Supervised and Unsupervised Ensembling for Knowledge Base Population

04/16/2016 ∙ by Nazneen Fatema Rajani, et al. ∙ The University of Texas at Austin 0

We present results on combining supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and Linking (TEDL). We demonstrate that our combined system along with auxiliary features outperforms the best performing system for both tasks in the 2015 competition, several ensembling baselines, as well as the state-of-the-art stacking approach to ensembling KBP systems. The success of our technique on two different and challenging problems demonstrates the power and generality of our combined approach to ensembling.



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1 Introduction

Using ensembles

of multiple systems is a standard approach to improving accuracy in machine learning


. Ensembles have been applied to a wide variety of problems in natural language processing, including parsing

[Henderson and Brill1999], word sense disambiguation [Pedersen2000]

, sentiment analysis

[Whitehead and Yaeger2010] and information extraction (IE) [Florian et al.2003, McClosky et al.2012]. Recently, using stacking [Wolpert1992] to ensemble IE systems was shown to give state-of-the-art results on slot-filling for Knowledge Base Population (KBP) [Viswanathan et al.2015]

. Stacking uses supervised learning to train a meta-classifier to combine multiple system outputs; therefore, it requires historical data on the performance of each system on a corpus of labeled training data. Viswanathan et al. viswanathan:acl15 use data from the 2013 KBP slot-filling competition for training and then test on the data from the 2014 competition, therefore they can only ensemble the

shared systems that participated in both years.

However, in some situations, we would like to ensemble systems for which we have no historical performance data. For example, due to privacy, some companies or agencies may not be willing to share their raw data but their final models or meta-level model output. Simple methods such as voting permit “unsupervised” ensembling, and several more sophisticated methods have also been developed for this scenario [Wang et al.2013]. However, such methods fail to exploit supervision for those systems for which we do have training data. Therefore, we present an approach that utilizes supervised and unsupervised ensembling to exploit the advantages of both. We first use unsupervised ensembling to combine systems without training data, and then use stacking to combine this ensembled system with other systems with available training data.

Using this new approach, we demonstrate new state-of-the-art results on two separate tasks in the well-known NIST KBP challenge – Cold Start Slot-Filling (CSSF)111 and the Tri-lingual Entity Discovery and Linking (TEDL) [Ji et al.2015]. Our approach outperforms the best individual system as well as other ensembling methods (such as stacking only the shared systems) on both tasks in the most recent 2015 competition; verifying the generality and power of combining supervised and unsupervised ensembling. There was been some work in the past on combining multiple supervised and unsupervised models using graph-based consensus maximization [Gao et al.2009], however we show that it does not do as well as our stacking method. We also propose two new auxiliary features for the CSSF task and verify that incorporating them in the combined approach improves performance.

2 Background

For the past several years, NIST has conducted the English Slot Filling (ESF) and the Entity Discovery and Linking (EDL) tasks in the Knowledge Base Population (KBP) track as a part of the Text Analysis Conference (TAC). In 2015, the ESF task [Surdeanu2013, Surdeanu and Ji2014] was replaced by the Cold Start Slot Filling (CSSF) task222 which requires filling specific slots of information for a given set of query entities based on a supplied text corpus. For the 2015 EDL task, two new foreign languages were introduced – Spanish and Chinese as well as English – and thus the task was renamed Tri-lingual Entity Discovery and Linking (TEDL) [Ji et al.2015]. The goal was to discover entities for all three languages based on a supplied text corpus as well as link these entities to an existing English Knowledge Base (KB) or cluster the mention with a NIL ID if the system could not successfully link the mention to any entity in the KB.

For CSSF, the participating systems employ a variety of techniques such as such as relevant document extraction, relation-modeling, open-IE and inference [Finin et al.2015, Soderland et al.2015, Kisiel et al.2015]. The top performing 2015 CSSF system [Angeli et al.2015] leverages both distant supervision [Mintz et al.2009] and pattern-based relation extraction. Another system, UMass_IESL [Roth et al.2015]

, used distant supervision, rule-based extractors, and semisupervised matrix embedding methods. The top performing 2015 TEDL system used a combination of deep neural networks and CRFs for mention detection and a language-independent probabilistic disambiguation model for entity linking

[Sil et al.2015].

