DOCSLIB.ORG

Mathlingbudapest: Concept Networks for Semantic Similarity

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mathlingbudapest: Concept Networks for Semantic Similarity MathLingBudapest: Concept Networks for Semantic Similarity Gabor´ Recski Judit Acs´ Research Institute for Linguistics HAS Research Institute for Linguistics and Hungarian Academy of Sciences Dept. of Automation and Applied Informatics Benczur´ u. 33 Budapest U of Technology and Economics 1068 Budapest, Hungary Magyar tudosok´ krt. 2 (Bldg. Q.) [email protected] 1117 Budapest, Hungary [email protected] Abstract on Semeval Task 2a (Semantic Textual Similarity for English). All components of our system are We present our approach to measuring seman- available for download under an MIT license from tic similarity of sentence pairs used in Se- GitHub12. Section 2 describes the system architec- meval 2015 tasks 1 and 2. We adopt the ture and points out the main differences between our sentence alignment framework of (Han et al., system and that in (Han et al., 2013), section 3 out- 2013) and experiment with several measures of word similarity. We hybridize the common lines our word similarity metric derived from the vector-based models with definition graphs 4lang concept lexicon. We present the evaluation from the 4lang concept dictionary and de- of our systems on both tasks in section 4, and section vise a measure of graph similarity that yields 5 provides a brief conclusion. good results on training data. We did not ad- dress the specific challenges posed by Twitter 2 Architecture data, and this is reflected in placing 11th from 30 in Task 1, but our systems perform fairly Our framework for determining the semantic simi- well on the generic datasets of Task 2, with the larity of two sentences is based on the system pre- hybrid approach placing 11th among 78 runs. sented in (Han et al., 2013). Their architecture, Align and Penalize, involves computing an align- 1 Introduction ment score between two sentences based on some measure of word similarity. We’ve chosen to reim- This paper describes the systems participating in plement this system so that we can experiment with Semeval-2015 Task 1 (Xu et al., 2015) and Task 2 various notions of word similarity, including the (Agirre et al., 2015). To compute the semantic sim- one based on 4lang and presented in section 3. ilarity of two sentences we use the architecture pre- Although we reimplemented virtually all rules and sented in (Han et al., 2013) to find, for each word, its components described by (Han et al., 2013) for counterpart in the other sentence that is semantically experimentation, we shall only describe those that most similar to it. We implemented several meth- ended up in at least one of the five configurations ods for measuring word similarity, among them (i) submitted to Semeval. a word embedding created by the method presented The core idea behind the Align and Penalize archi- in (Mikolov et al., 2013) and (ii) a metric based on tecture is, given two senteces S1 and S2 and some networks of concepts derived from the 4lang con- measure of word similarity, to align each word of cept lexicon (Kornai and Makrai, 2013; Kornai et one sentence with some word of the other sentence al., 2015) and definitions from the Longman Dic- so that the similarity of word pairs is maximized. tionary of Contemporary English (Bullon, 2003). A hybrid system exploiting both of these metrics yields 1http://github.com/juditacs/semeval the best results and placed 11th among 73 systems 2http://github.com/kornai/pymachine 138 Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 138–142, Denver, Colorado, June 4-5, 2015. c 2015 Association for Computational Linguistics The mapping need not be one-to-one and is calcu- 3 Similarity from Concept Networks lated independently for words of S1 (aligning them This section will present the word similarity mea- with words from S2) and words of S2 (aligning them sure based on principles of lexical semantics pre- with words from S1). The score of an alignment is sented in (Kornai, 2010). The 4lang concept dic- the sum of the similarities of each word pair in the tionary (Kornai and Makrai, 2013) contains 3500 alignment, normalized by sentence length. The fi- definitions created manually. Because the Longman nal score assigned to a pair of sentences is the aver- Defining Vocabulary (LDV) (Boguraev and Briscoe, age of the alignment scores for each sentence. For 1989) is a subset of 4lang, we could automat- out-of-vocabulary (OOV) words, i.e. those that are ically extend this manually created seed to every not covered by the component used for measuring headword of the Longman Dictionary of Contem- word similarity, we use the