction. Definition Frames are easily reformulated to a matrix representation where each row is semantically meaningful. This results in a fluid representation, where we can prune dimension(s) according to the type of information we want to retain for any specific task. Our results show that Definition Frames (1) maintain the significant semantic information of the original definition (human evaluation) and (2) have competitive performance with other distributional semantic approaches on word similarity tasks. Furthermore, our experiments show substantial improvements over word-embeddings when fine-tuned to a task even using only a linear transform.



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1 Introduction

Creating algorithms that extract human knowledge is a fundamental question for Natural Language Processing. An important problem in this area is how to construct concept representations and determine which information to encode.

Ontology-based methods constitute one of the oldest approaches to organize and represent knowledge that is still widely used in NLP tasks. They can be in the form of lexical resources like WordNet [24] and FrameNet [2], large knowledge bases like ConceptNet [30] and CYC [18] or domain-dependent ontologies carefully designed for particular problems/domains. Ontologies are particularly useful since they contain accurate and semantically interpretable information that can be easily accessed and filtered by humans according to the task of interest. However, this information is typically constructed manually, which is a very time-consuming and difficult process. This results in representations that are not easily extensible, so they cannot be modified or fine-tuned in the presence of new information.

Most recent advances focus on learning representations by training a language model on extremely large corpora. Although this is a more data-driven approach compared to the meticulous construction of an ontology, distributed representations are fully automated (no manual annotations needed) and they can be fine-tuned for any new task. Earlier examples include models like GloVe

[26], word2vec [22] and fastText [4], which can be used to obtain high quality generic word embeddings by pre-training them in large corpora. Most recent work focuses on context-sensitive word embeddings like ELMo [27] and BERT [8], which achieve significant improvements in various downstream NLP tasks. Those methods can represent polysemy, since the word embeddings are no longer static, but they change based on the context that the word occurs.

Despite their exceptional performance, most distributional methods do not have any explicit semantic interpretation. The resulting representations may encode tremendous amount of information, but we have no control or way to interpret what this information is, how it relates to the concept or if it just reflects biases of the data. Thus, we cannot choose which type of information is useful for a specific task, unless we have a lot of data and resources to fine-tune the representations (which, unfortunately, is a rare scenario for most semantically-oriented tasks). Although few approaches have tried to bridge the gap between semantics and distributed representations [9, 25], they only encode information from manually constructed ontologies. This causes serious limitations since most available information is either noisy or in a free text format. Furthermore, although those approaches use ontological relations, the resulting representation is a word embedding without any further semantic interpretation.

Motivated by these problems, we introduce a novel hybrid representation called Definition Frames that encodes semantic information extracted from definitions. This information is extracted automatically via a relation extraction model, which means that we can create a representation for any term, as long as there is some accompanying definition/text. According to our knowledge, Definition Frames are the first hybrid representation: they have an explicit structure due to their semantically meaningful rows, while maintaining the properties of distributional semantics. As our experiments show, Definition Frames achieve better performance in word similarity tasks, when used as a post-processing method.

2 Prior Work

Dictionary definitions constitute an excellent source of human knowledge, as they contain essential relations about a concept. Although definitions are written in natural language, they follow a specific structure. Most definitions of a concept contain the class to which it belongs (Genus) and the properties that differentiate it from other concepts of the same class (Differentia). In addition to their structure, definitions contain generic information that is sufficient to uniquely identify a concept, whereas most natural language text (i.e. news articles, books, online forums) typically contain information about specific instances of a concept. Those interesting properties of definitions motivate a series of work that uses them as sources to extract knowledge.