Given the diverse CSSF and TEDL systems available, it is productive to ensemble them, which has been shown to improve performance on slot filling [Viswanathan et al.2015]. However, their stacking method relies on past training data and thus cannot be used for systems that did not participate in previous years. On the other hand, constrained optimization techniques, that do not crucially rely on past data to aggregate confidence scores across multiple systems for the slot-filling task have also been explored [Wang et al.2013]. However, there has been no past work on ensembling for the TEDL task.

Stacking [Sigletos et al.2005, Wolpert1992] has not been used to combine supervised and unsupervised methods for ensembling KBP systems. In this paper, we introduce the novel idea of combining the supervised stacking approach with the unsupervised constrained optimization approach, improving performance on two different KBP tasks.

3 Overview of KBP Tasks

In this section we give a short overview of each of the KBP tasks considered in this paper.

3.1 Cold Start Slot Filling

The goal of CSSF is to collect information (fills) about specific attributes (slots) for a set of entities (queries) from a given corpus. The queries entities can be a person (PER), organization (ORG) or geo-political entity (GPE). The slots are fixed and the 2015 task also included the inverse of each slot, for example the slot org:subsidiaries and its inverse org:parents. Some slots (like per:age) are single-valued while others (like per:children) are list-valued i.e., they can take multiple slot fillers.

The input for CSSF is a set of queries and the corpus in which to look for information. The queries are provided in an XML format that includes an ID for the query, the name of the entity, and the type of entity (PER, ORG or GPE). The corpus consists of documents in XML format from discussion forums, newswire and the Internet, each identified by a unique ID. The output is a set of slot fills for each query. Along with the slot-fills, systems must also provide its provenance in the corpus in the form docid:startoffset-endoffset, where docid specifies a source document and the offsets demarcate the text in this document containing the extracted filler. Systems also provide a confidence score to indicate their certainty in the extracted information.

3.2 Tri-lingual Entity Discovery and Linking

The goal of TEDL is to discover all entity mentions in a corpus with English, Spanish and Chinese documents. The entities can be a person (PER), organization (ORG), geo-political entity (GPE), facility (FAC), or location (LOC). The FAC and LOC entity types were newly introduced in 2015. The extracted mentions are then linked to an existing English KB entity using its ID. If there is no KB entry for an entity, systems are expected to cluster all the mentions for that entity using a NIL ID.

The input is a corpus of documents in the three languages and an English KB of entities, each with a name, ID, type, and several relation tuples that allow systems to disambiguate entities. The output is a set of extracted mentions, each with a string, its provenance in the corpus, and a corresponding KB ID if the system could successfully link the mention, or else a mention cluster with a NIL ID. Systems can also provide a confidence score for each mention.

4 Algorithm

Figure 1: Our stacking approach to combining supervised and unsupervised ensembles for KBP.

This section describes our approach to ensembling both supervised and unsupervised methods. Figure 1 shows an overview of our system which trains a final meta-classifier for combining multiple systems using confidence scores and other auxiliary features depending on the task.

4.1 Supervised Ensembling Approach

For the KBP systems that are common between years, we have training data for supervised learning. We use the stacking method described in viswanathan:acl15 for these shared systems. The idea is to combine predictions by training a “meta-classifier” to weight and combine multiple models using their confidence scores as features. By training on a set of supervised data that is disjoint from that used to train the individual models, it learns how to combine their results into an improved ensemble model. The output of the ensembling system is similar to the output of an individual system, but it productively aggregates results from different systems. In a final post-processing step, the outputs that get classified as “correct” by the classifier are kept while the others are removed from the output.

The meta-classifier makes a binary decision for each distinct output represented as a key-value pair. For the CSSF task, the key for ensembling multiple systems is a query along with a slot type, for example, per:age of “Barack Obama” and the value is a computed slot fill. For the TEDL task, we define the key to be the KB (or NIL) ID and the value to be a mention, that is a specific reference to an entity in the text. In Figure 1, the top half shows these supervised systems for which we have past training data.