Dice-similarity over the porary English (LDOCE) by processing their defi- sets of character 4-grams in each word. Addition- nitions with the Stanford Dependency Parser (Klein ally, we use simple rules to detect acronyms and and Manning, 2003), and mapping dependency re- compounds: if a word of one sentence that is a se- lations to sets of edges in the 4lang-style concept quence of 2-5 characters (e.g. ABC) has a matching graph. Details of the mapping will be described else- sequence of words in the other sentence (e.g. Ameri- where (Recski, 2015). can Broadcasting Company), all words of the phrase Since these definitions are essentially graphs of are aligned with this word and recieve an alignment concepts, we have experimented with similarity score of 1. If a sentence contains a sequence of two functions over pairs of such graphs that capture se- words (e.g. long term or can not) that appear in the mantic similarity of the concepts defined by each of other sentence without a space and with or without them. There are two fundamentally different config- a hyphen (e.g. long-term or cannot), these are also urations present in 4lang graphs: aligned with a score of 1. The score returned by the word similarity component can be boosted based on 1. two nodes may be connected via a 0-edge, WordNet (Miller, 1995), e.g. if one is a hypernym which is a generalization over unary predi- of the other, if one appears frequently in glosses of 0 0 cation (dog bark), attribution (dog the other, or if they are derivationally related. For −→ −→ faithful), and hypernymy, or the IS A re- the exact cases covered and a description of how 0 lation (dog mammal). the boost is calculated, the reader is referred to (Han −→ et al., 2013). In our submissions we only used this 2. two nodes can be connected, via a 1-edge and a boost on word similarity scores obtained from word 2-edge respectively, to a third one representing embeddings. a binary relation. Binaries include all transitive 1 2 verbs (e.g. cat CATCH branch). and The similarity score may be reduced by a vari- ←− −→ 1 a handful of binary primitives (e.g. tree ety of penalties, which we only enabled for Task 2 ←− HAS branch). 1 runs – they haven’t brought any improvement on −→ Task 2 datasets in any of our early experiments. Of We start by the intuition that similar con- the penalties described in (Han et al., 2013) we only cepts will overlap in the elementary configura- used the one which decreases alignment scores if tions they take part in: they might share a 0- the word similarity score for some word pair is very 0 0 neighbor, e.g. train vehicle car, small (< 0.05). We also introduced two new types −→ ←− or they might be on the same path of 1- and of penalties based on our observations of error types 1 2 in Twitter data: if one sentence starts with a question 2-edges, e.g. park IN town and 1 2 ←− −→ word and the other one does not or if one sentence street IN town. ←− −→ contains a past-tense verb and the other does not, We’ll define the predicates of a node as we reduce the overall score by 1/(L(S1) + L(S2)), the set of such configurations it takes part where L(S1) and L(S2) are the numbers of words in in. For example, based on the definition each sentence. graph in Figure 1, the predicates of the concept 139 Figure 1: 4lang definition of bird. bird are vertebrate; (HAS, feather); { (HAS, wing); (MAKE, egg) . } Figure 2: Definition of casualty built from LDOCE. Our initial version of graph similarity is the Jac- card similarity of the sets of predicates of each con- cept, i.e. P (w ) P (w ) S(w , w ) = | 1 ∩ 2 | 1 2 P (w ) P (w ) | 1 ∪ 2 | For all words that are not among the 3500 de- fined in 4lang we obtain definition graphs by au- tomated parsing of Longman definitions and the ap- plication of a simple mapping from dependency re- lations to graph edges (Recski, 2015). By far the largest source of noise in these graphs is that cur- rently there is no postprocessor component that rec- ognizes common structures of dictionary definitions like appositive relative clauses. For example the word casualty is defined by LDOCE as someone Figure 3: Expected definition of casualty. who is hurt or killed in an accident or war and we currently build the graph in Figure 2 instead of that in Figure 3. To mitigate the effects of these anomal- ities, we updated our definition of predicates: we let them be “inherited” via paths of 0-edges encoding the IS A-relationship. We’ve also experimented with similarity mea- sures that take into account the sets of all nodes ac- cessible from each concept in their respective def- inition graphs. This proved useful in establishing that two concepts which would otherwise be treated as entirely dissimilar are in fact somewhat related.
