D ep N e CTI : Dependency-based Nested Compound Type Identification for S anskrit (2023)

Abstract: Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound’s components is crucial for deciphering its meaning. Earlier approaches in Sanskrit have focused on binary compounds and neglected the multi-component compound setting. This work introduces the novel task of nested compound type identification (NeCTI), which aims to identify nested spans of a multi-component compound and decode the implicit semantic relations between them. To the best of our knowledge, this is the first attempt in the field of lexical semantics to propose this task. We present 2 newly annotated datasets including an out-of-domain dataset for this task. We also benchmark these datasets by exploring the efficacy of the standard problem formulations such as nested named entity recognition, constituency parsing and seq2seq, etc. We present a novel framework named DepNeCTI: Dependency-based Nested Compound Type Identifier that surpasses the performance of the best baseline with an average absolute improvement of 13.1 points F1-score in terms of Labeled Span Score (LSS) and a 5-fold enhancement in inference efficiency. In line with the previous findings in the binary Sanskrit compound identification task, context provides benefits for the NeCTI task. The codebase and datasets are publicly available at: https://github.com/yaswanth-iitkgp/DepNeCTI

## introduction a compound is defined as a group of entities functioning as a single meaningful entity. the process of identifying the implied semantic relationship between the components of a compound in sanskrit is known as sanskrit compound type identification (sacti) @xcite or noun compound interpretation (nci) @xcite , * * denotes the first two authors contributed equally. 2020). within the literature, the nci problem has been approached in two ways, namely, classification @xcite @xcite and paraphrasing @xcite @xcite . in sanskrit literature, particularly in poetry, the use of multi-component compounds is ubiquitous @xcite . according to the digital corpus of sanskrit, more than 41% of compounds contain three or more components @xcite . however, earlier approaches focus solely on binary compounds and fail to address the complexities inherent in multi-component compounds adequately. thus, we propose a new task, the nested compound type identification (necti) task, which focuses on identifying nested spans within a multicomponent compound and interpreting their implicit semantic relationships. figure 1 illustrates an example of the necti task, highlighting nested spans and their associated semantic relations using distinct colors. the necti task presents multiple challenges: ## problem formulation the objective of the necti task is to detect nested spans within a multi-component compound and decipher the implicit semantic relations among them. our study focuses exclusively on this task and does not address the compound segmentation problem. it is assumed that the segmented components of the multi-component compound are already available. to obtain the segmentation of a compound, we rely on established resources such as the rule-based shallow parser @xcite or existing data-driven segmentation systems designed explicitly for sanskrit @xcite . complexity of necti task: as the number of components in a multi-component compound increases, the number of possible parses grows exponentially. our dataset encompasses multicomponent compounds ranging from 2 to 16 components. figure 3 visually depicts the exponential growth of possible parses with increasing component count. the parsing of a compound word with n + 1 components can be likened to the problem of fully parenthesizing n + 1 factors in all possible ways @xcite . thus, the total number of parse-options for a multi-component compound word with n + 1 components corresponds to the catalan number c n , where @xmath0. formally, in a given sentence @xmath1, x 2 , ..., x m } with m tokens, let x p (1 ≤ @xmath2. it is worth noting that x may contain multiple instances of multi-component compounds. a valid solution corresponds to a full paranthesization of these n components; let p n encompass all valid solutions for fully parenthesizing n factors, satisfying the cardinality |p n | = c n -1 , where c n -1 represents the catalan number. a valid solution for an n -component compound consists of n -1 nested spans. the necti system produces an output represented as a list of n -1 tuples for x p , given by y @xmath3, i t 1 , t 1 ], ..., [i h n -1 , i t n -1 , t n -1 ]}, such that y @xmath4. i h i and i t i denote the head and the tail indices, respectively, of the i th span, and t i corresponds to the label assigned to the respective span. how different is necti compared to the nested named entity recognition (nner) task? nner is a component of information extraction that aims to identify and classify nested named entities within unstructured text, considering their hierarchical structure. in contrast, the necti task focuses on identifying nested spans within a multicomponent compound and decoding their implicit semantic relations. these tasks have several key differences: (1) necti operates at the intra-word level, whereas nner operates at the inter-word level, considering entities across a phrase. (2) in necti, a multi-component compound can have multiple possible parses, requiring disambiguation through contextual cues and incorporating insights from pānin . īan grammar to address incompatibil-ities. conversely, we could not find discussions related to these aspects in nner literature. (3) necti benefits from prior knowledge of the compound's location and segmented components. in contrast, the nner task involves the additional challenge of identifying the location of entities within the text. consequently, leveraging existing nner frameworks for necti is not straightforward due to their inability to provide explicit support for providing the location of compounds. therefore, necti presents unique characteristics and challenges that differentiate it from the nner task, requiring specialized approaches tailored to its specific requirements. how different is necti compared to the multi-word expressions (mwe)? mwes encompass various categories such as idioms (e.g., kick the bucket), named entities (e.g., world health organization), and compounds (e.g., telephone box), etc. sanskrit compounds exhibit similarities to multi-word expressions, particularly multi-word compounds (nominal, noun, and verb), with respect