4.2 Unsupervised Ensembling Approach

Only of the systems that participated in CSSF 2015 also participated in 2014, and only of the systems that participated in TEDL 2015 also participated in the 2014 EDL task. Therefore, many KBP systems in 2015 were new and did not have past training data needed for the supervised approach. In fact, some of the new systems performed better than the shared systems, for example the hltcoe system did not participate in but was ranked in the 2015 TEDL task [Ji et al.2015]. We first ensemble these unsupervised systems using the constrained optimization approach described by wang:tac13. Their approach is specific to the English slot-filling task and also relies a bit on past data for identifying certain parameter values. Below we describe our modifications to their approach so that it can be applied to both KBP tasks in a purely unsupervised manner. The bottom half of Figure 1 shows the ensembling of the systems without historical training data.

The approach in wang:tac13 aggregates the “raw” confidence values produced by individual KBP systems to arrive at a single aggregated confidence value for each key-value. Suppose that are the distinct values produced by the systems and is the number of times the value is produced by the systems. Then wang:tac13 produce an aggregated confidence by solving the following optimization problem:


where denotes the raw confidence score and denotes the aggregated confidence score for , is a non-negative weight assigned to each instance. Equation 1 ensures that the aggregated confidence score is close to the raw score as well as proportional to the agreement among systems on a value for a given key. Thus for a given key, if a system’s value is also produced by multiple other systems, it would have a higher score than if it were not produced by any other system. The authors use the inverse ranking of the average precision previously achieved by individual systems as the weights in the above equation. However since we use this approach for systems that we do not have historical data, we use uniform weights across all unsupervised systems for both the tasks.

CSSF systems TEDL systems
Figure 2: Precision-Recall curves for identifying the best voting performance on the two KBP tasks

Equation 1 is subject to certain constraints on the confidence values depending on the task. For the slot-filling task, the authors define two different constraints based on whether the slot type is single valued or list valued. For single-valued slot types, only one slot value can be correct and thus the constraint is based on the mutual exclusion property of the slot values:


This constraint allows only one of the slot values to have a substantially higher probability compared to rest. On the other hand, for list-valued slot types, the

in the above equation is replaced by the value where is the average number of correct slot fills for that slot type across all entities in the previous year and

is the total number of slot fills for that slot type across all entities. This approach to estimating the number of correct values can be thought of as

collective precision for the slot type achieved by the set of systems. For the newly introduced slot inverses in 2015, we use the same ratio as that of the corresponding original slot type. Thus the slot type per:parents (new slot type) would have the same ratio as that of per:children.

For the TEDL task, we use the KB ID as the key and thus use the entity type for defining the constraint on the values. For each of the entity types (PER, ORG and GPE) we replace the quantity on the right hand side in Equation 2 by the ratio of the average number of correct values for that entity type in 2014 to the total number of values for that entity type, across all entities. For the two new entity types instroduced in 2015 (FAC and LOC), we use the same ratio as that of GPE because of their semantic similarities.

The output from this approach for both tasks is a set of key-values with aggregated confidence scores across all unsupervised systems which go directly into the stacker as shown in Figure 1. Using the aggregation approach as opposed to directly using the raw confidence scores allows the classifier to meaningfully compare confidence scores across multiple systems although they are produced by very diverse systems.

Another unsupervised ensembling we experimented with in place of the constrained optimization approach is the Bipartite Graph based Consensus Maximization (BGCM) approach by gao:nips09. The authors introduce BGCM as a way of combining supervised and unsupervised models for a given task. So we use their approach for the KBP tasks and compare it to our stacking approach to combining supervised and unsupervised systems, as well as an alternative approach to ensembling the unsupervised systems before feeding their output to the stacker. The idea behind BGCM is to cast the ensembling task as an optimization problem on a bipartite graph, where the objective function favors the smoothness of the prediction over the graph, as well as penalizing deviations from the initial labeling provided by supervised models. The authors propose to consolidate a classification solution by maximizing the consensus among both supervised predictions and unsupervised constraints. They show that their algorithm outperforms the component models on ten out of eleven classification tasks across three different datasets.