Load more

Recommended publications
  • Combining Semantic and Lexical Measures to Evaluate Medical Terms Similarity?
    Combining semantic and lexical measures to evaluate medical terms similarity? Silvio Domingos Cardoso1;2, Marcos Da Silveira1, Ying-Chi Lin3, Victor Christen3, Erhard Rahm3, Chantal Reynaud-Dela^ıtre2, and C´edricPruski1 1 LIST, Luxembourg Institute of Science and Technology, Luxembourg fsilvio.cardoso,marcos.dasilveira,[email protected] 2 LRI, University of Paris-Sud XI, France [email protected] 3 Department of Computer Science, Universit¨atLeipzig, Germany flin,christen,[email protected] Abstract. The use of similarity measures in various domains is corner- stone for different tasks ranging from ontology alignment to information retrieval. To this end, existing metrics can be classified into several cate- gories among which lexical and semantic families of similarity measures predominate but have rarely been combined to complete the aforemen- tioned tasks. In this paper, we propose an original approach combining lexical and ontology-based semantic similarity measures to improve the evaluation of terms relatedness. We validate our approach through a set of experiments based on a corpus of reference constructed by domain experts of the medical field and further evaluate the impact of ontology evolution on the used semantic similarity measures. Keywords: Similarity measures · Ontology evolution · Semantic Web · Medical terminologies 1 Introduction Measuring the similarity between terms is at the heart of many research inves- tigations. In ontology matching, similarity measures are used to evaluate the relatedness between concepts from different ontologies [9]. The outcomes are the mappings between the ontologies, increasing the coverage of domain knowledge and optimize semantic interoperability between information systems. In informa- tion retrieval, similarity measures are used to evaluate the relatedness between units of language (e.g., words, sentences, documents) to optimize search [39].
    [Show full text]
  • Evaluating Lexical Similarity to Build Sentiment Similarity
    Evaluating Lexical Similarity to Build Sentiment Similarity Grégoire Jadi∗, Vincent Claveauy, Béatrice Daille∗, Laura Monceaux-Cachard∗ ∗ LINA - Univ. Nantes {gregoire.jadi beatrice.daille laura.monceaux}@univ-nantes.fr y IRISA–CNRS, France [email protected] Abstract In this article, we propose to evaluate the lexical similarity information provided by word representations against several opinion resources using traditional Information Retrieval tools. Word representation have been used to build and to extend opinion resources such as lexicon, and ontology and their performance have been evaluated on sentiment analysis tasks. We question this method by measuring the correlation between the sentiment proximity provided by opinion resources and the semantic similarity provided by word representations using different correlation coefficients. We also compare the neighbors found in word representations and list of similar opinion words. Our results show that the proximity of words in state-of-the-art word representations is not very effective to build sentiment similarity. Keywords: opinion lexicon evaluation, sentiment analysis, spectral representation, word embedding 1. Introduction tend or build sentiment lexicons. For example, (Toh and The goal of opinion mining or sentiment analysis is to iden- Wang, 2014) uses the syntactic categories from WordNet tify and classify texts according to the opinion, sentiment or as features for a CRF to extract aspects and WordNet re- emotion that they convey. It requires a good knowledge of lations such as antonymy and synonymy to extend an ex- the sentiment properties of each word. This properties can isting opinion lexicons. Similarly, (Agarwal et al., 2011) be either discovered automatically by machine learning al- look for synonyms in WordNet to find the polarity of words gorithms, or embedded into language resources.