Earlier work on definitions focuses on extracting the Genus and Differentia

relations via string matching heuristics and syntactic properties

[3, 6, 7]. However, similarly to ontology-based representations, those methods require a lot of manual effort and lack generalization. Recent approaches on information extraction from definitions try to directly encode definitions to distributed representations. The motivation behind this work is to benefit from the rich knowledge encoded in definitions, while still maintaining the properties of distributional semantics. tissier2017dict2vec use a skip-gram model to obtain word embeddings trained on dictionary definitions. Inspired by the work of noraset2017definition on generating definitions from word embeddings, bosc2018auto use an auto-encoder on definition sentences, whereas the hidden layer is used as the distributed representation. Other work includes binary classification of sentences to definitional or not [1] and reverse dictionary look-up [15, 33].

Another line of work focuses on enriching word embeddings with semantic knowledge from lexical resources, typically in a post-processing manner. faruqui2015retrofitting propose Retrofitting, a process where they use belief-propagation to update embeddings on a relation graph from a large ontology. mrkvsic2017semantic and mrkvsic2016counter on the other hand, inject antonymy and synonymy constraints into word embeddings, a process they call Counter-fitting. An interesting example of Counter-fitting is the LEAR framework, where they discuss the particular importance of the isA relation in word embeddings [32].

3 Approach

3.1 Definitional Relations

Similar to work on definitions, relation extraction focuses on detecting a set of important relations between terms. Besides domain-specific relations, most RE tasks [11, 14] typically contain relations that belong to three main classes: hypernymy/hyponymy relations (isA), relations about structure (madeOf, partOf, hasA222The hasA relation is the inverse of partOf.) and teleological relations (usedFor, cause). In order to verify the prevalence of those relations in definitions and their correspondence to Genus and Differentia, we manually annotate 50 sentences defining a concept chosen at random (summarized in Table 1). Those concepts are selected from the set of all nominal synsets from WordNet that are linked to Wikipedia, while for the sentences/definitions we use the first sentence of Wikipedia. From those annotations we observed that most definitions use the isA relation combined with a Differentia type relation and that certain relations can only be used on concepts with specific semantic types. As an example, the cause relation can only be used on events, while the madeOf relation on physical entities. Some other structures used as Differentia include adjectives, topics and analytical descriptions of processes.

Relation Num Sentences
IsA 44
PartOf 7
HasA 9
MadeOf 2
UsedFor 10
Cause 4
Table 1: Annotated Relations for 50 Wikipedia Sentences.
Figure 1: Architecture diagram for Relation Retriever & Definition Encoder.

3.2 Data Construction

Because there is no prior work on neural-based RE from definitions, we follow a domain adaptation technique where we use a model pre-trained on different data. However, most existing datasets on RE are particularly small or focus on a very narrow domain, which makes it hard to use them to obtain general relations. Given those constraints, we construct a large but simple dataset based on ConceptNet to pre-train the Relation Retriever model (more details in section 3.3).

ConceptNet [30] is a large general purpose ontology that contains relations between pairs of concepts. Many of those relations are accompanied by a small source-definition, where the relation was extracted from. For example, in Figure 1 we see that the Concept-query Sun is linked to two sentences (Sun is a star and Sun is in our Solar System) from ConceptNet with the corresponding Definitional Relations isA and partOf. In order to construct the training data, we first extract all ConceptNet relations that overlap with Definitional Relations ( isA, usedFor, partOf, hasA and madeOf 333We exclude the cause relation, as our evaluation datasets typically do not include events). Then, for every pair of concepts, we extract the POS and chunk tags using the Stanford CoreNLP parser [21]. We also mark the concept that corresponds to the first argument of the relation, as it represents the term for which we want to extract the Definition Frames given its definition (Concept-query). Those are used as additional features to the initial sentence in the Relation Retriever model. In order to select the best performing model, we split our data into train (68,700 relations), dev and test (8,500 relations respectively).

In order to extract the Definition Frames we use data from Wikipedia which we pre-process in a similar way. One major difference compared to ConceptNet is that Wikipedia sentences are more complex, as they may contain relations of the Concept-query with multiple terms or even relations between terms other than the Concept-query. In order to account for those differences, we do not add any constraints on the number of the extracted relations.