4.3 Combining the Supervised and Unsupervised Approaches

Our new approach combines these supervised and unsupervised approaches using a stacked meta-classifier as the final arbiter for accepting a given key-value. Most KBP teams submit multiple variations of their system. Before running the supervised and unsupervised approaches discussed above, we first combine multiple runs of the same team into one. Of the CSSF systems from teams for which we have 2014 data for training and the systems from teams that do not have training data, we combine the runs of each team into one to ensure diversity of the final ensemble (since different runs from the same team tend to be minor variations). For the slot fills that were common between the runs of a given team, we compute an average confidence value, and then add any additional fills that are not common between runs. Thus, we obtained systems (one for each team) for which we have supervised data for training stacking. Similarly, we combine the TEDL systems from teams that have 2014 training data and systems from teams that did not have training data into one per team. Thus using the notation in Figure 1, for TEDL, and while for CSSF, and .

The output of the unsupervised method produces aggregated confidence scores calibrated across all of the component systems and goes directly into our final meta-classifier. We treat this combination as a single system which we call the unsupervised ensemble. In other words, in order to combine systems that have training data with those that do not, we add the unsupervised ensemble as an additional system to the stacker, thus giving us a total of , that is CSSF and TEDL systems. Once we have extracted the auxiliary features for each of the supervised systems and the unsupervised ensemble for both years, we train the stacker on 2014 systems, and test on the 2015 systems. The unsupervised ensemble for each year is composed of different systems, but hopefully the stacker learns to combine a generic unsupervised ensemble with the supervised systems that are shared across years. This allows the stacker to be the final arbitrator on the correctness of a key-value pair, combining new systems for which we have no historical data with additional systems for which training data is available. We employ a single classifier to train and test the meta-classifier using an L1-regularized SVM with a linear kernel [Fan et al.2008] (other classifiers gave similar results).

Methodology Precision Recall F1
Combined stacking and constrained optimization with auxiliary features 0.4679 0.4314 0.4489
Top ranked SFV system in 2015 [Rodriguez et al.2015] 0.5101 0.1812 0.4228
Combined stacking and constrained optimization without new features 0.4789 0.3588 0.4103
Stacking using BGCM instead of constrained optimization 0.5901 0.3021 0.3996
BGCM for combining supervised and unsupervised outputs 0.4902 0.3363 0.3989
Stacking approach described in [Viswanathan et al.2015] 0.5084 0.2855 0.3657
Top ranked CSSF system in 2015 [Angeli et al.2015] 0.3989 0.3058 0.3462
Oracle Voting baseline (3 or more systems must agree) 0.4384 0.2720 0.3357
Constrained optimization approach described in [Wang et al.2013] 0.1712 0.3998 0.2397
Table 1: Results on 2015 Cold Start Slot Filling (CSSF) task using the official NIST scorer

4.4 Auxiliary Features for Stacking

Along with the confidence scores, we also include auxiliary features which provide additional context for improving the meta-classifier [Viswanathan et al.2015]. For CSSF, the slot type (e.g. per:age) is used as an auxiliary feature, and in TEDL, we use the entity type. For slot-filling, features related to the provenance of the fill have also been used [Viswanathan et al.2015]. We include these for CSSF, along with two new features.

The two novel features measure similarity between specific documents. The 2015 CSSF task had a much smaller corpus of shorter documents compared to the previous year’s slot-filling corpus [Ellis et al.2015, Surdeanu and Ji2014]. Thus, the provenance feature of viswanathan:acl15 did not sufficiently capture the reliability of a slot fill based on where it was extracted. Slot filling queries were provided to participants in an XML format that included the query entity’s ID, name, entity type, the document where the entity appears, and beginning and end offsets in the document where that entity appears. This allowed the participants to disambiguate query entities that could potentially have the same name but refer to different entities. Below is a sample query from the 2015 task:

  <query id=”CSSF15_ENG_0006e06ebf”>

The <docid> tag refers to the document where the query entity appears, which we will call the query document.