to characteristics like collocation of components based on semantic relations between them and strict preference for ordering of the components. multi-word compounds in various languages involve adjacent lexemes juxtaposed with potential semantic relations. in contrast, sanskrit compounds are intuitively constructed based on the semantic compatibility of their components. additionally, in sanskrit, compounds always appear as a single word, requiring mandatory sandhi (euphonic transformations) between their components. conversely, certain categories of mwes (idioms, complex function words, verb-particles, and lightverbs) have relatively fixed structures, predominantly with components separated by spaces. furthermore, nesting within multi-word expressions is considered syntactic overlaps, while the nested structures of sanskrit compounds result from successive combinations of components based on semantic relations, thereby clearly distinguishing sanskrit compounds from mwes. summarily, the semantic relations among compound components resemble dependency relations, which can be represented as directed labels within the dependency parse structure. this approach succinctly represents the semantic relations without introducing intermediary nodes. lastly, it enables the simultaneous identification of the structure or constituency span alongside the identification of compound types. we encourage readers to refer to appendix § a for a more detailed illustration. ## depnecti: the proposed framework figure 4 illustrates the proposed framework with an example "sumitrā-ānanda-vardhanah . laks . man . ah . rāmam anujagāma" (translation: "laks . man . a, the one who enhanced the delight of sumitrā, followed rāma") where 'sumitrā-ānanda-vardhanah . ' is a multi-component compound word. we assume prior knowledge of compound segmentation and treat the individual components of multicomponent compounds as separate words. we propose two variants of depnecti depending on the choice of encoder: depnecti-lstm and depnecti-xlmr. in depnecti-lstm, to differentiate between compound (highlighted with color) and non-compound (highlighted with color) tokens, we employ span encoding. the span encoding uses two randomly initialized vectors (compound or non-compound) to inform the model whether the corresponding instance is a compound or non-compound word. on the other hand, depnecti-xlmr is informed about compound's location in the input string using bracketing (for example, <sumitrā-ānanda-vardhanah . >) and it lacks span encoding component. in order to convert the compound-level dependency parsing task into standard dependency parsing, we introduce two modifications. first, we introduce an additional token called "global" ( color) which serves as the global head for all words in the sentence. second, we establish connections between the compound head and non-compound words to the global token using the "compound root" and "global relation" relations, respectively. formally, in a given sentence @xmath5, x 2 , ..., x m } with m tokens, let x p (1 ≤ @xmath6. notably, x may contain multiple occurrences of multi-component compounds. the n -component compound (x p ) is further split into its components (x @xmath7, x 2 p , ..., x n p }). next, we pass the overall sequence (@xmath8, x 2 , ..., x 1 p , x 2 p , ..., x n p ..., x m }) to the encoder to obtain hidden representations. the lstm encoder concatenates a token's word, character and span embedding to obtain its representation and the xlmr encoder uses word-pieces. finally, a bi-affine @xcite dependency module is applied on top of it. ## analysis here, we examine the proposed system, focusing on a comprehensive analysis and its applicability. for this purpose, we utilize the nectis coarse dataset under the w/ context configuration. we report lss in terms of macro-average f1-scores. our systems, depnecti-lstm and depnecti-xlmr are leveraging a simple architecture and utilizing dependency parsing as an appropriate problem formulation, exhibits a 5-fold/3-fold improvement over the most efficient baseline, botcp. 7 related works lexical semantics is a dedicated field focused on word meaning. various tasks such as word-sense disambiguation @xcite @xcite , relationship extraction @xcite @xcite , and semantic role labeling @xcite @xcite play essential roles in determining word meaning. moreover, when dealing with complex word structures such as compounds, named entities, and multi-word expressions, relying solely on basic word senses and relationships is inadequate. while efforts have been made in noun compound identification @xcite @xcite @xcite @xcite , multi-word expression (mwe) @xcite @xcite and named entity recognition @xcite @xcite , the nested compounds remains unexplored. sanskrit compound type identification has attracted significant attention over the past decade. decoding the meaning of a sanskrit compound requires determining its constituents @xcite @xcite , understanding how these constituents are grouped @xcite , identifying the semantic relationship between them @xcite , and ultimately generating a paraphrase of the compound @xcite . previous studies proposed rule-based approaches @xcite , a datadriven approach @xcite and a hybrid approach @xcite for sacti. @xcite proposed a context-sensitive architecture for binary compounds. earlier works in sanskrit solely focused on binary compounds, neglecting the identification of multi-component compound types; however, our proposed framework fills this research gap. ## conclusion in this work, we focused on multi-component compounding in sanskrit, which helps to decode the implicit structure of a compound to decipher its meaning. while previous approaches have primarily focused on binary compounds, we introduced a novel task called nested compound type identification (necti). this task aims to identify nested spans within multi-component compounds and decode the implicit semantic relations between them, filling a gap in the field of lexical semantics. to facilitate research in this area, we created 2 newly annotated datasets, designed explicitly for the necti task. these datasets were utilized to benchmark various problem formulations. our novel framework depnecti outperformed the best baseline system by achieving a stupendous absolute gain of 13.1 points f1-score in terms of lss. similar to the previous findings on binary sanskrit compound identification, we discovered that our proposed system exhibits substantial performance superiority over the best baseline in low-resourced scenarios.