    [Show full text]
  • Using Prior Knowledge to Guide BERT's Attention in Semantic
    Using Prior Knowledge to Guide BERT’s Attention in Semantic Textual Matching Tasks Tingyu Xia Yue Wang School of Artificial Intelligence, Jilin University School of Information and Library Science Key Laboratory of Symbolic Computation and Knowledge University of North Carolina at Chapel Hill Engineering, Jilin University [email protected] [email protected] Yuan Tian∗ Yi Chang∗ School of Artificial Intelligence, Jilin University School of Artificial Intelligence, Jilin University Key Laboratory of Symbolic Computation and Knowledge International Center of Future Science, Jilin University Engineering, Jilin University Key Laboratory of Symbolic Computation and Knowledge [email protected] Engineering, Jilin University [email protected] ABSTRACT 1 INTRODUCTION We study the problem of incorporating prior knowledge into a deep Measuring semantic similarity between two pieces of text is a fun- Transformer-based model, i.e., Bidirectional Encoder Representa- damental and important task in natural language understanding. tions from Transformers (BERT), to enhance its performance on Early works on this task often leverage knowledge resources such semantic textual matching tasks. By probing and analyzing what as WordNet [28] and UMLS [2] as these resources contain well- BERT has already known when solving this task, we obtain better defined types and relations between words and concepts. Recent understanding of what task-specific knowledge BERT needs the works have shown that deep learning models are more effective most and where it is most needed. The analysis further motivates on this task by learning linguistic knowledge from large-scale text us to take a different approach than most existing works. Instead of data and representing text (words and sentences) as continuous using prior knowledge to create a new training task for fine-tuning trainable embedding vectors [10, 17].
    [Show full text]
  • Latent Semantic Analysis for Text-Based Research
    Behavior Research Methods, Instruments, & Computers 1996, 28 (2), 197-202 ANALYSIS OF SEMANTIC AND CLINICAL DATA Chaired by Matthew S. McGlone, Lafayette College Latent semantic analysis for text-based research PETER W. FOLTZ New Mexico State University, Las Cruces, New Mexico Latent semantic analysis (LSA) is a statistical model of word usage that permits comparisons of se­ mantic similarity between pieces of textual information. This papersummarizes three experiments that illustrate how LSA may be used in text-based research. Two experiments describe methods for ana­ lyzinga subject's essay for determining from what text a subject learned the information and for grad­ ing the quality of information cited in the essay. The third experiment describes using LSAto measure the coherence and comprehensibility of texts. One of the primary goals in text-comprehension re­ A theoretical approach to studying text comprehen­ search is to understand what factors influence a reader's sion has been to develop cognitive models ofthe reader's ability to extract and retain information from textual ma­ representation ofthe text (e.g., Kintsch, 1988; van Dijk terial. The typical approach in text-comprehension re­ & Kintsch, 1983). In such a model, semantic information search is to have subjects read textual material and then from both the text and the reader's summary are repre­ have them produce some form of summary, such as an­ sented as sets ofsemantic components called propositions. swering questions or writing an essay. This summary per­ Typically, each clause in a text is represented by a single mits the experimenter to determine what information the proposition.
    [Show full text]
  • Ying Liu, Xi Yang
    Liu, Y. & Yang, X. (2018). A similar legal case retrieval Liu & Yang system by multiple speech question and answer. In Proceedings of The 18th International Conference on Electronic Business (pp. 72-81). ICEB, Guilin, China, December 2-6. A Similar Legal Case Retrieval System by Multiple Speech Question and Answer (Full paper) Ying Liu, School of Computer Science and Information Engineering, Guangxi Normal University, China, [email protected] Xi Yang*, School of Computer Science and Information Engineering, Guangxi Normal University, China, [email protected] ABSTRACT In real life, on the one hand people often lack legal knowledge and legal awareness; on the other hand lawyers are busy, time- consuming, and expensive. As a result, a series of legal consultation problems cannot be properly handled. Although some legal robots can answer some problems about basic legal knowledge that are matched by keywords, they cannot do similar case retrieval and sentencing prediction according to the criminal facts described in natural language. To overcome the difficulty, we propose a similar case retrieval system based on natural language understanding. The system uses online speech synthesis of IFLYTEK and speech reading and writing technology, integrates natural language semantic processing technology and multiple rounds of question-and-answer dialogue mechanism to realise the legal knowledge question and answer with the memory-based context processing ability, and finally retrieves a case that is most similar to the criminal facts that the user consulted. After trial use, the system has a good level of human-computer interaction and high accuracy of information retrieval, which can largely satisfy people's consulting needs for legal issues.