Our first new feature involves measuring the similarity between this query document and the provenance document that is provided by a given system. We represent the query and provenance documents as standard TF-IDF weighted vectors and use cosine similarity to compare documents. Therefore every system that provides a slot fill, also provides the provenance for the fill and thus has a similarity score with the query document. If a system does not provide a particular slot fill then its document similarity score is simply zero. This feature is intended to measure the degree to which the system’s provenance document is referencing the correct query entity.

Our second new feature measures the document similarity between the provenance documents that different systems provide. Suppose for a given query and slot type, systems provide the same slot fill. For each of the systems, we measure the average document cosine similarity between the system’s provenance document and those of the other systems. The previous approach simply measured whether systems agreed on the exact provenance document. By softening this to take into account the similarity of provenance documents, we hope to more flexibly measure provenance agreement between systems.

For TEDL, we only use the entity type as an auxiliary feature and leave the development of more sophisticated features as future research.

4.5 Post-processing

Once we obtain the decisions on each of the key-value pairs from the stacker, we perform some final post-processing. For CSSF, this is straight forward. Each list-valued slot fill that is classified as correct is included in the final output. For single-valued slot fills, if they are multiple fills that were classified correctly for the same query and slot type, we include the fill with the highest meta-classifier confidence.

For TEDL, for each entity mention link that is classified as correct, if the link is a KB cluster ID then we include it in the final output, but if the link is a NIL cluster ID then we keep it aside until all mention links are processed. Thereafter, we resolve the NIL IDs across systems since NIL ID’s for each system are unique. We merge NIL clusters across systems into one if there is at least one common entity mention among them. Finally, we give a new NIL ID for these newly merged clusters.

Methodology Precision Recall F1
Combined stacking and constrained optimization 0.686 0.624 0.653
Stacking using BGCM instead of constrained optimization 0.803 0.525 0.635
BGCM for combining supervised and unsupervised outputs 0.810 0.517 0.631
Stacking approach described in [Viswanathan et al.2015] 0.814 0.508 0.625
Top ranked TEDL system in 2015 [Sil et al.2015] 0.693 0.547 0.611
Oracle Voting baseline (4 or more systems must agree) 0.514 0.601 0.554
Constrained optimization approach 0.445 0.176 0.252
Table 2: Results on 2015 Tri-lingual Entity Discovery and Linking (TEDL) task using the official NIST scorer and the CEAFm metric

5 Experimental Results

This section describes a comprehensive set of experiments evaluating ensembling for both KBP tasks using the algorithm described in the previous section, comparing our full system to various ablations and prior results. All results were obtained using the official NIST scorers for the tasks provided after the competition ended.333, We compare our results to several baselines. We apply the purely supervised approach of viswanathan:acl15 to systems that are common between 2014 and 2015, and also the constrained optimization approach of wang:tac13 on all the 2015 KBP systems. We also compare our combined stacking approach to Bipartite Graph based Consensus Maximization (BGCM) [Gao et al.2009] in two ways. First, we use BGCM in place of the constrained optimization approach to ensemble unsupervised systems while keeping the rest of our pipeline the same. Secondly, we also compare to combining both supervised and unsupervised systems using BGCM instead of stacking. We also include a voting baseline for ensembling the system outputs. For this approach, we vary the threshold on the number of systems that must agree to identify an “oracle” threshold that results in the highest F1 score for 2015 by plotting a Precision-Recall curve and finding the best F1 score for the voting baseline on both the KBP tasks. Figure 2 shows the plots for finding this “oracle” threshold for each of the KBP tasks. At each step we add one more to the number of systems that must agree on a key-value. We find that for CSSF, a threshold of or more systems and for TEDL a threshold of or more systems gives us the best resulting F1 for voting.