3.3 Extracting Definition Frames

Our framework consists of two parts: the Relation Retriever and the Definition Encoder. Given a Concept-query, the Relation Retriever uses the corresponding Wikipedia sentence to extract the terms that are related to that concept. The set of the extracted relations with the respective related terms form the Definition Frame.

As an example, consider the Concept-query Moon for which we want to extract the Definition Frames. As we see in Figure 1, we first extract the Wikipedia definition about Moon. This sentence is then processed in the pre-trained Relation Retriever model, which detects the terms that are related to Moon. In our example those terms are satellite, astronomical body and Solar System. Those terms with their corresponding relations constitute the Definition Frame for Moon.

Model Pr Re F1
BiLSTM 95.1 94.5 94.8
Stacked-BiLSTM 93.6 93.4 93.5
BiLSTM-CNN 94.5 93.6 94.0
Table 2: Relation Retriever on ConceptNet data.

Since our setting is different from typical relation extraction tasks and ConceptNet data is fairly simple compared to Wikipedia definitions, we choose to avoid over-complicated models for the Relation Retriever, as they are prone to over-fitting. Thus, for our model selection we perform experiments with models that in general constitute strong baselines for RE tasks and do not take into account specific properties of the data. Those models include: a simple BiLSTM [17], a 2-layer deep BiLSTM (Stacked-BiLSTM) and a hybrid BiLSTM-character-CNN model that shows high performance on NER tasks [20]. Although our goal is not to detect named entities, NER is a problem highly correlated with our setting, since we do not have gold entities (besides the Concept-query).

As we see in Table 2

, all models have extremely good performance, which is probably due to the simplicity of the ConceptNet dataset. Given that the simple BiLSTM shows slightly better results while having the smallest number of parameters, we select it as the main model in the Relation Retriever module.

3.4 Encoding Definition Frames

In the previous section we described how we obtained the Definition Frames for a Concept-query. Although Definition Frames capture important information to define a concept, we still face the problem of how to use them in a downstream NLP task. In this section we explain our method to encode them in a distributed representation via the Definition Encoder.

The output representation from the Definition Encoder is a matrix where each row corresponds to one of the Definitional Relations. Given a relation , the corresponding th row of the matrix is an encoding of the terms related to the Concept-query with the same relation , as provided by the corresponding Definition Frame. The Definition Encoder uses an embedding space (we refer to this as ) to construct the individual word embeddings for the related terms.

Specifically, given a Definition Frame , .., , where each isA, usedFor, partOf, hasA, madeOf, cause } and is the set of terms related to the Concept-query with the relation , we define the average embedding for relation as:

where is the embedding for each word based on the input Basis space. Then, we construct the matrix , where each dimension

contains the vector

and semantically corresponds to the terms that relate to the Concept-query via the relation . All encoded Definition Frames maintain the same structure (each row corresponds to a fixed relation), thus a semantically meaningful representation. If no terms were extracted for a relation, we use the zero vector of the appropriate size instead of . An example of the encoded Definition Frame for the concept Moon is shown in Figure 1, where each dimension corresponds to a unique relation (isA and partOf relations have encoded embeddings, while the others correspond to zero vectors).

4 Experiments & Discussion

4.1 Evaluation on Word-Similarity Tasks

This set of experiments focuses on the performance of Definition Frames on word similarity tasks and how we can benefit from their inherit structure. Our experiments are based on benchmark word-similarity datasets and code, as provided by faruqui-2014:SystemDemo, for which we report Spearman’s correlation

between the cosine similarity of the words representations and the normalized ground truth similarity score. For all experiments we only consider words that exist in all our compared methods and baselines.