    [Show full text]
  • Untangling Semantic Similarity: Modeling Lexical Processing Experiments with Distributional Semantic Models
    Untangling Semantic Similarity: Modeling Lexical Processing Experiments with Distributional Semantic Models. Farhan Samir Barend Beekhuizen Suzanne Stevenson Department of Computer Science Department of Language Studies Department of Computer Science University of Toronto University of Toronto, Mississauga University of Toronto ([email protected]) ([email protected]) ([email protected]) Abstract DSMs thought to instantiate one but not the other kind of sim- Distributional semantic models (DSMs) are substantially var- ilarity have been found to explain different priming effects on ied in the types of semantic similarity that they output. Despite an aggregate level (as opposed to an item level). this high variance, the different types of similarity are often The underlying conception of semantic versus associative conflated as a monolithic concept in models of behavioural data. We apply the insight that word2vec’s representations similarity, however, has been disputed (Ettinger & Linzen, can be used for capturing both paradigmatic similarity (sub- 2016; McRae et al., 2012), as has one of the main ways in stitutability) and syntagmatic similarity (co-occurrence) to two which it is operationalized (Gunther¨ et al., 2016). In this pa- sets of experimental findings (semantic priming and the effect of semantic neighbourhood density) that have previously been per, we instead follow another distinction, based on distri- modeled with monolithic conceptions of DSM-based seman- butional properties (e.g. Schutze¨ & Pedersen, 1993), namely, tic similarity. Using paradigmatic and syntagmatic similarity that between syntagmatically related words (words that oc- based on word2vec, we show that for some tasks and types of items the two types of similarity play complementary ex- cur in each other’s near proximity, such as drink–coffee), planatory roles, whereas for others, only syntagmatic similar- and paradigmatically related words (words that can be sub- ity seems to matter.
    [Show full text]
  • Measuring Semantic Similarity of Words Using Concept Networks
    Measuring semantic similarity of words using concept networks Gabor´ Recski Eszter Iklodi´ Research Institute for Linguistics Dept of Automation and Applied Informatics Hungarian Academy of Sciences Budapest U of Technology and Economics H-1068 Budapest, Benczur´ u. 33 H-1117 Budapest, Magyar tudosok´ krt. 2 [email protected] [email protected] Katalin Pajkossy Andras´ Kornai Department of Algebra Institute for Computer Science Budapest U of Technology and Economics Hungarian Academy of Sciences H-1111 Budapest, Egry J. u. 1 H-1111 Budapest, Kende u. 13-17 [email protected] [email protected] Abstract using word embeddings and WordNet. In Sec- tion 3 we briefly introduce the 4lang resources We present a state-of-the-art algorithm and the formalism it uses for encoding the mean- for measuring the semantic similarity of ing of words as directed graphs of concepts, then word pairs using novel combinations of document our efforts to develop novel 4lang- word embeddings, WordNet, and the con- based similarity features. Besides improving the cept dictionary 4lang. We evaluate our performance of existing systems for measuring system on the SimLex-999 benchmark word similarity, the goal of the present project is to data. Our top score of 0.76 is higher than examine the potential of 4lang representations in any published system that we are aware of, representing non-trivial lexical relationships that well beyond the average inter-annotator are beyond the scope of word embeddings and agreement of 0.67, and close to the 0.78 standard linguistic ontologies. average correlation between a human rater Section 4 presents our results and pro- and the average of all other ratings, sug- vides rough error analysis.