Tables 1 and 2 show the results for CSSF and TEDL respectively. Our full system, which combines supervised and unsupervised ensembling performed the best on both tasks. TAC-KBP also includes the Slot Filler Validation (SFV) task444 where the goal is to ensemble/filter outputs from multiple slot filling systems. The top ranked system in 2015 [Rodriguez et al.2015] does substantially better than many of the other ensembling approaches, but it does not do as well as our best performing system. The purely supervised approach of viswanathan:acl15 performs substantially worse, although still outperforming the top-ranked individual system in the 2015 competition. This approach only uses the common systems from 2014, thus ignoring approximately half of the systems. The approach of wang:tac13 performs very poorly by itself; but when combined with stacking gives a boost to recall and thus the overall . Note that all our combined methods have a substantially higher recall and thus highlighting the importance of the unsupervised ensemble. The oracle voting baseline also performs very poorly indicating that naive ensembling is not advantageous.

For TEDL, our combined approach gives the best overall performance, beating the top-ranked system for TEDL 2015. The TEDL evaluation provides three different approaches to measuring Precision, Recall and F1. First is entity discovery, second is entity linking and last is mention CEAF [Ji et al.2015]

. The mention CEAF metric finds the optimal alignment between system and gold standard clusters, and then evaluates precision and recall micro-averaged over mentions. We obtained similar results on all three evaluations and thus only include the mention CEAF score in this paper. The purely supervised stacking approach over shared systems does not do as well as any of our combined approaches even though it beats the best performing system (i.e. IBM) in the 2015 competition

[Sil et al.2015]. The relative ranking of the approaches is similar to those obtained on the CSSF task, thus proving that our approach is very general and provides improved performance on two quite different and challenging problems.

6 Related Work

Stacking has been previously applied to several problems in NLP such as collective document classification [Kou and Cohen2007]

, named entity recognition

[Florian2002], stacked dependency parsing [Martins et al.2008] and joint Chinese word segmentation and part-of-speech tagging [Sun2011]. Although viswanathan:acl15 applied stacking to KBP slot filling, we extend their approach with new auxiliary features and combine supervised and unsupervised systems for both CSSF and TEDL.

A fast and scalable collective entity linking method that relies on stacking was proposed by he:emnlp13. They stack a global predictor on top of a local predictor to collect coherence information from neighboring decisions. Biomedical entity extraction using a stacked ensemble of an SVM and CRF was shown to outperform individual components as well as voting baselines [Ekbal and Saha2013].

Stacking for information extraction has been demonstrated to outperform both majority voting and weighted voting methods [Sigletos et al.2005]. The FAUST system for biomolecular even extraction uses model combination strategies such as voting and stacking and was placed first in three of the four BioNLP tasks in 2011 [Riedel et al.2011]. Google’s Knowledge Vault system [Dong et al.2014] combines four diverse extraction methods by building a boosted decision stump classifier [Reyzin and Schapire2006]. For each proposed fact, the classifier considers both the confidence value of each extractor and the number of responsive documents found by the extractor.

7 Conclusion and Future Work

This paper has presented experimental results on two diverse KBP tasks, showing that a novel stacking-based approach to ensembling both supervised and unsupervised systems is very promising. The approach provides an overall F1 score of on 2015 KBP CSSF task and CEAFm F1 of on 2015 KBP TEDL, outperforming the top ranked systems from both 2015 competitions as well as several other baseline ensembling methods, thereby achieving a new state-of-the-art for both of these important, challenging tasks. We found that adding the unsupervised ensemble along with the shared systems increased the recall substantially, highlighting the importance of utilizing systems that do not have historical training data.

As discussed in Section 5, two new auxiliary stacking features for slot-filling based on provenance similarity with the query document and document similarity across systems improved CSSF performance substantially. In the future, we hope to develop similar auxiliary features for EDL. The input for TEDL includes a KB with several relational tuples for each entity. Similarity of this relational information to the context of a mention could be a useful auxiliary feature. Another feature for evaluating the reliability of agreement on a particular mention link could be a measure of how often the same systems agree on other linking decisions.

8 Acknowledgment

This research was supported in part by the DARPA DEFT program under AFRL grant FA8750-13-2-0026 and by MURI ARO grant W911NF-08-1-0242.


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