GloVe Word2Vec wn Dict2Vec
RG-65 0.79 0.68 0.81 0.40 0.06 0.23 0.84 0.73 0.86
SimLex 0.31 0.30 0.31 0.13 0.12 0.12 0.41 0.36 0.39
SimVerb 0.19 0.14 0.15 0.13 -0.02 0.04 0.24 0.14 0.16
WS-SIM 0.63 0.57 0.65 0.58 0.47 0.52 0.78 0.76 0.78
MC30 0.70 0.72 0.71 0.25 0.38 0.13 0.73 0.80 0.74
Table 3: Spearman’s correlation for word embeddings in similarity datasets. The best performing model between and is shown in bold, while the best performing model overal (, and ) is underlined.
GloVe Word2Vec wn Dict2Vec
MEN 0.72 0.66 0.71 0.47 0.35 0.43 0.74 0.70 0.73
MTurk287 0.74 0.68 0.70 0.30 0.25 0.28 0.71 0.66 0.72
MTurk771 0.64 0.64 0.63 0.37 0.40 0.38 0.71 0.69 0.69
RW-STAN 0.37 0.36 0.36 0.21 0.16 0.14 0.41 0.40 0.41
WS-REL 0.51 0.46 0.52 0.35 0.31 0.36 0.67 0.62 0.67
Table 4: Spearman’s correlation for word embeddings in relatedness datasets. The best performing model between and is shown in bold, while the best performing model overal (, and ) is underlined.

Word-similarity tasks are particularly interesting, as words can be similar in different ways or facets. Although most of our data does not have an explicit type of similarity, we can divide them into two broad categories, as prior literature suggests: similarity and relatedness. For similarity datasets we use RG-65 [29], SimLex999 [16], SimVerb3500 [12] and MC-30 [23], while for relatedness we use MEN [5], MTurk287 [28], MTurk771 [13] and RW-Stanford [19]. Furthermore, we evaluate on WS-353 dataset [10] by dividing it into similarity and relatedness subsets (WS-SIM and WS-REL), as proposed by agirre2009study.

The Role of Structure

We perform experiments with three different types of word embeddings that vary with respect to the method and the data they were trained on. Those include: GloVe embeddings pretrained on Wikipedia (directly provided from [26]), word2vec trained on WordNet definitions (as described in bosc2018auto) and dict2vec trained on Wikipedia (using the code available from tissier2017dict2vec). Given that dict2vec is also a post-processing method on word2vec via definitions, we are not comparing with additional word2vec baselines. Finally, since all datasets comprise of a pair of words without any more context, we are not comparing with any context-based representations.

Each of those embeddings is used as the Basis embedding space in the Definition Encoder model, as described in section 3.4. In our first experiments, we compare two versions of Definition Frames to the original Basis embeddings without any fine-tuning or modification: one that contains all the relations () and one that contains only the word and the isA relation (). Our choice of those Definition Frames is based on a series of ablation studies where we eliminate dimensions. According to those studies, the isA relation affects the performance in a different way according to the type of task (similarity versus relatedness).

In Tables 3 and 4 we summarize the results from those experiments. Although we cannot clearly conclude whether Definition Frames achieve better performance than the Basis embeddings, we observe some interesting patterns of consistent comparative performance (, and ).

Our first observation is that the comparative performance of and is mostly similar across all three Basis embeddings for any given dataset. We further notice that for many instances where outperforms , it also outperforms

. This consistent behavior indicates that, although Definition Tensors carry additional useful information through their structure, we do not exploit it in the best way possible.

Our second observation concerns the difference on performance with respect to the type of similarity. When we compare the Definition Frames with the Basis embeddings we notice that the former perform better in similarity tasks (Table 3) than in relatedness (Table 4), as also reported by bosc2018auto. Our explanation of the poor performance in relatedness tasks is that, even if we have complete and accurate information of all the relations, some relations are not mapped properly due to the cosine similarity metric, a problem also discussed by faruqui2016problems. In our framework for example, consider two highly related words like car and wheel. Although Definition Frames might include the partOf relation between them, the standard cosine similarity metric is not able to account for similarities across different dimensions (in this case partOf with the actual word).