    [Show full text]
  • Semantic Similarity Between Sentences
    International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 01 | Jan -2017 www.irjet.net p-ISSN: 2395-0072 SEMANTIC SIMILARITY BETWEEN SENTENCES PANTULKAR SRAVANTHI1, DR. B. SRINIVASU2 1M.tech Scholar Dept. of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Telangana- Hyderabad, India 2Associate Professor - Dept. of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Telangana- Hyderabad, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The task of measuring sentence similarity is [1].Sentence similarity is one of the core elements of Natural defined as determining how similar the meanings of two Language Processing (NLP) tasks such as Recognizing sentences are. Computing sentence similarity is not a trivial Textual Entailment (RTE)[2] and Paraphrase Recognition[3]. task, due to the variability of natural language - expressions. Given two sentences, the task of measuring sentence Measuring semantic similarity of sentences is closely related to similarity is defined as determining how similar the meaning semantic similarity between words. It makes a relationship of two sentences is. The higher the score, the more similar between a word and the sentence through their meanings. The the meaning of the two sentences. WordNet and similarity intention is to enhance the concepts of semantics over the measures play an important role in sentence level similarity syntactic measures that are able to categorize the pair of than document level[4]. sentences effectively. Semantic similarity plays a vital role in Natural language processing, Informational Retrieval, Text 1.1 Problem Description Mining, Q & A systems, text-related research and application area.
    [Show full text]
  • Semantic Sentence Similarity: Size Does Not Always Matter
    INTERSPEECH 2021 30 August – 3 September, 2021, Brno, Czechia Semantic sentence similarity: size does not always matter Danny Merkx, Stefan L. Frank, Mirjam Ernestus Centre for Language Studies, Radboud University, Nijmegen, The Netherlands [email protected], [email protected], [email protected] Abstract liest VGS models, used a database of around 8,000 utterances This study addresses the question whether visually [15]. Harwath and colleagues introduced the first ‘modern’ neu- grounded speech recognition (VGS) models learn to capture ral network based approach which was trained on the Flickr8k sentence semantics without access to any prior linguistic knowl- Audio Caption corpus, a corpus with 40,000 utterances [16]. edge. We produce synthetic and natural spoken versions of a This corpus was quickly followed up by Places Audio Captions well known semantic textual similarity database and show that (400,000 utterances) [17] and, most recently, by SpokenCOCO our VGS model produces embeddings that correlate well with (600,000 utterances) [14]. human semantic similarity judgements. Our results show that However, previous work on visual grounding using writ- a model trained on a small image-caption database outperforms ten captions has shown that larger databases do not always re- two models trained on much larger databases, indicating that sult in better models. In [18], we compared models trained on database size is not all that matters. We also investigate the im- the written captions of Flickr8k [19] and MSCOCO [20]. We portance of having multiple captions per image and find that showed that, although the much larger MSCOCO (600k sen- this is indeed helpful even if the total number of images is tences) achieved better performance on the training task, the lower, suggesting that paraphrasing is a valuable learning sig- model trained on the smaller Flickr database performed better nal.
    [Show full text]
  • Word Embeddings for Semantic Similarity: Comparing LDA with Word2vec
    Word embeddings for semantic similarity: comparing LDA with Word2vec Luandrie Potgieter1 and Alta de Waal12 1 Department of Statistics, University of Pretoria 2 Center for Artificial Intelligence Research (CAIR) 1 Introduction Distributional semantics is a subfield in Natural Language Processing (NLP) that studies methods to derive meaning, and semantic representations for text. These representations can be thought of as statistical distributions of words assuming that it characterises semantic behaviour. These statistical distributions are often referred to as embeddings, or lower dimensional representations of the text. The two main categories of embeddings are count-based and prediction-based methods. Count-based methods most often result in global word embeddings as it utilizes the frequency of words in documents. Prediction-based embeddings on the other hand are often referred to as local embeddings as it takes into account a window of words adjacent to the word of interest. Once a corpus is represented by its semantic distribution (which is in a vector space), all kinds of similarity measures can be applied. This again leads to other NLP applications such as collaborative filtering, aspect-based sentiment analysis, intent classification for chatbots and machine translation. In this research, we investigate the appropriateness of Latent Dirichlet Allo- cation (LDA) [1] and word2vec [4] embeddings as semantic representations of a corpus. Once the semantic representation of a corpus is obtained, the distances between the documents and query documents are obtained by means of dis- tance measures such as cosine, Euclidean or Jensen-Shannon. We expect short distances between documents with similar semantic representations and longer distances between documents with different semantic representations.