Applying a Linear Transform

In order to validate our hypothesis about the effect of structure and whether the cosine similarity metric is an impediment for our representations, we design a slightly modified version of the previous experiments. For any dataset, instead of directly evaluating the encoded Definition Frame, we first apply a linear transformation on it. Thus, given the Definition Frames and for a pair of words , we get

which we now use in our experiments. The parameters , are learnt for each dataset separately to account for discrepancies across datasets. Our objective is to minimize the mean squared error between the cosine similarity of the linearly transformed representations and the normalized ground truth similarity score.

For our experiments we use 10-Fold cross-validation and we report the average performance. We ignore datasets with less than 100 instances due to their small size. We also follow the same method for the Basis embeddings on each dataset by learning the parameters , . In Table 5 we compare the performance of the Basis embeddings before and after the linear transformation ( and ), with the Definition Frame ( and ). Since they were the best performing embeddings in the previous section, we perform experiments with both GloVe and dict2vec as the Basis embeddings used for and . The performance of the embeddings before and after the transformation is reported on the same cross-validation splits to avoid randomness. Finally, for our reported results, we ignore datasets where both and embeddings show lower performance after the linear transformation (MTurk287, MTurk771 and RW-STAN) or with a high p-value () for the cross validation splits (SimVerb), as this hints inconsistency of the type of similarity within the dataset.

GloVe Dict2Vec
Dataset Basis Basis Gain DF DF Gain Basis Basis Gain DF DF Gain
SimLex 0.29 +0.15 0.31 +0.19 0.41 0.48 +0.07 0.36 0.47 +0.11
WS-SIM 0.53 0.66 +0.13 0.54 0.67 +0.13 0.73 0.76 +0.03 0.68 0.71 +0.03
MEN 0.72 +0.10 0.63 +0.21 0.73 0.07 0.65 +0.15
WS-REL 0.49 0.56 +0.07 0.41 0.50 +0.09 0.58 0.65 +0.07 0.57 0.60 +0.03
Sim-All 0.31 +0.08 0.31 +0.15 0.43 +0.11 0.40 +0.14
Rel-All 0.70 +0.09 0.61 +0.19 0.71 +0.07 0.64 +0.15
Table 5: Spearman’s correlation for embeddings before and after the linear transform. We show in bold the model with the best performance and the highest . We denote with the experiments with p-value .

Our results show that outperforms in most datasets. Furthermore, the average gain in performance () is significantly higher for Definition Frames, which confirms our previous hypothesis. We also report the performance after training jointly on all similarity (Sim-All) and relatedness datasets (Rel-All). In this setting we also include the small sized datasets (MC-30 and RG-65), but not those that show negative gain. Since we now have more data, we see a clear improvement of Definition Frames for both GloVe and dict2vec used as . We further observe that for large datasets (Sim-All, Rel-All and MEN) p-values are extremely small () and clearly outperforms , whereas for smaller datasets (WS-SIM and WS-REL) p-values are higher ( ).

Through these experiments we show that structure leads to more fluid representations: a crucial factor when we need only a subset of the information encoded. Although fine-tuning is a widely used method to account for such phenomena, it typically involves complex models that require a lot of in-domain data. However, using only a linear transform, we achieve overall better performance compared to state-of-the-art pre-trained embeddings. This is a crucial step, as a linear transform allows to maintain semantic coherence of the representations compared to currently non-trackable neural methods.

4.2 Semantics of Definition Frames

The major contribution of Definition Frames is that, besides having overall better performance than other distributed representations, they are also semantically meaningful. While in the previous section we presented our results on their performance on word-similarity tasks, here we focus on their semantic aspect.

The first point to discuss is the quality of Definition Frames as a concept representation. Definition Frames are based on a set of relations and related terms that are extracted automatically, compared to other approaches that use ontologies for that. Although this allows to extend our representations with more information, it might also add noise in them. In order to account for this phenomenon and evaluate the ability of Definition Frames to capture the essential semantic aspects of a definition, we performed a human study on Definition Frames.

For this study we use a subset of 240 Wikipedia sentences for random Concept-queries. Because there is no other relation extraction system that uses exactly the same set of relations, we use the AllenNLP Open Information Extraction system [31] as our baseline. Given that OpenIE is a general IE tool, we only consider a subset of the output where the Concept-query is contained in the first argument (ARG0). Furthermore, we note that for a significant number of sentences OpenIE has no such output, so those sentences were disregarded before the evaluation.

System Score
OpenIE 79/240 (33%)
Definition Frames 126/240 (53%)
Equally Good
/ Bad 35/240 (14%)
Table 6: Amazon Turkers evaluation of Definition Frames.

In order to compare the output of the two systems, we asked from Amazon Mechanical Turkers to rank them (3 annotators per sentence). For each datum, we provide the original definition sentence, the Concept-query and the output of the two systems. Then, we ask each annotator the following question with three possible, mutually exclusive replies:

’Which system better represents the definition of the Concept-query?’
(1) system 1
(2) system 2
(3) both are equally good/bad.

In order to interpret the annotators’ replies, we label a datum to belong to system if at least 2/3 of the annotators choose it, otherwise we label it to belong to the class equally good/bad. As we see in Table 6, according to the study, Definition Frames outperformed OpenIE by a large margin. Although we do not claim that our representation is better than OpenIE in a general setting (they have a different objective), these results are a good verification that Definition Frames are able to capture the semantics of definitions.

The second point is whether Definition Frames are still an explainable representation, after they are encoded in a matrix format. As discussed earlier, Definition Frames maintain a very specific structure. Given a concept , each dimension of its Definition Frame contains the terms that are related with via a particular relation. The exact same structure is maintained in the matrix representation, as each row contains the now distributed representation of those same terms. Thus, from a human perspective, given a Definition Frame in a matrix format, we know that for every row that contains a non-zero vector, there is some term(s) that are related with via the relation .

An important property of the Definition Frames is that we can retrieve those related terms from the matrix representation. As described in section 3.4, the Definition Encoder module maps each word to some pre-existing embedding space (Basis). Given that we do neither learn nor modify this space, we can easily find the word given its embedding or use any standard similarity metric (i.e. cosine distance, euclidean distance, etc) when multiple words are encoded in the same row. Thus, although the encoded matrix representation is not interpretable by humans as-is, we can easily convert it back to the original, semantically meaningful Definition Frame. This is also the reason why we only used a linear transformation in the second set of experiments in section 4.1

: we can easily revert linear transformations, unlike the non-linearities of neural networks.

5 Conclusion & Future Work

Through this paper we propose a hybrid representation that has interpretable dimensions, while still maintaining properties of distributional semantics. While previous work focused on improving the performance of distributional vectors by infusing semantic knowledge in them, our goal is to design a novel representation that benefits from the information encoded in word embeddings but is also semantically meaningful. Towards this end, we achieve better results in word similarity tasks by using only a weighted version of our structured representations (linear transformation).

More than the representations themselves, the contribution of this work is that it sets a possible basis to combine meaning with downstream performance in NLP. Some promising directions for future work include improving the encoding of Definition Frames to a richer representation and exploring in depth how we can exploit the structure of Definition Frames to improve the representations. Another path of future work may focus on using the information encoded in Definition Frames to propagate information across them and to learn a new embedding space. Finally, we believe that Definition Frames can be an extremely useful representation to tasks that rely heavily in semantics, like common sense reasoning, open question answering, natural language inference, etc. Due to the nature of those tasks and their complexity, a hybrid meaningful distributed representation, like Definition Frames, allows us to choose which aspects of the representation are important for a problem.


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