    [Show full text]
  • Event-Based Knowledge Reconciliation Using Frame Embeddings And
    Knowledge-Based Systems 135 (2017) 192–203 Contents lists available at ScienceDirect Knowle dge-Base d Systems journal homepage: www.elsevier.com/locate/knosys Event-base d knowle dge reconciliation using frame emb e ddings and frame similarity ∗ Mehwish Alam a, Diego Reforgiato Recupero b,d, , Misael Mongiovi c, Aldo Gangemi a,d, Petar Ristoski e a Université Paris 13, 99 avenue JB Clément, Villetaneuse, Paris 93430, France b Università degli Studi di Cagliari, Department of Mathematics and Computer Science, Via Ospedale 72, Cagliari 09124, Italy c CNR, ISTC, Catania, Italy d CNR, ISTC, Via S. Martino della Battaglia 44, Rome, Italy e University of Mannheim, Mannheim, Germany a r t i c l e i n f o a b s t r a c t Article history: This paper proposes an evolution over MERGILO, a tool for reconciling knowledge graphs extracted from Received 6 April 2017 text, using graph alignment and word similarity. The reconciled knowledge graphs are typically used Revised 9 August 2017 for multi-document summarization, or to detect knowledge evolution across document series. The main Accepted 14 August 2017 point of improvement focuses on event reconciliation i.e., reconciling knowledge graphs generated by text Available online 16 August 2017 about two similar events described differently. In order to gather a complete semantic representation of Keywords: events, we use FRED semantic web machine reader, jointly with Framester, a linguistic linked data hub Knowledge reconciliation represented using a novel formal semantics for frames. Framester is used to enhance the extracted event Frame semantics knowledge with semantic frames.
    [Show full text]
  • Assessing BERT As a Distributional Semantics Model
    Proceedings of the Society for Computation in Linguistics Volume 3 Article 34 2020 What do you mean, BERT? Assessing BERT as a Distributional Semantics Model Timothee Mickus Université de Lorraine, CNRS, ATILF, [email protected] Denis Paperno Utrecht University, [email protected] Mathieu Constant Université de Lorraine, CNRS, ATILF, [email protected] Kees van Deemter Utrecht University, [email protected] Follow this and additional works at: https://scholarworks.umass.edu/scil Part of the Computational Linguistics Commons Recommended Citation Mickus, Timothee; Paperno, Denis; Constant, Mathieu; and van Deemter, Kees (2020) "What do you mean, BERT? Assessing BERT as a Distributional Semantics Model," Proceedings of the Society for Computation in Linguistics: Vol. 3 , Article 34. DOI: https://doi.org/10.7275/t778-ja71 Available at: https://scholarworks.umass.edu/scil/vol3/iss1/34 This Paper is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Proceedings of the Society for Computation in Linguistics by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. What do you mean, BERT? Assessing BERT as a Distributional Semantics Model Timothee Mickus Denis Paperno Mathieu Constant Kees van Deemter Universite´ de Lorraine Utrecht University Universite´ de Lorraine Utrecht University CNRS, ATILF [email protected] CNRS, ATILF [email protected] [email protected] [email protected] Abstract based on BERT. Furthermore, BERT serves both as a strong baseline and as a basis for a fine- Contextualized word embeddings, i.e. vector tuned state-of-the-art word sense disambiguation representations for words in context, are nat- urally seen as an extension of previous non- pipeline (Wang et al., 2019a).
    [Show full text]